code: 
  
  
  import argparse
  import json
  import logging
  import os
  import sys
  from unittest import skip
  from unittest.mock import patch
  
  import tensorflow as tf
  
  from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
  
  
  UpperCAmelCase      :						Tuple    =		[
      os.path.join(os.path.dirname(__file__), dirname)
      for dirname in [
          "text-generation",
          "text-classification",
          "token-classification",
          "language-modeling",
          "multiple-choice",
          "question-answering",
          "summarization",
          "translation",
          "image-classification",
      ]
  ]
  sys.path.extend(SRC_DIRS)
  
  
  if SRC_DIRS is not None:
  			import run_clm
  			import run_image_classification
  			import run_mlm
  			import run_ner
  			import run_qa as run_squad
  			import run_summarization
  			import run_swag
  			import run_text_classification
  			import run_translation
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  UpperCAmelCase      :						str    =		logging.getLogger()
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     argparse.ArgumentParser()
  			parser.add_argument("""-f"""			)
  			lowerCamelCase								=     parser.parse_args()
  			return args.f
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	str			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     {}
  			lowerCamelCase								=     os.path.join(lowerCamelCase__     ,       """all_results.json"""			)
  			if os.path.exists(lowerCamelCase__			):
  						with open(lowerCamelCase__     ,       """r"""			) as f:
  									lowerCamelCase								=     json.load(lowerCamelCase__			)
  			else:
  						raise ValueError(f'can\'t find {path}'			)
  			return results
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return bool(tf.config.list_physical_devices("""GPU"""			)			)
  
  
  UpperCAmelCase      :						Any    =		logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  
  
  
  
  
  
  
  class 					__lowercase					(       __lowerCamelCase     ):
  
  
  
  		"""simple docstring"""
  
  		@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""  )
  		def 				__A      (   self  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  					if is_cuda_available():
  								testargs.append("""--fp16"""  )
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_text_classification.main()
  								# Reset the mixed precision policy so we don't break other tests
  								tf.keras.mixed_precision.set_global_policy("""float32"""  )
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""eval_accuracy"""]		,			0.75  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()
  
  					if len(tf.config.list_physical_devices("""GPU"""  )  ) > 1:
  								# Skipping because there are not enough batches to train the model + would need a drop_last to work.
  								return
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_clm.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertLess(result["""eval_perplexity"""]		,			1_00  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_mlm.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertLess(result["""eval_perplexity"""]		,			42  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     7 if get_gpu_count() > 1 else 2
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_ner.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""accuracy"""]		,			0.75  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_squad.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""f1"""]		,			30  )
  								self.assertGreaterEqual(result["""exact"""]		,			30  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_swag.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""val_accuracy"""]		,			0.8  )
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_summarization.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""rouge1"""]		,			10  )
  								self.assertGreaterEqual(result["""rouge2"""]		,			2  )
  								self.assertGreaterEqual(result["""rougeL"""]		,			7  )
  								self.assertGreaterEqual(result["""rougeLsum"""]		,			7  )
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_translation.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""bleu"""]		,			30  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.get_auto_remove_tmp_dir()
  					lowerCamelCase								=     F'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()
  
  					with patch.object(A		,			"""argv"""		,			A  ):
  								run_image_classification.main()
  								lowerCamelCase								=     get_results(A  )
  								self.assertGreaterEqual(result["""accuracy"""]		,			0.7  )
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase      :						List[str]    =		{
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "BigBirdForCausalLM",
  		    "BigBirdForMaskedLM",
  		    "BigBirdForMultipleChoice",
  		    "BigBirdForPreTraining",
  		    "BigBirdForQuestionAnswering",
  		    "BigBirdForSequenceClassification",
  		    "BigBirdForTokenClassification",
  		    "BigBirdLayer",
  		    "BigBirdModel",
  		    "BigBirdPreTrainedModel",
  		    "load_tf_weights_in_big_bird",
  		]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "FlaxBigBirdForCausalLM",
  		    "FlaxBigBirdForMaskedLM",
  		    "FlaxBigBirdForMultipleChoice",
  		    "FlaxBigBirdForPreTraining",
  		    "FlaxBigBirdForQuestionAnswering",
  		    "FlaxBigBirdForSequenceClassification",
  		    "FlaxBigBirdForTokenClassification",
  		    "FlaxBigBirdModel",
  		    "FlaxBigBirdPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird import BigBirdTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_big_bird import (
  				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    BigBirdForCausalLM,
  				    BigBirdForMaskedLM,
  				    BigBirdForMultipleChoice,
  				    BigBirdForPreTraining,
  				    BigBirdForQuestionAnswering,
  				    BigBirdForSequenceClassification,
  				    BigBirdForTokenClassification,
  				    BigBirdLayer,
  				    BigBirdModel,
  				    BigBirdPreTrainedModel,
  				    load_tf_weights_in_big_bird,
  				)
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_big_bird import (
  				    FlaxBigBirdForCausalLM,
  				    FlaxBigBirdForMaskedLM,
  				    FlaxBigBirdForMultipleChoice,
  				    FlaxBigBirdForPreTraining,
  				    FlaxBigBirdForQuestionAnswering,
  				    FlaxBigBirdForSequenceClassification,
  				    FlaxBigBirdForTokenClassification,
  				    FlaxBigBirdModel,
  				    FlaxBigBirdPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  