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
  
  
  __lowerCAmelCase				      =					[
      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)
  
  __lowerCAmelCase				      =					logging.getLogger()
  
  
  def       a			(   )    ->List[str]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()
  			parser.add_argument('''-f'''  )
  			SCREAMING_SNAKE_CASE    	=   parser.parse_args()
  			return args.f
  
  
  def       a			(   a  )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   {}
  			SCREAMING_SNAKE_CASE    	=   os.path.join(a		,	'''all_results.json'''  )
  			if os.path.exists(a  ):
  						with open(a		,	'''r'''  ) as f:
  									SCREAMING_SNAKE_CASE    	=   json.load(a  )
  			else:
  						raise ValueError(F"""can't find {path}"""  )
  			return results
  
  
  def       a			(   )    ->List[Any]:
  
  
  
  
  			'''simple docstring'''
  
  
  			return bool(tf.config.list_physical_devices('''GPU'''  )  )
  
  
  __lowerCAmelCase				      =					logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.''' )
  	def    snake_case__    (    self			:Union[str, Any] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_text_classification.py
              --model_name_or_path distilbert-base-uncased
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
              --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
              --do_train
              --do_eval
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=1
              --learning_rate=1e-4
              --max_steps=10
              --warmup_steps=2
              --seed=42
              --max_seq_length=128
              """.split()
  
  				if is_cuda_available():
  							testargs.append('''--fp16''' )
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_text_classification.main()
  							# Reset the mixed precision policy so we don't break other tests
  							tf.keras.mixed_precision.set_global_policy('''float32''' )
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''eval_accuracy''']      ,    0.75 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_clm.py
              --model_name_or_path distilgpt2
              --train_file ./tests/fixtures/sample_text.txt
              --validation_file ./tests/fixtures/sample_text.txt
              --do_train
              --do_eval
              --block_size 128
              --per_device_train_batch_size 2
              --per_device_eval_batch_size 1
              --num_train_epochs 2
              --output_dir {tmp_dir}
              --overwrite_output_dir
              """.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(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_clm.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertLess(result['''eval_perplexity''']      ,    1_0_0 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_mlm.py
              --model_name_or_path distilroberta-base
              --train_file ./tests/fixtures/sample_text.txt
              --validation_file ./tests/fixtures/sample_text.txt
              --max_seq_length 64
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --do_train
              --do_eval
              --prediction_loss_only
              --num_train_epochs=1
              --learning_rate=1e-4
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_mlm.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertLess(result['''eval_perplexity''']      ,    4_2 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   7 if get_gpu_count() > 1 else 2
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_ner.py
              --model_name_or_path bert-base-uncased
              --train_file tests/fixtures/tests_samples/conll/sample.json
              --validation_file tests/fixtures/tests_samples/conll/sample.json
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --do_train
              --do_eval
              --warmup_steps=2
              --learning_rate=2e-4
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=2
              --num_train_epochs={epochs}
              --seed 7
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_ner.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''accuracy''']      ,    0.75 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_qa.py
              --model_name_or_path bert-base-uncased
              --version_2_with_negative
              --train_file tests/fixtures/tests_samples/SQUAD/sample.json
              --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --max_steps=10
              --warmup_steps=2
              --do_train
              --do_eval
              --learning_rate=2e-4
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=1
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_squad.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''f1''']      ,    3_0 )
  							self.assertGreaterEqual(result['''exact''']      ,    3_0 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_swag.py
              --model_name_or_path bert-base-uncased
              --train_file tests/fixtures/tests_samples/swag/sample.json
              --validation_file tests/fixtures/tests_samples/swag/sample.json
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --max_steps=20
              --warmup_steps=2
              --do_train
              --do_eval
              --learning_rate=2e-4
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=1
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_swag.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''val_accuracy''']      ,    0.8 )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_summarization.py
              --model_name_or_path t5-small
              --train_file tests/fixtures/tests_samples/xsum/sample.json
              --validation_file tests/fixtures/tests_samples/xsum/sample.json
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --max_steps=50
              --warmup_steps=8
              --do_train
              --do_eval
              --learning_rate=2e-4
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=1
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_summarization.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''rouge1''']      ,    1_0 )
  							self.assertGreaterEqual(result['''rouge2''']      ,    2 )
  							self.assertGreaterEqual(result['''rougeL''']      ,    7 )
  							self.assertGreaterEqual(result['''rougeLsum''']      ,    7 )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Optional[int] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_translation.py
              --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
              --source_lang en
              --target_lang ro
              --train_file tests/fixtures/tests_samples/wmt16/sample.json
              --validation_file tests/fixtures/tests_samples/wmt16/sample.json
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --warmup_steps=8
              --do_train
              --do_eval
              --learning_rate=3e-3
              --num_train_epochs 12
              --per_device_train_batch_size=2
              --per_device_eval_batch_size=1
              --source_lang en_XX
              --target_lang ro_RO
          """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_translation.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''bleu''']      ,    3_0 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_auto_remove_tmp_dir()
  				SCREAMING_SNAKE_CASE    	=   f"""
              run_image_classification.py
              --dataset_name hf-internal-testing/cats_vs_dogs_sample
              --model_name_or_path microsoft/resnet-18
              --do_train
              --do_eval
              --learning_rate 1e-4
              --per_device_train_batch_size 2
              --per_device_eval_batch_size 1
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --dataloader_num_workers 16
              --num_train_epochs 2
              --train_val_split 0.1
              --seed 42
              --ignore_mismatched_sizes True
              """.split()
  
  				with patch.object(lowercase      ,    '''argv'''      ,    lowercase ):
  							run_image_classification.main()
  							SCREAMING_SNAKE_CASE    	=   get_results(lowercase )
  							self.assertGreaterEqual(result['''accuracy''']      ,    0.7 )
style_context: 
  import json
  
  import requests
  
  from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
  
  
  def       a			(   a  )    ->Tuple:
  
  
  
  
  			'''simple docstring'''
  
  
  			class 						lowerCamelCase			:
  
  
  
  
  
  
  
  				def __init__(    self			:int      ,    lowercase			:List[str] )		->			None:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							assert isinstance(lowercase      ,    (bytes, str) )
  							SCREAMING_SNAKE_CASE    	=   content
  
  
  
  
  
  
  
  				def    snake_case__    (    self			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  							return json.loads(self.content )
  
  			def mock_response(*a		,	**a  ):
  						assert args[0] == AUTHENTICATED_USER_ENDPOINT
  						assert "Authorization" in kwargs["headers"]
  						assert kwargs["headers"]["Authorization"].startswith('''token '''  )
  						assert "Accept" in kwargs["headers"]
  						return FakeResponse(B'''{"login":"test","id":1}'''  )
  
  			monkeypatch.setattr(a		,	'''get'''		,	a  )
  			SCREAMING_SNAKE_CASE    	=   fetch_github_info('''token'''  )
  			assert result["login"] == "test"
  			assert result["id"] == 1