




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


UpperCAmelCase__    :							Tuple       		=     {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							List[Any]       		=     [
       'Constraint',
       'ConstraintListState',
       'DisjunctiveConstraint',
       'PhrasalConstraint',
   ]
   UpperCAmelCase__    :							int       		=     [
       'BeamHypotheses',
       'BeamScorer',
       'BeamSearchScorer',
       'ConstrainedBeamSearchScorer',
   ]
   UpperCAmelCase__    :							List[str]       		=     [
       'EpsilonLogitsWarper',
       'EtaLogitsWarper',
       'ForcedBOSTokenLogitsProcessor',
       'ForcedEOSTokenLogitsProcessor',
       'HammingDiversityLogitsProcessor',
       'InfNanRemoveLogitsProcessor',
       'LogitsProcessor',
       'LogitsProcessorList',
       'LogitsWarper',
       'MinLengthLogitsProcessor',
       'MinNewTokensLengthLogitsProcessor',
       'NoBadWordsLogitsProcessor',
       'NoRepeatNGramLogitsProcessor',
       'PrefixConstrainedLogitsProcessor',
       'RepetitionPenaltyLogitsProcessor',
       'SequenceBiasLogitsProcessor',
       'EncoderRepetitionPenaltyLogitsProcessor',
       'TemperatureLogitsWarper',
       'TopKLogitsWarper',
       'TopPLogitsWarper',
       'TypicalLogitsWarper',
       'EncoderNoRepeatNGramLogitsProcessor',
       'ExponentialDecayLengthPenalty',
       'LogitNormalization',
   ]
   UpperCAmelCase__    :							int       		=     [
       'MaxNewTokensCriteria',
       'MaxLengthCriteria',
       'MaxTimeCriteria',
       'StoppingCriteria',
       'StoppingCriteriaList',
       'validate_stopping_criteria',
   ]
   UpperCAmelCase__    :							Dict       		=     [
       'GenerationMixin',
       'top_k_top_p_filtering',
       'GreedySearchEncoderDecoderOutput',
       'GreedySearchDecoderOnlyOutput',
       'SampleEncoderDecoderOutput',
       'SampleDecoderOnlyOutput',
       'BeamSearchEncoderDecoderOutput',
       'BeamSearchDecoderOnlyOutput',
       'BeamSampleEncoderDecoderOutput',
       'BeamSampleDecoderOnlyOutput',
       'ContrastiveSearchEncoderDecoderOutput',
       'ContrastiveSearchDecoderOnlyOutput',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							List[str]       		=     [
       'TFForcedBOSTokenLogitsProcessor',
       'TFForcedEOSTokenLogitsProcessor',
       'TFLogitsProcessor',
       'TFLogitsProcessorList',
       'TFLogitsWarper',
       'TFMinLengthLogitsProcessor',
       'TFNoBadWordsLogitsProcessor',
       'TFNoRepeatNGramLogitsProcessor',
       'TFRepetitionPenaltyLogitsProcessor',
       'TFTemperatureLogitsWarper',
       'TFTopKLogitsWarper',
       'TFTopPLogitsWarper',
       'TFForceTokensLogitsProcessor',
       'TFSuppressTokensAtBeginLogitsProcessor',
       'TFSuppressTokensLogitsProcessor',
   ]
   UpperCAmelCase__    :							Dict       		=     [
       'TFGenerationMixin',
       'tf_top_k_top_p_filtering',
       'TFGreedySearchDecoderOnlyOutput',
       'TFGreedySearchEncoderDecoderOutput',
       'TFSampleEncoderDecoderOutput',
       'TFSampleDecoderOnlyOutput',
       'TFBeamSearchEncoderDecoderOutput',
       'TFBeamSearchDecoderOnlyOutput',
       'TFBeamSampleEncoderDecoderOutput',
       'TFBeamSampleDecoderOnlyOutput',
       'TFContrastiveSearchEncoderDecoderOutput',
       'TFContrastiveSearchDecoderOnlyOutput',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Optional[Any]       		=     [
       'FlaxForcedBOSTokenLogitsProcessor',
       'FlaxForcedEOSTokenLogitsProcessor',
       'FlaxLogitsProcessor',
       'FlaxLogitsProcessorList',
       'FlaxLogitsWarper',
       'FlaxMinLengthLogitsProcessor',
       'FlaxTemperatureLogitsWarper',
       'FlaxTopKLogitsWarper',
       'FlaxTopPLogitsWarper',
   ]
   UpperCAmelCase__    :							Any       		=     [
       'FlaxGenerationMixin',
       'FlaxGreedySearchOutput',
       'FlaxSampleOutput',
       'FlaxBeamSearchOutput',
   ]

if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
      from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
      from .logits_process import (
          EncoderNoRepeatNGramLogitsProcessor,
          EncoderRepetitionPenaltyLogitsProcessor,
          EpsilonLogitsWarper,
          EtaLogitsWarper,
          ExponentialDecayLengthPenalty,
          ForcedBOSTokenLogitsProcessor,
          ForcedEOSTokenLogitsProcessor,
          HammingDiversityLogitsProcessor,
          InfNanRemoveLogitsProcessor,
          LogitNormalization,
          LogitsProcessor,
          LogitsProcessorList,
          LogitsWarper,
          MinLengthLogitsProcessor,
          MinNewTokensLengthLogitsProcessor,
          NoBadWordsLogitsProcessor,
          NoRepeatNGramLogitsProcessor,
          PrefixConstrainedLogitsProcessor,
          RepetitionPenaltyLogitsProcessor,
          SequenceBiasLogitsProcessor,
          TemperatureLogitsWarper,
          TopKLogitsWarper,
          TopPLogitsWarper,
          TypicalLogitsWarper,
      )
      from .stopping_criteria import (
          MaxLengthCriteria,
          MaxNewTokensCriteria,
          MaxTimeCriteria,
          StoppingCriteria,
          StoppingCriteriaList,
          validate_stopping_criteria,
      )
      from .utils import (
          BeamSampleDecoderOnlyOutput,
          BeamSampleEncoderDecoderOutput,
          BeamSearchDecoderOnlyOutput,
          BeamSearchEncoderDecoderOutput,
          ContrastiveSearchDecoderOnlyOutput,
          ContrastiveSearchEncoderDecoderOutput,
          GenerationMixin,
          GreedySearchDecoderOnlyOutput,
          GreedySearchEncoderDecoderOutput,
          SampleDecoderOnlyOutput,
          SampleEncoderDecoderOutput,
          top_k_top_p_filtering,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tf_logits_process import (
          TFForcedBOSTokenLogitsProcessor,
          TFForcedEOSTokenLogitsProcessor,
          TFForceTokensLogitsProcessor,
          TFLogitsProcessor,
          TFLogitsProcessorList,
          TFLogitsWarper,
          TFMinLengthLogitsProcessor,
          TFNoBadWordsLogitsProcessor,
          TFNoRepeatNGramLogitsProcessor,
          TFRepetitionPenaltyLogitsProcessor,
          TFSuppressTokensAtBeginLogitsProcessor,
          TFSuppressTokensLogitsProcessor,
          TFTemperatureLogitsWarper,
          TFTopKLogitsWarper,
          TFTopPLogitsWarper,
      )
      from .tf_utils import (
          TFBeamSampleDecoderOnlyOutput,
          TFBeamSampleEncoderDecoderOutput,
          TFBeamSearchDecoderOnlyOutput,
          TFBeamSearchEncoderDecoderOutput,
          TFContrastiveSearchDecoderOnlyOutput,
          TFContrastiveSearchEncoderDecoderOutput,
          TFGenerationMixin,
          TFGreedySearchDecoderOnlyOutput,
          TFGreedySearchEncoderDecoderOutput,
          TFSampleDecoderOnlyOutput,
          TFSampleEncoderDecoderOutput,
          tf_top_k_top_p_filtering,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .flax_logits_process import (
          FlaxForcedBOSTokenLogitsProcessor,
          FlaxForcedEOSTokenLogitsProcessor,
          FlaxLogitsProcessor,
          FlaxLogitsProcessorList,
          FlaxLogitsWarper,
          FlaxMinLengthLogitsProcessor,
          FlaxTemperatureLogitsWarper,
          FlaxTopKLogitsWarper,
          FlaxTopPLogitsWarper,
      )
      from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
   import sys

   UpperCAmelCase__    :							Union[str, Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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__    :							Any       		=     [
    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__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





  _lowercase:			str      =     {}
  _lowercase:			Union[str, Any]      =     os.path.join(UpperCamelCase       ,  'all_results.json'				)
  if os.path.exists(UpperCamelCase				):
    with open(UpperCamelCase       ,  'r'				) as f:
      _lowercase:			Dict      =     json.load(UpperCamelCase				)
  else:
    raise ValueError(F'can\'t find {path}'				)
  return results


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





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


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






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






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_clm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






   def 			_a    (						self :							int  )->      Any:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     _lowercase:			List[Any]      =     7 if get_gpu_count() > 1 else 2

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_ner.main()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			int      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Any      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			str      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['rouge1']    ,       10  )
       self.assertGreaterEqual(result['rouge2']    ,       2  )
       self.assertGreaterEqual(result['rougeL']    ,       7  )
       self.assertGreaterEqual(result['rougeLsum']    ,       7  )






   @slow
   def 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_translation.main()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Dict      =     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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_image_classification.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
