



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


__A				    =						[
    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)

__A				    =						logging.getLogger()



def        __a     (    )      -> Dict:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					int				)      -> List[str]:






						'''simple docstring'''



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



def        __a     (    )      -> Tuple:






						'''simple docstring'''



						return bool(tf.config.list_physical_devices("""GPU"""				)				)


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






class 		lowercase						( a__):






	"""simple docstring"""


	@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Dict:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_text_classification.main()
													# Reset the mixed precision policy so we don't break other tests
													tf.keras.mixed_precision.set_global_policy("""float32"""     )
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""eval_accuracy"""]						,  0.75     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Tuple:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_clm.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertLess(result["""eval_perplexity"""]						,  100     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Any:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_mlm.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertLess(result["""eval_perplexity"""]						,  42     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> Any:
							UpperCAmelCase_=   7 if get_gpu_count() > 1 else 2

							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_ner.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""accuracy"""]						,  0.75     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Dict:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_squad.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""f1"""]						,  30     )
													self.assertGreaterEqual(result["""exact"""]						,  30     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Optional[Any]:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_swag.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""val_accuracy"""]						,  0.8     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> int:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_summarization.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""rouge1"""]						,  10     )
													self.assertGreaterEqual(result["""rouge2"""]						,  2     )
													self.assertGreaterEqual(result["""rougeL"""]						,  7     )
													self.assertGreaterEqual(result["""rougeLsum"""]						,  7     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Dict:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_translation.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""bleu"""]						,  30     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Union[str, Any]:
							UpperCAmelCase_=   self.get_auto_remove_tmp_dir()
							UpperCAmelCase_=   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(_snake_case						,  """argv"""						,  _snake_case     ):
													run_image_classification.main()
													UpperCAmelCase_=   get_results(_snake_case     )
													self.assertGreaterEqual(result["""accuracy"""]						,  0.7     )











"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 	a				(					a__   ):
 snake_case__    	=    '''AutoFeatureExtractor'''
 snake_case__    	=    '''Speech2Text2Tokenizer'''



 def __init__(			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  super().__init__(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False



 def __call__(			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  if self._in_target_context_manager:
   return self.current_processor(*_snake_case							,    **_snake_case			)

  if "raw_speech" in kwargs:
   warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
   lowerCAmelCase  							=      kwargs.pop('raw_speech'			)
  else:
   lowerCAmelCase  							=      kwargs.pop('audio'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('sampling_rate'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('text'							,    _snake_case			)
  if len(_snake_case			) > 0:
   lowerCAmelCase  							=      args[0]
   lowerCAmelCase  							=      args[1:]

  if audio is None and text is None:
   raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

  if audio is not None:
   lowerCAmelCase  							=      self.feature_extractor(_snake_case							,    *_snake_case							,    sampling_rate=_snake_case							,    **_snake_case			)
  if text is not None:
   lowerCAmelCase  							=      self.tokenizer(_snake_case							,    **_snake_case			)

  if text is None:
   return inputs
  elif audio is None:
   return encodings
  else:
   lowerCAmelCase  							=      encodings['input_ids']
   return inputs



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.batch_decode(*_snake_case							,    **_snake_case			)



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.decode(*_snake_case							,    **_snake_case			)



 @contextmanager
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  warnings.warn(
      '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
      'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
      'your audio inputs, or in a separate call.'			)
  lowerCAmelCase  							=      True
  lowerCAmelCase  							=      self.tokenizer
  yield
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False





