
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
				import torch

if is_tf_available():
				import tensorflow as tf


class      _lowerCAmelCase       (       __lowercase ):







							'''simple docstring'''

							lowerCAmelCase_   		=		["image_processor"]
							lowerCAmelCase_   		=		"SamImageProcessor"

							def __init__(self    ,    UpperCAmelCase    )	->  Tuple:
														super().__init__(UpperCAmelCase    )
														_snake_case  =    self.image_processor
														_snake_case  =    -10
														_snake_case  =    self.image_processor.size["""longest_edge"""]

							def __call__(self    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase = None    ,    **UpperCAmelCase    ,    )	->  BatchEncoding:
														_snake_case  =    self.image_processor(
														    UpperCAmelCase    ,    return_tensors=UpperCAmelCase    ,    **UpperCAmelCase    ,    )

														# pop arguments that are not used in the foward but used nevertheless
														_snake_case  =    encoding_image_processor["""original_sizes"""]

														if hasattr(UpperCAmelCase    ,    """numpy"""    ):  # Checks if Torch or TF tensor
																					_snake_case  =    original_sizes.numpy()

														_snake_case,				_snake_case,				_snake_case  =    self._check_and_preprocess_points(
														    input_points=UpperCAmelCase    ,    input_labels=UpperCAmelCase    ,    input_boxes=UpperCAmelCase    ,    )

														_snake_case  =    self._normalize_and_convert(
														    UpperCAmelCase    ,    UpperCAmelCase    ,    input_points=UpperCAmelCase    ,    input_labels=UpperCAmelCase    ,    input_boxes=UpperCAmelCase    ,    return_tensors=UpperCAmelCase    ,    )

														return encoding_image_processor

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase="pt"    ,    )	->  Union[str, Any]:

														if input_points is not None:
																					if len(UpperCAmelCase    ) != len(UpperCAmelCase    ):
																												_snake_case  =    [
																												    self._normalize_coordinates(self.target_size    ,    UpperCAmelCase    ,    original_sizes[0]    ) for point in input_points
																												]
																					else:
																												_snake_case  =    [
																												    self._normalize_coordinates(self.target_size    ,    UpperCAmelCase    ,    UpperCAmelCase    )
																												    for point, original_size in zip(UpperCAmelCase    ,    UpperCAmelCase    )
																												]
																					# check that all arrays have the same shape
																					if not all(point.shape == input_points[0].shape for point in input_points    ):
																												if input_labels is not None:
																																			_snake_case,				_snake_case  =    self._pad_points_and_labels(UpperCAmelCase    ,    UpperCAmelCase    )

																					_snake_case  =    np.array(UpperCAmelCase    )

														if input_labels is not None:
																					_snake_case  =    np.array(UpperCAmelCase    )

														if input_boxes is not None:
																					if len(UpperCAmelCase    ) != len(UpperCAmelCase    ):
																												_snake_case  =    [
																												    self._normalize_coordinates(self.target_size    ,    UpperCAmelCase    ,    original_sizes[0]    ,    is_bounding_box=UpperCAmelCase    )
																												    for box in input_boxes
																												]
																					else:
																												_snake_case  =    [
																												    self._normalize_coordinates(self.target_size    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    is_bounding_box=UpperCAmelCase    )
																												    for box, original_size in zip(UpperCAmelCase    ,    UpperCAmelCase    )
																												]
																					_snake_case  =    np.array(UpperCAmelCase    )

														if input_boxes is not None:
																					if return_tensors == "pt":
																												_snake_case  =    torch.from_numpy(UpperCAmelCase    )
																												# boxes batch size of 1 by default
																												_snake_case  =    input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
																					elif return_tensors == "tf":
																												_snake_case  =    tf.convert_to_tensor(UpperCAmelCase    )
																												# boxes batch size of 1 by default
																												_snake_case  =    tf.expand_dims(UpperCAmelCase    ,    1    ) if len(input_boxes.shape    ) != 3 else input_boxes
																					encoding_image_processor.update({"""input_boxes""": input_boxes}    )
														if input_points is not None:
																					if return_tensors == "pt":
																												_snake_case  =    torch.from_numpy(UpperCAmelCase    )
																												# point batch size of 1 by default
																												_snake_case  =    input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
																					elif return_tensors == "tf":
																												_snake_case  =    tf.convert_to_tensor(UpperCAmelCase    )
																												# point batch size of 1 by default
																												_snake_case  =    tf.expand_dims(UpperCAmelCase    ,    1    ) if len(input_points.shape    ) != 4 else input_points
																					encoding_image_processor.update({"""input_points""": input_points}    )
														if input_labels is not None:
																					if return_tensors == "pt":
																												_snake_case  =    torch.from_numpy(UpperCAmelCase    )
																												# point batch size of 1 by default
																												_snake_case  =    input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
																					elif return_tensors == "tf":
																												_snake_case  =    tf.convert_to_tensor(UpperCAmelCase    )
																												# point batch size of 1 by default
																												_snake_case  =    tf.expand_dims(UpperCAmelCase    ,    1    ) if len(input_labels.shape    ) != 3 else input_labels
																					encoding_image_processor.update({"""input_labels""": input_labels}    )

														return encoding_image_processor

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  str:
														_snake_case  =    max([point.shape[0] for point in input_points]    )
														_snake_case  =    []
														for i, point in enumerate(UpperCAmelCase    ):
																					if point.shape[0] != expected_nb_points:
																												_snake_case  =    np.concatenate(
																												    [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]    ,    axis=0    )
																												_snake_case  =    np.append(input_labels[i]    ,    [self.point_pad_value]    )
																					processed_input_points.append(UpperCAmelCase    )
														_snake_case  =    processed_input_points
														return input_points, input_labels

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase=False    )	->  np.ndarray:
														_snake_case,				_snake_case  =    original_size
														_snake_case,				_snake_case  =    self.image_processor._get_preprocess_shape(UpperCAmelCase    ,    longest_edge=UpperCAmelCase    )
														_snake_case  =    deepcopy(UpperCAmelCase    ).astype(UpperCAmelCase    )

														if is_bounding_box:
																					_snake_case  =    coords.reshape(-1    ,    2    ,    2    )

														_snake_case  =    coords[..., 0] * (new_w / old_w)
														_snake_case  =    coords[..., 1] * (new_h / old_h)

														if is_bounding_box:
																					_snake_case  =    coords.reshape(-1    ,    4    )

														return coords

							def 			lowercase				(self    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    UpperCAmelCase=None    ,    )	->  Dict:

														if input_points is not None:
																					if hasattr(UpperCAmelCase    ,    """numpy"""    ):  # Checks for TF or Torch tensor
																												_snake_case  =    input_points.numpy().tolist()

																					if not isinstance(UpperCAmelCase    ,    UpperCAmelCase    ) or not isinstance(input_points[0]    ,    UpperCAmelCase    ):
																												raise ValueError("""Input points must be a list of list of floating points."""    )
																					_snake_case  =    [np.array(UpperCAmelCase    ) for input_point in input_points]
														else:
																					_snake_case  =    None

														if input_labels is not None:
																					if hasattr(UpperCAmelCase    ,    """numpy"""    ):
																												_snake_case  =    input_labels.numpy().tolist()

																					if not isinstance(UpperCAmelCase    ,    UpperCAmelCase    ) or not isinstance(input_labels[0]    ,    UpperCAmelCase    ):
																												raise ValueError("""Input labels must be a list of list integers."""    )
																					_snake_case  =    [np.array(UpperCAmelCase    ) for label in input_labels]
														else:
																					_snake_case  =    None

														if input_boxes is not None:
																					if hasattr(UpperCAmelCase    ,    """numpy"""    ):
																												_snake_case  =    input_boxes.numpy().tolist()

																					if (
																					    not isinstance(UpperCAmelCase    ,    UpperCAmelCase    )
																					    or not isinstance(input_boxes[0]    ,    UpperCAmelCase    )
																					    or not isinstance(input_boxes[0][0]    ,    UpperCAmelCase    )
																					):
																												raise ValueError("""Input boxes must be a list of list of list of floating points."""    )
																					_snake_case  =    [np.array(UpperCAmelCase    ).astype(np.floataa    ) for box in input_boxes]
														else:
																					_snake_case  =    None

														return input_points, input_labels, input_boxes

							@property
							def 			lowercase				(self    )	->  Optional[int]:
														_snake_case  =    self.image_processor.model_input_names
														return list(dict.fromkeys(UpperCAmelCase    )    )





							def 			lowercase				(self    ,    *UpperCAmelCase    ,    **UpperCAmelCase    )	->  int:
														return self.image_processor.post_process_masks(*UpperCAmelCase    ,    **UpperCAmelCase    )

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase__						=				logging.getLogger()


def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						argparse.ArgumentParser()
		parser.add_argument("""-f"""			)
		_UpperCamelCase         =						parser.parse_args()
		return args.f


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						{}
		_UpperCamelCase         =						os.path.join(lowerCAmelCase    ,	"""all_results.json"""			)
		if os.path.exists(lowerCAmelCase			):
				with open(lowerCAmelCase    ,	"""r"""			) as f:
						_UpperCamelCase         =						json.load(lowerCAmelCase			)
		else:
				raise ValueError(F'can\'t find {path}'			)
		return results


def 				__A()		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						torch.cuda.is_available() and torch_device == """cuda"""
		return is_using_cuda and is_apex_available()


lowerCamelCase__						=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCAmelCase__   ( __lowercase						):

						@classmethod
						def 			A_					(      cls					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						tempfile.mkdtemp()
								_UpperCamelCase         =						os.path.join(cls.tmpdir							,				"""default_config.yml"""					)
								write_basic_config(save_location=cls.configPath					)
								_UpperCamelCase         =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

						@classmethod
						def 			A_					(      cls					)       ->   str:


								'''simple docstring'''

								shutil.rmtree(cls.tmpdir					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""glue_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								if torch.cuda.device_count() > 1:
										# Skipping because there are not enough batches to train the model + would need a drop_last to work.
										return

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				1_00					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""clm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				42					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""mlm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						7 if get_gpu_count() > 1 else 2

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertLess(result["""train_loss"""]							,				0.5					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""ner_no_trainer"""					)					)					)

						@unittest.skip(reason="""Fix me @muellerzr"""					)
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result["""eval_f1"""]							,				28					)
								self.assertGreaterEqual(result["""eval_exact"""]							,				28					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""qa_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.8					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""swag_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_rouge1"""]							,				10					)
								self.assertGreaterEqual(result["""eval_rouge2"""]							,				2					)
								self.assertGreaterEqual(result["""eval_rougeL"""]							,				7					)
								self.assertGreaterEqual(result["""eval_rougeLsum"""]							,				7					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""summarization_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_bleu"""]							,				30					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""translation_no_trainer"""					)					)					)

						@slow
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						logging.StreamHandler(sys.stdout					)
								logger.addHandler(a					)

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,				0.10					)







						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

								if is_cuda_and_apex_available():
										testargs.append("""--fp16"""					)

								run_command(self._launch_args + testargs					)
								_UpperCamelCase         =						get_results(a					)
								# The base model scores a 25%
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.6					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""step_1"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""image_classification_no_trainer"""					)					)					)



