
from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


__a  			=					logging.get_logger(__name__)


# General docstring
__a  			=					'MobileNetV1Config'

# Base docstring
__a  			=					'google/mobilenet_v1_1.0_224'
__a  			=					[1, 1_0_2_4, 7, 7]

# Image classification docstring
__a  			=					'google/mobilenet_v1_1.0_224'
__a  			=					'tabby, tabby cat'


__a  			=					[
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]=None					):


				'''simple docstring'''
				lowercase_       =					{}

				if isinstance(snake_case__						,   snake_case__					):
								lowercase_       =					model.mobilenet_va
				else:
								lowercase_       =					model

				lowercase_       =					'''MobilenetV1/Conv2d_0/'''
				lowercase_       =					backbone.conv_stem.convolution.weight
				lowercase_       =					backbone.conv_stem.normalization.bias
				lowercase_       =					backbone.conv_stem.normalization.weight
				lowercase_       =					backbone.conv_stem.normalization.running_mean
				lowercase_       =					backbone.conv_stem.normalization.running_var

				for i in range(13					):
								lowercase_       =					i + 1
								lowercase_       =					i * 2

								lowercase_       =					backbone.layer[pt_index]
								lowercase_       =					F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
								lowercase_       =					pointer.convolution.weight
								lowercase_       =					pointer.normalization.bias
								lowercase_       =					pointer.normalization.weight
								lowercase_       =					pointer.normalization.running_mean
								lowercase_       =					pointer.normalization.running_var

								lowercase_       =					backbone.layer[pt_index + 1]
								lowercase_       =					F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
								lowercase_       =					pointer.convolution.weight
								lowercase_       =					pointer.normalization.bias
								lowercase_       =					pointer.normalization.weight
								lowercase_       =					pointer.normalization.running_mean
								lowercase_       =					pointer.normalization.running_var

				if isinstance(snake_case__						,   snake_case__					):
								lowercase_       =					'''MobilenetV1/Logits/Conv2d_1c_1x1/'''
								lowercase_       =					model.classifier.weight
								lowercase_       =					model.classifier.bias

				return tf_to_pt_map




def 				a						(							snake_case__:					Dict						,   snake_case__:					List[str]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				try:
								import numpy as np
								import tensorflow as tf
				except ImportError:
								logger.error(
								    '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
								    '''https://www.tensorflow.org/install/ for installation instructions.'''					)
								raise

				# Load weights from TF model
				lowercase_       =					tf.train.list_variables(snake_case__					)
				lowercase_       =					{}
				for name, shape in init_vars:
								logger.info(F'''Loading TF weight {name} with shape {shape}'''					)
								lowercase_       =					tf.train.load_variable(snake_case__						,   snake_case__					)
								lowercase_       =					array

				# Build TF to PyTorch weights loading map
				lowercase_       =					_build_tf_to_pytorch_map(snake_case__						,   snake_case__						,   snake_case__					)

				for name, pointer in tf_to_pt_map.items():
								logger.info(F'''Importing {name}'''					)
								if name not in tf_weights:
												logger.info(F'''{name} not in tf pre-trained weights, skipping'''					)
												continue

								lowercase_       =					tf_weights[name]

								if "depthwise_weights" in name:
												logger.info('''Transposing depthwise'''					)
												lowercase_       =					np.transpose(snake_case__						,   (2, 3, 0, 1)					)
								elif "weights" in name:
												logger.info('''Transposing'''					)
												if len(pointer.shape					) == 2:  # copying into linear layer
																lowercase_       =					array.squeeze().transpose()
												else:
																lowercase_       =					np.transpose(snake_case__						,   (3, 2, 0, 1)					)

								if pointer.shape != array.shape:
												raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched'''					)

								logger.info(F'''Initialize PyTorch weight {name} {array.shape}'''					)
								lowercase_       =					torch.from_numpy(snake_case__					)

								tf_weights.pop(snake_case__						,   snake_case__					)
								tf_weights.pop(name + '''/RMSProp'''						,   snake_case__					)
								tf_weights.pop(name + '''/RMSProp_1'''						,   snake_case__					)
								tf_weights.pop(name + '''/ExponentialMovingAverage'''						,   snake_case__					)

				logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys()					)}'''					)
				return model




def 				a						(							snake_case__:					torch.Tensor						,   snake_case__:					nn.Convad					):


				'''simple docstring'''
				lowercase_  ,	lowercase_       =					features.shape[-2:]
				lowercase_  ,	lowercase_       =					conv_layer.stride
				lowercase_  ,	lowercase_       =					conv_layer.kernel_size

				if in_height % stride_height == 0:
								lowercase_       =					max(kernel_height - stride_height						,   0					)
				else:
								lowercase_       =					max(kernel_height - (in_height % stride_height)						,   0					)

				if in_width % stride_width == 0:
								lowercase_       =					max(kernel_width - stride_width						,   0					)
				else:
								lowercase_       =					max(kernel_width - (in_width % stride_width)						,   0					)

				lowercase_       =					pad_along_width // 2
				lowercase_       =					pad_along_width - pad_left
				lowercase_       =					pad_along_height // 2
				lowercase_       =					pad_along_height - pad_top

				lowercase_       =					(pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(snake_case__						,   snake_case__						,   '''constant'''						,   0.0					)
class  lowercase__(				nn.Module							):



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			MobileNetVaConfig       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = 1       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = 1       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = True       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool or str] = True       ,							)       ->     None:
					super().__init__()
					lowercase_       =					config

					if in_channels % groups != 0:
									raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.'''       )
					if out_channels % groups != 0:
									raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.'''       )

					lowercase_       =					0 if config.tf_padding else int((kernel_size - 1) / 2       )

					lowercase_       =					nn.Convad(
					    in_channels=SCREAMING_SNAKE_CASE_       ,							out_channels=SCREAMING_SNAKE_CASE_       ,							kernel_size=SCREAMING_SNAKE_CASE_       ,							stride=SCREAMING_SNAKE_CASE_       ,							padding=SCREAMING_SNAKE_CASE_       ,							groups=SCREAMING_SNAKE_CASE_       ,							bias=SCREAMING_SNAKE_CASE_       ,							padding_mode='''zeros'''       ,							)

					if use_normalization:
									lowercase_       =					nn.BatchNormad(
									    num_features=SCREAMING_SNAKE_CASE_       ,							eps=config.layer_norm_eps       ,							momentum=0.99_97       ,							affine=SCREAMING_SNAKE_CASE_       ,							track_running_stats=SCREAMING_SNAKE_CASE_       ,							)
					else:
									lowercase_       =					None

					if use_activation:
									if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					ACTaFN[use_activation]
									elif isinstance(config.hidden_act       ,							SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					ACTaFN[config.hidden_act]
									else:
													lowercase_       =					config.hidden_act
					else:
									lowercase_       =					None




	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			torch.Tensor       )       ->     torch.Tensor:
					if self.config.tf_padding:
									lowercase_       =					apply_tf_padding(SCREAMING_SNAKE_CASE_       ,							self.convolution       )
					lowercase_       =					self.convolution(SCREAMING_SNAKE_CASE_       )
					if self.normalization is not None:
									lowercase_       =					self.normalization(SCREAMING_SNAKE_CASE_       )
					if self.activation is not None:
									lowercase_       =					self.activation(SCREAMING_SNAKE_CASE_       )
					return features

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Any         =	MobileNetVaConfig
	a			:Tuple         =	load_tf_weights_in_mobilenet_va
	a			:Optional[Any]         =	'mobilenet_v1'
	a			:Any         =	'pixel_values'
	a			:Optional[Any]         =	False
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[nn.Linear, nn.Convad]       )       ->     None:
					if isinstance(SCREAMING_SNAKE_CASE_       ,							(nn.Linear, nn.Convad)       ):
									module.weight.data.normal_(mean=0.0       ,							std=self.config.initializer_range       )
									if module.bias is not None:
													module.bias.data.zero_()
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							nn.BatchNormad       ):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0       )

__a  			=					r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

__a  			=					r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.' ,						UpperCAmelCase ,						)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			MobileNetVaConfig       ,							SCREAMING_SNAKE_CASE_  :			bool = True       )       ->     Optional[int]:
					super().__init__(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					config

					lowercase_       =					3_2
					lowercase_       =					max(int(depth * config.depth_multiplier       )       ,							config.min_depth       )

					lowercase_       =					MobileNetVaConvLayer(
					    SCREAMING_SNAKE_CASE_       ,							in_channels=config.num_channels       ,							out_channels=SCREAMING_SNAKE_CASE_       ,							kernel_size=3       ,							stride=2       ,							)

					lowercase_       =					[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

					lowercase_       =					nn.ModuleList()
					for i in range(1_3       ):
									lowercase_       =					out_channels

									if strides[i] == 2 or i == 0:
													depth *= 2
													lowercase_       =					max(int(depth * config.depth_multiplier       )       ,							config.min_depth       )

									self.layer.append(
									    MobileNetVaConvLayer(
									        SCREAMING_SNAKE_CASE_       ,							in_channels=SCREAMING_SNAKE_CASE_       ,							out_channels=SCREAMING_SNAKE_CASE_       ,							kernel_size=3       ,							stride=strides[i]       ,							groups=SCREAMING_SNAKE_CASE_       ,							)       )

									self.layer.append(
									    MobileNetVaConvLayer(
									        SCREAMING_SNAKE_CASE_       ,							in_channels=SCREAMING_SNAKE_CASE_       ,							out_channels=SCREAMING_SNAKE_CASE_       ,							kernel_size=1       ,							)       )

					lowercase_       =					nn.AdaptiveAvgPoolad((1, 1)       ) if add_pooling_layer else None

					# Initialize weights and apply final processing
					self.post_init()
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Dict:
					raise NotImplementedError




	@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_       )
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC       ,							output_type=SCREAMING_SNAKE_CASE_       ,							config_class=_CONFIG_FOR_DOC       ,							modality='''vision'''       ,							expected_output=_EXPECTED_OUTPUT_SHAPE       ,							)
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.Tensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							)       ->     Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
					lowercase_       =					(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					lowercase_       =					return_dict if return_dict is not None else self.config.use_return_dict

					if pixel_values is None:
									raise ValueError('''You have to specify pixel_values'''       )

					lowercase_       =					self.conv_stem(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					() if output_hidden_states else None

					for i, layer_module in enumerate(self.layer       ):
									lowercase_       =					layer_module(SCREAMING_SNAKE_CASE_       )

									if output_hidden_states:
													lowercase_       =					all_hidden_states + (hidden_states,)

					lowercase_       =					hidden_states

					if self.pooler is not None:
									lowercase_       =					torch.flatten(self.pooler(SCREAMING_SNAKE_CASE_       )       ,							start_dim=1       )
					else:
									lowercase_       =					None

					if not return_dict:
									return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None       )

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=SCREAMING_SNAKE_CASE_       ,							pooler_output=SCREAMING_SNAKE_CASE_       ,							hidden_states=SCREAMING_SNAKE_CASE_       ,							)

@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ' ,						UpperCAmelCase ,						)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			MobileNetVaConfig       )       ->     None:
					super().__init__(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					config.num_labels
					lowercase_       =					MobileNetVaModel(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.mobilenet_va.layer[-1].convolution.out_channels

					# Classifier head
					lowercase_       =					nn.Dropout(config.classifier_dropout_prob       ,							inplace=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							config.num_labels       ) if config.num_labels > 0 else nn.Identity()

					# Initialize weights and apply final processing
					self.post_init()




	@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_       )
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,							output_type=SCREAMING_SNAKE_CASE_       ,							config_class=_CONFIG_FOR_DOC       ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,							)
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.Tensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.Tensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							)       ->     Union[tuple, ImageClassifierOutputWithNoAttention]:
					lowercase_       =					return_dict if return_dict is not None else self.config.use_return_dict

					lowercase_       =					self.mobilenet_va(SCREAMING_SNAKE_CASE_       ,							output_hidden_states=SCREAMING_SNAKE_CASE_       ,							return_dict=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					outputs.pooler_output if return_dict else outputs[1]

					lowercase_       =					self.classifier(self.dropout(SCREAMING_SNAKE_CASE_       )       )

					lowercase_       =					None
					if labels is not None:
									if self.config.problem_type is None:
													if self.num_labels == 1:
																	lowercase_       =					'''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	lowercase_       =					'''single_label_classification'''
													else:
																	lowercase_       =					'''multi_label_classification'''

									if self.config.problem_type == "regression":
													lowercase_       =					MSELoss()
													if self.num_labels == 1:
																	lowercase_       =					loss_fct(logits.squeeze()       ,							labels.squeeze()       )
													else:
																	lowercase_       =					loss_fct(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
									elif self.config.problem_type == "single_label_classification":
													lowercase_       =					CrossEntropyLoss()
													lowercase_       =					loss_fct(logits.view(-1       ,							self.num_labels       )       ,							labels.view(-1       )       )
									elif self.config.problem_type == "multi_label_classification":
													lowercase_       =					BCEWithLogitsLoss()
													lowercase_       =					loss_fct(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					if not return_dict:
									lowercase_       =					(logits,) + outputs[2:]
									return ((loss,) + output) if loss is not None else output

					return ImageClassifierOutputWithNoAttention(
					    loss=SCREAMING_SNAKE_CASE_       ,							logits=SCREAMING_SNAKE_CASE_       ,							hidden_states=outputs.hidden_states       ,							)









'''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)