




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class   __a:






    """simple docstring"""





    lowerCAmelCase        =				None





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.feature_extraction_class(**self.feat_extract_dict  )
         UpperCAmelCase_      :							Any       =      json.loads(feat_extract.to_json_string()  )
         for key, value in self.feat_extract_dict.items():
              self.assertEqual(obj[key]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							str       =      self.feature_extraction_class(**self.feat_extract_dict  )

         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_      :							Optional[int]       =      os.path.join(_SCREAMING_SNAKE_CASE       ,'''feat_extract.json'''  )
              feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[str]       =      self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE  )

         self.assertEqual(feat_extract_second.to_dict()       ,feat_extract_first.to_dict()  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[int]       =      self.feature_extraction_class(**self.feat_extract_dict  )

         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_      :							Optional[Any]       =      feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE  )[0]
              check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Tuple       =      self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE  )

         self.assertEqual(feat_extract_second.to_dict()       ,feat_extract_first.to_dict()  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Tuple       =      self.feature_extraction_class()
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)