






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase       :					Optional[int]									=		{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					List[Any]									=		['BertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Tuple									=		[
       'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BertForMaskedLM',
       'BertForMultipleChoice',
       'BertForNextSentencePrediction',
       'BertForPreTraining',
       'BertForQuestionAnswering',
       'BertForSequenceClassification',
       'BertForTokenClassification',
       'BertLayer',
       'BertLMHeadModel',
       'BertModel',
       'BertPreTrainedModel',
       'load_tf_weights_in_bert',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					List[Any]									=		[
       'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFBertEmbeddings',
       'TFBertForMaskedLM',
       'TFBertForMultipleChoice',
       'TFBertForNextSentencePrediction',
       'TFBertForPreTraining',
       'TFBertForQuestionAnswering',
       'TFBertForSequenceClassification',
       'TFBertForTokenClassification',
       'TFBertLMHeadModel',
       'TFBertMainLayer',
       'TFBertModel',
       'TFBertPreTrainedModel',
   ]
try:
   if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					List[str]									=		['TFBertTokenizer']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					List[Any]									=		[
       'FlaxBertForCausalLM',
       'FlaxBertForMaskedLM',
       'FlaxBertForMultipleChoice',
       'FlaxBertForNextSentencePrediction',
       'FlaxBertForPreTraining',
       'FlaxBertForQuestionAnswering',
       'FlaxBertForSequenceClassification',
       'FlaxBertForTokenClassification',
       'FlaxBertModel',
       'FlaxBertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
   from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_fast import BertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert import (
          BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BertForMaskedLM,
          BertForMultipleChoice,
          BertForNextSentencePrediction,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          BertForTokenClassification,
          BertLayer,
          BertLMHeadModel,
          BertModel,
          BertPreTrainedModel,
          load_tf_weights_in_bert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bert import (
          TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBertEmbeddings,
          TFBertForMaskedLM,
          TFBertForMultipleChoice,
          TFBertForNextSentencePrediction,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertForTokenClassification,
          TFBertLMHeadModel,
          TFBertMainLayer,
          TFBertModel,
          TFBertPreTrainedModel,
      )

   try:
      if not is_tensorflow_text_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_tf import TFBertTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bert import (
          FlaxBertForCausalLM,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForNextSentencePrediction,
          FlaxBertForPreTraining,
          FlaxBertForQuestionAnswering,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertModel,
          FlaxBertPreTrainedModel,
      )

else:
   import sys

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












"""simple docstring"""


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_UpperCamelCase       :					List[str]									=		logging.getLogger()




def     snake_case						(A_   :Path			,    A_   :list   ):







  '''simple docstring'''

  a  :    Optional[int]       =     '\n'.join(A_   )
  Path(A_   ).open('w'   ).writelines(A_   )


_UpperCamelCase       :					Optional[Any]									=		'patrickvonplaten/t5-tiny-random'
_UpperCamelCase       :					str									=		'sshleifer/bart-tiny-random'
_UpperCamelCase       :					Any									=		'sshleifer/tiny-mbart'

_UpperCamelCase       :					List[str]									=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 		snake_case						(						UpperCAmelCase ):





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      Optional[Any]    ):


      '''simple docstring'''




      a  :    Dict       =     Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
      a  :    List[str]       =     input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()
      a  :    Optional[Any]       =     [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
      _dump_articles(A       ,	A    )

      a  :    Tuple       =     str(Path(self.get_auto_remove_tmp_dir()    ) / 'scores.json'    )
      a  :    List[Any]       =     'translation_en_to_de' if model == T5_TINY else 'summarization'
      a  :    List[Any]       =     F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

      with patch.object(A       ,	'argv'       ,	A    ):
        run_generate()
        assert Path(A    ).exists()
        # os.remove(Path(output_file_name))





    def        lowerCamelCase__   (							self			:      Optional[Any]    ):


      '''simple docstring'''




      self.run_eval_tester(A    )





    @parameterized.expand([BART_TINY, MBART_TINY]    )
    @slow
    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      List[Any]    ):


      '''simple docstring'''




      self.run_eval_tester(A    )





    @parameterized.expand([T5_TINY, MBART_TINY]    )
    @slow
    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	A			:      List[Any]    ):


      '''simple docstring'''




      a  :    Dict       =     Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
      a  :    int       =     input_file_name.parent / 'utest_output.txt'
      assert not output_file_name.exists()

      a  :    Optional[Any]       =     {
          'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
          'de': [
              'Maschinelles Lernen ist großartig, oder?',
              'Ich esse gerne Bananen',
              'Morgen ist wieder ein toller Tag!',
          ],
      }

      a  :    int       =     Path(self.get_auto_remove_tmp_dir()    )
      a  :    int       =     str(tmp_dir / 'scores.json'    )
      a  :    Optional[int]       =     str(tmp_dir / 'val.target'    )
      _dump_articles(A       ,	text['en']    )
      _dump_articles(A       ,	text['de']    )
      a  :    List[str]       =     'translation_en_to_de' if model == T5_TINY else 'summarization'
      a  :    Any       =     F'''
            run_eval_search.py
            {model}
            {str(A    )}
            {str(A    )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
      testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']    )

      with patch.object(A       ,	'argv'       ,	A    ):
        with CaptureStdout() as cs:
          run_search()
        a  :    Tuple       =     [' num_beams | length_penalty', model, 'Best score args']
        a  :    List[str]       =     ['Info']
        if "translation" in task:
          expected_strings.append('bleu'    )
        else:
          expected_strings.extend(A    )
        for w in expected_strings:
          assert w in cs.out
        for w in un_expected_strings:
          assert w not in cs.out
        assert Path(A    ).exists()
        os.remove(Path(A    )    )




