



'''simple docstring'''




import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    FocalNetBackbone,
						    FocalNetForImageClassification,
						    FocalNetForMaskedImageModeling,
						    FocalNetModel,
						)
						from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Dict=1_3							,     lowerCAmelCase__     : Optional[int]=3_2							,     lowerCAmelCase__     : List[Any]=2							,     lowerCAmelCase__     : List[str]=3							,     lowerCAmelCase__     : Union[str, Any]=1_6							,     lowerCAmelCase__     : Any=[3_2, 6_4, 1_2_8]							,     lowerCAmelCase__     : int=[1, 2, 1]							,     lowerCAmelCase__     : Dict=[2, 2, 4]							,     lowerCAmelCase__     : int=2							,     lowerCAmelCase__     : int=2.0							,     lowerCAmelCase__     : Any=True							,     lowerCAmelCase__     : str=0.0							,     lowerCAmelCase__     : Any=0.0							,     lowerCAmelCase__     : int=0.1							,     lowerCAmelCase__     : Dict="gelu"							,     lowerCAmelCase__     : Union[str, Any]=False							,     lowerCAmelCase__     : str=True							,     lowerCAmelCase__     : Optional[Any]=0.02							,     lowerCAmelCase__     : Tuple=1E-5							,     lowerCAmelCase__     : Tuple=True							,     lowerCAmelCase__     : List[Any]=None							,     lowerCAmelCase__     : List[str]=True							,     lowerCAmelCase__     : Optional[Any]=1_0							,     lowerCAmelCase__     : str=8							,     lowerCAmelCase__     : str=["stage1", "stage2"]							,     lowerCAmelCase__     : List[Any]=[1, 2]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		parent
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		batch_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		image_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		patch_size
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		num_channels
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		embed_dim
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		hidden_sizes
					__SCREAMING_SNAKE_CASE       :		str           =		depths
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		num_heads
					__SCREAMING_SNAKE_CASE       :		str           =		window_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		mlp_ratio
					__SCREAMING_SNAKE_CASE       :		int           =		qkv_bias
					__SCREAMING_SNAKE_CASE       :		int           =		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Tuple           =		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		drop_path_rate
					__SCREAMING_SNAKE_CASE       :		Dict           =		hidden_act
					__SCREAMING_SNAKE_CASE       :		Dict           =		use_absolute_embeddings
					__SCREAMING_SNAKE_CASE       :		int           =		patch_norm
					__SCREAMING_SNAKE_CASE       :		Any           =		layer_norm_eps
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		Tuple           =		is_training
					__SCREAMING_SNAKE_CASE       :		Tuple           =		scope
					__SCREAMING_SNAKE_CASE       :		Tuple           =		use_labels
					__SCREAMING_SNAKE_CASE       :		str           =		type_sequence_label_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		encoder_stride
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		out_features
					__SCREAMING_SNAKE_CASE       :		int           =		out_indices

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE       :		int           =		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE       :		Dict           =		ids_tensor([self.batch_size]							,     self.type_sequence_label_size						)

					__SCREAMING_SNAKE_CASE       :		str           =		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					return FocalNetConfig(
					    image_size=self.image_size							,     patch_size=self.patch_size							,     num_channels=self.num_channels							,     embed_dim=self.embed_dim							,     hidden_sizes=self.hidden_sizes							,     depths=self.depths							,     num_heads=self.num_heads							,     window_size=self.window_size							,     mlp_ratio=self.mlp_ratio							,     qkv_bias=self.qkv_bias							,     hidden_dropout_prob=self.hidden_dropout_prob							,     attention_probs_dropout_prob=self.attention_probs_dropout_prob							,     drop_path_rate=self.drop_path_rate							,     hidden_act=self.hidden_act							,     use_absolute_embeddings=self.use_absolute_embeddings							,     path_norm=self.patch_norm							,     layer_norm_eps=self.layer_norm_eps							,     initializer_range=self.initializer_range							,     encoder_stride=self.encoder_stride							,     out_features=self.out_features							,     out_indices=self.out_indices							,     )

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		FocalNetModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Any           =		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		int           =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
					__SCREAMING_SNAKE_CASE       :		int           =		int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

					self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, expected_seq_len, expected_dim)						)

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		FocalNetBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model(lowerCAmelCase__						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)							,     len(config.out_features						)						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,     [self.batch_size, self.image_size, 8, 8]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,     len(config.out_features						)						)
					self.parent.assertListEqual(model.channels							,     config.hidden_sizes[:-1]						)

					# verify backbone works with out_features=None
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		None
					__SCREAMING_SNAKE_CASE       :		Dict           =		FocalNetBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(lowerCAmelCase__						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)							,     1						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,     [self.batch_size, self.image_size * 2, 4, 4]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,     1						)
					self.parent.assertListEqual(model.channels							,     [config.hidden_sizes[-1]]						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		FocalNetForMaskedImageModeling(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model(lowerCAmelCase__						)
					self.parent.assertEqual(
					    result.reconstruction.shape							,     (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		1
					__SCREAMING_SNAKE_CASE       :		List[str]           =		FocalNetForMaskedImageModeling(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE       :		Dict           =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE       :		str           =		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.reconstruction.shape							,     (self.batch_size, 1, self.image_size, self.image_size)						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.type_sequence_label_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		FocalNetForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		str           =		model(lowerCAmelCase__							,     labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		1
					__SCREAMING_SNAKE_CASE       :		str           =		FocalNetForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE       :		Any           =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)						)



			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		self.prepare_config_and_inputs()

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		config_and_inputs
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__    ,					lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		Dict			    =		(
			    (
			        FocalNetModel,
			        FocalNetForImageClassification,
			        FocalNetForMaskedImageModeling,
			        FocalNetBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_A      :		List[Any]			    =		(
			    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_A      :		Tuple			    =		False

			_A      :		Optional[Any]			    =		False
			_A      :		Optional[Any]			    =		False
			_A      :		Any			    =		False
			_A      :		Dict			    =		False

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		FocalNetModelTester(self						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		ConfigTester(self							,     config_class=lowerCAmelCase__							,     embed_dim=3_7							,     has_text_modality=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					return

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)

			@unittest.skip(reason="""FocalNet does not use inputs_embeds"""						)
			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""FocalNet does not use feedforward chunking"""						)
			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					pass

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model_class(lowerCAmelCase__						)
							self.assertIsInstance(model.get_input_embeddings()							,     (nn.Module)						)
							__SCREAMING_SNAKE_CASE       :		str           =		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCAmelCase__							,     nn.Linear						)						)

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:-1]:
							__SCREAMING_SNAKE_CASE       :		int           =		model_class(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE       :		str           =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model_class(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		str           =		model(**self._prepare_for_class(lowerCAmelCase__							,     lowerCAmelCase__						)						)

					__SCREAMING_SNAKE_CASE       :		Any           =		outputs.hidden_states

					__SCREAMING_SNAKE_CASE       :		Dict           =		getattr(
					    self.model_tester							,     """expected_num_hidden_layers"""							,     len(self.model_tester.depths						) + 1						)
					self.assertEqual(len(lowerCAmelCase__						)							,     lowerCAmelCase__						)

					# FocalNet has a different seq_length
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		(
					    config.patch_size
					    if isinstance(config.patch_size							,     collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)							,     [num_patches, self.model_tester.embed_dim]							,     )

					__SCREAMING_SNAKE_CASE       :		Tuple           =		outputs.reshaped_hidden_states
					self.assertEqual(len(lowerCAmelCase__						)							,     lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		reshaped_hidden_states[0].shape
					__SCREAMING_SNAKE_CASE       :		Any           =		(
					    reshaped_hidden_states[0].view(lowerCAmelCase__							,     lowerCAmelCase__							,     height * width						).permute(0							,     2							,     1						)
					)
					self.assertListEqual(
					    list(reshaped_hidden_states.shape[-2:]						)							,     [num_patches, self.model_tester.embed_dim]							,     )

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE       :		str           =		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,     collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)

					for model_class in self.all_model_classes[:-1]:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		True
							self.check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE       :		str           =		True

							self.check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE       :		List[str]           =		3

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,     collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		(
					    config.patch_size
					    if isinstance(config.patch_size							,     collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					__SCREAMING_SNAKE_CASE       :		int           =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes[:-1]:
							__SCREAMING_SNAKE_CASE       :		str           =		True
							self.check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     (padded_height, padded_width)						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		True
							self.check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     (padded_height, padded_width)						)

			@slow
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		FocalNetModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE       :		Tuple           =		_config_zero_init(lowerCAmelCase__						)
					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       :		Dict           =		model_class(config=lowerCAmelCase__						)
							for name, param in model.named_parameters():
									if "embeddings" not in name and param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()							,     [0.0, 1.0]							,     msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,     )






@require_vision
@require_torch
class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""						) if is_vision_available() else None



			@slow
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.default_image_processor

					__SCREAMING_SNAKE_CASE       :		Dict           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		image_processor(images=lowerCAmelCase__							,     return_tensors="""pt"""						).to(lowerCAmelCase__						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		int           =		model(**lowerCAmelCase__						)

					# verify the logits
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.Size((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		torch.tensor([0.21_66, -0.43_68, 0.21_91]						).to(lowerCAmelCase__						)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,     lowerCAmelCase__							,     atol=1E-4						)						)
					self.assertTrue(outputs.logits.argmax(dim=-1						).item()							,     2_8_1						)






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		(FocalNetBackbone,) if is_torch_available() else ()
			_A      :		Tuple			    =		FocalNetConfig

			_A      :		Union[str, Any]			    =		False

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		FocalNetModelTester(self						)




'''simple docstring'''




import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


UpperCamelCase__       :	str    = logging.getLogger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		'''summarization'''
			_A      :		Optional[Any]			    =		['''loss''']
			_A      :		Tuple			    =		ROUGE_KEYS
			_A      :		int			    =		'''rouge2'''

			def __init__(							self     : int							,     lowerCAmelCase__     : Union[str, Any]							,     **lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					if hparams.sortish_sampler and hparams.gpus > 1:
							__SCREAMING_SNAKE_CASE       :		Any           =		False
					elif hparams.max_tokens_per_batch is not None:
							if hparams.gpus > 1:
									raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""						)
							if hparams.sortish_sampler:
									raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""						)

					super().__init__(lowerCAmelCase__							,     num_labels=lowerCAmelCase__							,     mode=self.mode							,     **lowerCAmelCase__						)
					use_task_specific_params(self.model							,     """summarization"""						)
					save_git_info(self.hparams.output_dir						)
					__SCREAMING_SNAKE_CASE       :		int           =		Path(self.output_dir						) / """metrics.json"""
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		Path(self.output_dir						) / """hparams.pkl"""
					pickle_save(self.hparams							,     self.hparams_save_path						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		defaultdict(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.config.model_type
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

					__SCREAMING_SNAKE_CASE       :		dict           =		{
					    "data_dir": self.hparams.data_dir,
					    "max_source_length": self.hparams.max_source_length,
					    "prefix": self.model.config.prefix or "",
					}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		{
					    """train""": self.hparams.n_train,
					    """val""": self.hparams.n_val,
					    """test""": self.hparams.n_test,
					}
					__SCREAMING_SNAKE_CASE       :		Any           =		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

					__SCREAMING_SNAKE_CASE       :		Any           =		{
					    """train""": self.hparams.max_target_length,
					    """val""": self.hparams.val_max_target_length,
					    """test""": self.hparams.test_max_target_length,
					}
					assert self.target_lens["train"] <= self.target_lens["val"], F"target_lens: {self.target_lens}"
					assert self.target_lens["train"] <= self.target_lens["test"], F"target_lens: {self.target_lens}"
					if self.hparams.freeze_embeds:
							freeze_embeds(self.model						)
					if self.hparams.freeze_encoder:
							freeze_params(self.model.get_encoder()						)
							assert_all_frozen(self.model.get_encoder()						)

					__SCREAMING_SNAKE_CASE       :		Any           =		get_git_info()["""repo_sha"""]
					__SCREAMING_SNAKE_CASE       :		Any           =		hparams.num_workers
					__SCREAMING_SNAKE_CASE       :		Tuple           =		None  # default to config
					if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
							__SCREAMING_SNAKE_CASE       :		Any           =		self.decoder_start_token_id
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(
					    SeqaSeqDataset if hasattr(self.tokenizer							,     """prepare_seq2seq_batch"""						) else LegacySeqaSeqDataset
					)
					__SCREAMING_SNAKE_CASE       :		Dict           =		False
					__SCREAMING_SNAKE_CASE       :		Any           =		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
					if self.hparams.eval_max_gen_length is not None:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.hparams.eval_max_gen_length
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model.config.max_length
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Dict[str, torch.Tensor]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{
					    k: self.tokenizer.batch_decode(v.tolist()						) if """mask""" not in k else v.shape for k, v in batch.items()
					}
					save_json(lowerCAmelCase__							,     Path(self.output_dir						) / """text_batch.json"""						)
					save_json({k: v.tolist() for k, v in batch.items()}							,     Path(self.output_dir						) / """tok_batch.json"""						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		True
					return readable_batch

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Any							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					return self.model(lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.batch_decode(
					    lowerCAmelCase__							,     skip_special_tokens=lowerCAmelCase__							,     clean_up_tokenization_spaces=lowerCAmelCase__						)
					return lmap(str.strip							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.pad_token_id
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""], batch["""attention_mask"""]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		batch["""labels"""]
					if isinstance(self.model							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		str           =		self.model._shift_right(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		shift_tokens_right(lowerCAmelCase__							,     lowerCAmelCase__						)
					if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
							__SCREAMING_SNAKE_CASE       :		Tuple           =		decoder_input_ids
							self.save_readable_batch(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		int           =		self(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     decoder_input_ids=lowerCAmelCase__							,     use_cache=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		outputs["""logits"""]
					if self.hparams.label_smoothing == 0:
							# Same behavior as modeling_bart.py, besides ignoring pad_token_id
							__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.CrossEntropyLoss(ignore_index=lowerCAmelCase__						)

							assert lm_logits.shape[-1] == self.vocab_size
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		ce_loss_fct(lm_logits.view(-1							,     lm_logits.shape[-1]						)							,     tgt_ids.view(-1						)						)
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.functional.log_softmax(lowerCAmelCase__							,     dim=-1						)
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		label_smoothed_nll_loss(
							    lowerCAmelCase__							,     lowerCAmelCase__							,     self.hparams.label_smoothing							,     ignore_index=lowerCAmelCase__						)
					return (loss,)

			@property
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return self.tokenizer.pad_token_id

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._step(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		dict(zip(self.loss_names							,     lowerCAmelCase__						)						)
					# tokens per batch
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		batch["""input_ids"""].ne(self.pad						).sum() + batch["""labels"""].ne(self.pad						).sum()
					__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""].shape[0]
					__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""].eq(self.pad						).sum()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		batch["""input_ids"""].eq(self.pad						).float().mean()
					# TODO(SS): make a wandb summary metric for this
					return {"loss": loss_tensors[0], "log": logs}

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Union[str, Any]="val"						):





					"""simple docstring"""






					self.step_count += 1
					__SCREAMING_SNAKE_CASE       :		int           =		{k: torch.stack([x[k] for x in outputs]						).mean() for k in self.loss_names}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		losses["""loss"""]
					__SCREAMING_SNAKE_CASE       :		int           =		{
					    k: np.array([x[k] for x in outputs]						).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
					}
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		(
					    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
					)
					__SCREAMING_SNAKE_CASE       :		torch.FloatTensor           =		torch.tensor(lowerCAmelCase__						).type_as(lowerCAmelCase__						)
					generative_metrics.update({k: v.item() for k, v in losses.items()}						)
					losses.update(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		{F"{prefix}_avg_{k}": x for k, x in losses.items()}
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.step_count
					self.metrics[prefix].append(lowerCAmelCase__						)  # callback writes this to self.metrics_save_path
					__SCREAMING_SNAKE_CASE       :		int           =		flatten_list([x["""preds"""] for x in outputs]						)
					return {
					    "log": all_metrics,
					    "preds": preds,
					    F"{prefix}_loss": loss,
					    F"{prefix}_{self.val_metric}": metric_tensor,
					}

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					return calculate_rouge(lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		time.time()

					# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.model.generate(
					    batch["""input_ids"""]							,     attention_mask=batch["""attention_mask"""]							,     use_cache=lowerCAmelCase__							,     decoder_start_token_id=self.decoder_start_token_id							,     num_beams=self.eval_beams							,     max_length=self.eval_max_length							,     )
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(time.time() - ta) / batch["""input_ids"""].shape[0]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.ids_to_clean_text(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.ids_to_clean_text(batch["""labels"""]						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self._step(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		dict(zip(self.loss_names							,     lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.calc_generative_metrics(lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		np.mean(lmap(lowerCAmelCase__							,     lowerCAmelCase__						)						)
					base_metrics.update(gen_time=lowerCAmelCase__							,     gen_len=lowerCAmelCase__							,     preds=lowerCAmelCase__							,     target=lowerCAmelCase__							,     **lowerCAmelCase__						)
					return base_metrics

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					return self.validation_epoch_end(lowerCAmelCase__							,     prefix="""test"""						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.n_obs[type_path]
					__SCREAMING_SNAKE_CASE       :		str           =		self.target_lens[type_path]
					__SCREAMING_SNAKE_CASE       :		str           =		self.dataset_class(
					    self.tokenizer							,     type_path=lowerCAmelCase__							,     n_obs=lowerCAmelCase__							,     max_target_length=lowerCAmelCase__							,     **self.dataset_kwargs							,     )
					return dataset

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : bool = False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.get_dataset(lowerCAmelCase__						)

					if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		dataset.make_sortish_sampler(lowerCAmelCase__							,     distributed=self.hparams.gpus > 1						)
							return DataLoader(
							    lowerCAmelCase__							,     batch_size=lowerCAmelCase__							,     collate_fn=dataset.collate_fn							,     shuffle=lowerCAmelCase__							,     num_workers=self.num_workers							,     sampler=lowerCAmelCase__							,     )

					elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
							__SCREAMING_SNAKE_CASE       :		Any           =		dataset.make_dynamic_sampler(
							    self.hparams.max_tokens_per_batch							,     distributed=self.hparams.gpus > 1						)
							return DataLoader(
							    lowerCAmelCase__							,     batch_sampler=lowerCAmelCase__							,     collate_fn=dataset.collate_fn							,     num_workers=self.num_workers							,     )
					else:
							return DataLoader(
							    lowerCAmelCase__							,     batch_size=lowerCAmelCase__							,     collate_fn=dataset.collate_fn							,     shuffle=lowerCAmelCase__							,     num_workers=self.num_workers							,     sampler=lowerCAmelCase__							,     )

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.get_dataloader("""train"""							,     batch_size=self.hparams.train_batch_size							,     shuffle=lowerCAmelCase__						)
					return dataloader

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					return self.get_dataloader("""val"""							,     batch_size=self.hparams.eval_batch_size						)

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					return self.get_dataloader("""test"""							,     batch_size=self.hparams.eval_batch_size						)



			@staticmethod
			def UpperCamelCase__					(							lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					BaseTransformer.add_model_specific_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					add_generic_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					parser.add_argument(
					    """--max_source_length"""							,     default=1_0_2_4							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--max_target_length"""							,     default=5_6							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--val_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--test_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument("""--freeze_encoder"""							,     action="""store_true"""						)
					parser.add_argument("""--freeze_embeds"""							,     action="""store_true"""						)
					parser.add_argument("""--sortish_sampler"""							,     action="""store_true"""							,     default=lowerCAmelCase__						)
					parser.add_argument("""--overwrite_output_dir"""							,     action="""store_true"""							,     default=lowerCAmelCase__						)
					parser.add_argument("""--max_tokens_per_batch"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__						)
					parser.add_argument("""--logger_name"""							,     type=lowerCAmelCase__							,     choices=["""default""", """wandb""", """wandb_shared"""]							,     default="""default"""						)
					parser.add_argument("""--n_train"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_val"""							,     type=lowerCAmelCase__							,     default=5_0_0							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_test"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument(
					    """--task"""							,     type=lowerCAmelCase__							,     default="""summarization"""							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--label_smoothing"""							,     type=lowerCAmelCase__							,     default=0.0							,     required=lowerCAmelCase__						)
					parser.add_argument("""--src_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--tgt_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--eval_beams"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__						)
					parser.add_argument(
					    """--val_metric"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__							,     choices=["""bleu""", """rouge2""", """loss""", None]						)
					parser.add_argument("""--eval_max_gen_length"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     help="""never generate more than n tokens"""						)
					parser.add_argument("""--save_top_k"""							,     type=lowerCAmelCase__							,     default=1							,     required=lowerCAmelCase__							,     help="""How many checkpoints to save"""						)
					parser.add_argument(
					    """--early_stopping_patience"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help=(
					        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
					        """ val_check_interval will effect it."""
					    )							,     )
					return parser






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		'''translation'''
			_A      :		int			    =		['''loss''']
			_A      :		Union[str, Any]			    =		['''bleu''']
			_A      :		Dict			    =		'''bleu'''

			def __init__(							self     : Any							,     lowerCAmelCase__     : int							,     **lowerCAmelCase__     : Any						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__							,     **lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		hparams.src_lang
					__SCREAMING_SNAKE_CASE       :		Dict           =		hparams.tgt_lang



			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return calculate_bleu(lowerCAmelCase__							,     lowerCAmelCase__						)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   str=None					):
		Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
		check_output_dir(_lowerCamelCase       ,      expected_items=3					)

		if model is None:
				if "summarization" in args.task:
						__SCREAMING_SNAKE_CASE       :		SummarizationModule           =		SummarizationModule(_lowerCamelCase					)
				else:
						__SCREAMING_SNAKE_CASE       :		SummarizationModule           =		TranslationModule(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		Path(args.data_dir					).name
		if (
		    args.logger_name == "default"
		    or args.fast_dev_run
		    or str(args.output_dir					).startswith("""/tmp"""					)
		    or str(args.output_dir					).startswith("""/var"""					)
		):
				__SCREAMING_SNAKE_CASE       :		str           =		True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				__SCREAMING_SNAKE_CASE       :		Any           =		os.environ.get("""WANDB_PROJECT"""       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		str           =		WandbLogger(name=model.output_dir.name       ,      project=_lowerCamelCase					)

		elif args.logger_name == "wandb_shared":
				from pytorch_lightning.loggers import WandbLogger

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		WandbLogger(name=model.output_dir.name       ,      project=F"hf_{dataset}"					)

		if args.early_stopping_patience >= 0:
				__SCREAMING_SNAKE_CASE       :		str           =		get_early_stopping_callback(model.val_metric       ,      args.early_stopping_patience					)
		else:
				__SCREAMING_SNAKE_CASE       :		Tuple           =		False

		__SCREAMING_SNAKE_CASE       :		Dict           =		args.val_metric == """loss"""
		__SCREAMING_SNAKE_CASE       :		pl.Trainer           =		generic_train(
		    _lowerCamelCase       ,      _lowerCamelCase       ,      logging_callback=SeqaSeqLoggingCallback()       ,      checkpoint_callback=get_checkpoint_callback(
		        args.output_dir       ,      model.val_metric       ,      args.save_top_k       ,      _lowerCamelCase					)       ,      early_stopping_callback=_lowerCamelCase       ,      logger=_lowerCamelCase       ,      )
		pickle_save(model.hparams       ,      model.output_dir / """hparams.pkl"""					)
		if not args.do_predict:
				return model

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		""""""
		__SCREAMING_SNAKE_CASE       :		Any           =		sorted(glob.glob(os.path.join(args.output_dir       ,      """*.ckpt"""					)       ,      recursive=_lowerCamelCase					)					)
		if checkpoints:
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		checkpoints[-1]
				__SCREAMING_SNAKE_CASE       :		str           =		checkpoints[-1]
		trainer.logger.log_hyperparams(model.hparams					)

		# test() without a model tests using the best checkpoint automatically
		trainer.test()
		return model


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[int]    = argparse.ArgumentParser()
						UpperCamelCase__       :	Dict    = pl.Trainer.add_argparse_args(parser)
						UpperCamelCase__       :	List[Any]    = SummarizationModule.add_model_specific_args(parser, os.getcwd())

						UpperCamelCase__       :	List[str]    = parser.parse_args()

						main(args)