import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


a_					      = logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
a_					      = direct_transformers_import(Path(__file__).parent)


a_					      = {
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     ['feature_extractor', 'tokenizer']
 # Names need to be attr_class for attr in attributes
 lowercase         =     None
 lowercase         =     None
 lowercase         =     None

 def __init__(				self			:							Optional[Any]     ,   *snake_case			:							int     ,   **snake_case			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  for key in kwargs:
   if key not in self.attributes:
    raise TypeError(f"Unexpected keyword argument {key}."    )
  for arg, attribute_name in zip(snake_case     ,   self.attributes    ):
   if attribute_name in kwargs:
    raise TypeError(f"Got multiple values for argument {attribute_name}."    )
   else:
    UpperCamelCase_      :		Any				=      arg

  if len(snake_case    ) != len(self.attributes    ):
   raise ValueError(
       f"This processor requires {len(self.attributes    )} arguments: {', '.join(self.attributes    )}. Got "
       f"{len(snake_case    )} arguments instead."    )

  # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  for attribute_name, arg in kwargs.items():
   UpperCamelCase_      :		int				=      getattr(self     ,   f"{attribute_name}_class"    )
   # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
   UpperCamelCase_      :		Optional[Any]				=      AUTO_TO_BASE_CLASS_MAPPING.get(snake_case     ,   snake_case    )
   if isinstance(snake_case     ,   snake_case    ):
    UpperCamelCase_      :		Optional[int]				=      tuple(getattr(snake_case     ,   snake_case    ) for n in class_name if n is not None    )
   else:
    UpperCamelCase_      :		Dict				=      getattr(snake_case     ,   snake_case    )

   if not isinstance(snake_case     ,   snake_case    ):
    raise ValueError(
        f"Received a {type(snake_case    ).__name__} for argument {attribute_name}, but a {class_name} was expected."    )

   setattr(self     ,   snake_case     ,   snake_case    )

 def __repr__(				self			:							int    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		int				=      [f"- {name}: {repr(getattr(self     ,   snake_case    )    )}" for name in self.attributes]
  UpperCamelCase_      :		Union[str, Any]				=      '\n'.join(snake_case    )
  return f"{self.__class__.__name__}:\n{attributes_repr}"

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Any     ,   snake_case			:							bool = False     ,   **snake_case			:							str    )     ->		int:

  """simple docstring"""





  os.makedirs(snake_case     ,   exist_ok=snake_case    )

  if push_to_hub:
   UpperCamelCase_      :		Optional[int]				=      kwargs.pop('commit_message'     ,   snake_case    )
   UpperCamelCase_      :		Tuple				=      kwargs.pop('repo_id'     ,   save_directory.split(os.path.sep    )[-1]    )
   UpperCamelCase_      :		int				=      self._create_repo(snake_case     ,   **snake_case    )
   UpperCamelCase_      :		Optional[int]				=      self._get_files_timestamps(snake_case    )
  # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  # loaded from the Hub.
  if self._auto_class is not None:
   UpperCamelCase_      :		Tuple				=      [getattr(self     ,   snake_case    ) for attribute_name in self.attributes]
   UpperCamelCase_      :		Any				=      [(a.init_kwargs if isinstance(snake_case     ,   snake_case    ) else a) for a in attrs]
   custom_object_save(self     ,   snake_case     ,   config=snake_case    )

  for attribute_name in self.attributes:
   UpperCamelCase_      :		List[Any]				=      getattr(self     ,   snake_case    )
   # Include the processor class in the attribute config so this processor can then be reloaded with the
   # `AutoProcessor` API.
   if hasattr(snake_case     ,   '_set_processor_class'    ):
    attribute._set_processor_class(self.__class__.__name__    )
   attribute.save_pretrained(snake_case    )

  if self._auto_class is not None:
   # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
   for attribute_name in self.attributes:
    UpperCamelCase_      :		Dict				=      getattr(self     ,   snake_case    )
    if isinstance(snake_case     ,   snake_case    ):
     del attribute.init_kwargs["auto_map"]

  if push_to_hub:
   self._upload_modified_files(
       snake_case     ,   snake_case     ,   snake_case     ,   commit_message=snake_case     ,   token=kwargs.get('use_auth_token'    )     ,   )

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							Optional[int]     ,   snake_case			:							Union[str, os.PathLike]     ,   snake_case			:							Optional[Union[str, os.PathLike]] = None     ,   snake_case			:							bool = False     ,   snake_case			:							bool = False     ,   snake_case			:							Optional[Union[str, bool]] = None     ,   snake_case			:							str = "main"     ,   **snake_case			:							int     ,   )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      cache_dir
  UpperCamelCase_      :		Any				=      force_download
  UpperCamelCase_      :		Tuple				=      local_files_only
  UpperCamelCase_      :		str				=      revision

  UpperCamelCase_      :		Any				=      kwargs.pop('use_auth_token'     ,   snake_case    )
  if use_auth_token is not None:
   warnings.warn(
       'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'     ,   snake_case    )
   if token is not None:
    raise ValueError(
        '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'    )
   UpperCamelCase_      :		Optional[int]				=      use_auth_token

  if token is not None:
   # change to `token` in a follow-up PR
   UpperCamelCase_      :		Optional[Any]				=      token

  UpperCamelCase_      :		List[Any]				=      cls._get_arguments_from_pretrained(snake_case     ,   **snake_case    )
  return cls(*snake_case    )

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							Dict     ,   snake_case			:							str="AutoProcessor"    )     ->		Union[str, Any]:

  """simple docstring"""





  if not isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      auto_class.__name__

  import transformers.models.auto as auto_module

  if not hasattr(snake_case     ,   snake_case    ):
   raise ValueError(f"{auto_class} is not a valid auto class."    )

  UpperCamelCase_      :		Optional[int]				=      auto_class

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							Optional[int]     ,   snake_case			:							Tuple     ,   **snake_case			:							List[str]    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      []
  for attribute_name in cls.attributes:
   UpperCamelCase_      :		Union[str, Any]				=      getattr(cls     ,   f"{attribute_name}_class"    )
   if isinstance(snake_case     ,   snake_case    ):
    UpperCamelCase_      :		Any				=      tuple(getattr(snake_case     ,   snake_case    ) if n is not None else None for n in class_name    )
    UpperCamelCase_      :		Optional[Any]				=      kwargs.get('use_fast'     ,   snake_case    )
    if use_fast and classes[1] is not None:
     UpperCamelCase_      :		Dict				=      classes[1]
    else:
     UpperCamelCase_      :		List[Any]				=      classes[0]
   else:
    UpperCamelCase_      :		List[Any]				=      getattr(snake_case     ,   snake_case    )

   args.append(attribute_class.from_pretrained(snake_case     ,   **snake_case    )    )
  return args


 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      getattr(self     ,   self.attributes[0]    )
  return getattr(snake_case     ,   'model_input_names'     ,   snake_case    )
a_					      = copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
   a_					      = ProcessorMixin.push_to_hub.__doc__.format(
       object='processor', object_class='AutoProcessor', object_files='processor files'
   )




import unittest

from cer import CER


a_					      = CER()





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      ['White House']
  UpperCamelCase_      :		Optional[Any]				=      ['white house']
  # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1818181818    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['were wolf']
  UpperCamelCase_      :		str				=      ['werewolf']
  # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  UpperCamelCase_      :		List[str]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1111111    ) < 1e-6    )

  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		Union[str, Any]				=      ['weae     wolf']
  # S = 1, D = 1, I = 0, N = 8, CER = 0.25
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

  # consecutive whitespaces case 1
  UpperCamelCase_      :		Optional[int]				=      ['were wolf']
  UpperCamelCase_      :		Dict				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

  # consecutive whitespaces case 2
  UpperCamelCase_      :		Optional[Any]				=      ['were   wolf']
  UpperCamelCase_      :		int				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		int				=      ['weaewolf']
  # S = 1, D = 0, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Dict				=      ['wereawolf']
  # S = 0, D = 1, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Any				=      ['wereolf']
  # S = 0, D = 0, I = 1, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[Any]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['werewolf']
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      ['werewolf', 'I am your father']
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

  UpperCamelCase_      :		Any				=      ['werewolf', 'I am your father', 'doge']
  UpperCamelCase_      :		List[str]				=      ['werxwolf', 'I       am your father', 'doge']
  # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.03571428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['My hovercraft', 'is full of eels']
  UpperCamelCase_      :		Union[str, Any]				=      ['My hovercraft is full', ' of eels']
  # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  # whitespace at the front of " of eels" will be strip during preporcessing
  # so need to insert 2 whitespaces
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case     ,   concatenate_texts=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.071428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      [' 能吞虾玻璃而 不霜身体啦']
  # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.4545454545    ) < 1e-6    )

  UpperCamelCase_      :		Union[str, Any]				=      ['我能吞下玻璃', '而不伤身体']
  UpperCamelCase_      :		int				=      ['我    能 吞 下 玻 璃', '而不伤身体']
  # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Any				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.454545454545    ) < 1e-6    )

  UpperCamelCase_      :		Any				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertFalse(snake_case     ,   0.0    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['']
  UpperCamelCase_      :		Optional[Any]				=      ['Hypothesis']
  with self.assertRaises(snake_case    ):
   cer.compute(predictions=snake_case     ,   references=snake_case    )


if __name__ == "__main__":
   unittest.main()



