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__	:  str 		=[
      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__	:  Dict 		=logging.getLogger()
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				List[str]:
  							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
  							parser.add_argument('-f'     )
  							__SCREAMING_SNAKE_CASE								=      parser.parse_args()
  							return args.f
  
  
  
  
  
  
  def 			__lowercase     (				a__     )       ->				Tuple:
  							__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 			__lowercase     (				)       ->				Dict:
  							return bool(tf.config.list_physical_devices('GPU'     )     )
  
  
  lowerCAmelCase__	:  List[Any] 		=logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_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'	)
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['eval_accuracy']   ,							0.7_5	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_clm.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertLess(result['eval_perplexity']   ,							100	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_mlm.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertLess(result['eval_perplexity']   ,							42	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_ner.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['accuracy']   ,							0.7_5	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_squad.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['f1']   ,							30	)
  																				self.assertGreaterEqual(result['exact']   ,							30	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_swag.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['val_accuracy']   ,							0.8	)
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_summarization.main()
  																				__SCREAMING_SNAKE_CASE								=      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	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_translation.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['bleu']   ,							30	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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(_A   ,							'argv'   ,							_A	):
  																				run_image_classification.main()
  																				__SCREAMING_SNAKE_CASE								=      get_results(_A	)
  																				self.assertGreaterEqual(result['accuracy']   ,							0.7	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  