"""simple docstring"""




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


__lowerCamelCase									=	logging.get_logger(__name__)

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


__lowerCamelCase									=	{
    """AutoTokenizer""": """PreTrainedTokenizerBase""",
    """AutoFeatureExtractor""": """FeatureExtractionMixin""",
    """AutoImageProcessor""": """ImageProcessingMixin""",
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    int    				=		['''feature_extractor''', '''tokenizer''']
						# Names need to be attr_class for attr in attributes
						lowerCAmelCase__	:    Any    				=		None
						lowerCAmelCase__	:    Dict    				=		None
						lowerCAmelCase__	:    Dict    				=		None





						def __init__(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					int:







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

													if len(__UpperCAmelCase  ) != len(self.attributes  ):
																				raise ValueError(
																				    f'''This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '''
																				    f'''{len(__UpperCAmelCase  )} 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():
																				A__						  =				getattr(self		,f'''{attribute_name}_class'''  )
																				# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																				A__						  =				AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase		,__UpperCAmelCase  )
																				if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				tuple(getattr(__UpperCAmelCase		,__UpperCAmelCase  ) for n in class_name if n is not None  )
																				else:
																											A__						  =				getattr(__UpperCAmelCase		,__UpperCAmelCase  )

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

																				setattr(self		,__UpperCAmelCase		,__UpperCAmelCase  )





						def __repr__(							self  )       ->					Union[str, Any]:
													A__						  =				[f'''- {name}: {repr(getattr(self		,__UpperCAmelCase  )  )}''' for name in self.attributes]
													A__						  =				'''\n'''.join(__UpperCAmelCase  )
													return f'''{self.__class__.__name__}:\n{attributes_repr}'''





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = False		,**__UpperCAmelCase  )       ->					Union[str, Any]:
													os.makedirs(__UpperCAmelCase		,exist_ok=__UpperCAmelCase  )

													if push_to_hub:
																				A__						  =				kwargs.pop('commit_message'		,__UpperCAmelCase  )
																				A__						  =				kwargs.pop('repo_id'		,save_directory.split(os.path.sep  )[-1]  )
																				A__						  =				self._create_repo(__UpperCAmelCase		,**__UpperCAmelCase  )
																				A__						  =				self._get_files_timestamps(__UpperCAmelCase  )
													# 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:
																				A__						  =				[getattr(self		,__UpperCAmelCase  ) for attribute_name in self.attributes]
																				A__						  =				[(a.init_kwargs if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) else a) for a in attrs]
																				custom_object_save(self		,__UpperCAmelCase		,config=__UpperCAmelCase  )

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

													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:
																											A__						  =				getattr(self		,__UpperCAmelCase  )
																											if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																																		del attribute.init_kwargs["auto_map"]

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





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,__UpperCAmelCase = "main"		,**__UpperCAmelCase		,)       ->					Dict:
													A__						  =				cache_dir
													A__						  =				force_download
													A__						  =				local_files_only
													A__						  =				revision

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

													if token is not None:
																				# change to `token` in a follow-up PR
																				A__						  =				token

													A__						  =				cls._get_arguments_from_pretrained(__UpperCAmelCase		,**__UpperCAmelCase  )
													return cls(*__UpperCAmelCase  )





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase="AutoProcessor"  )       ->					Union[str, Any]:







													if not isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				auto_class.__name__

													import transformers.models.auto as auto_module

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

													A__						  =				auto_class





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Optional[int]:
													A__						  =				[]
													for attribute_name in cls.attributes:
																				A__						  =				getattr(cls		,f'''{attribute_name}_class'''  )
																				if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				tuple(getattr(__UpperCAmelCase		,__UpperCAmelCase  ) if n is not None else None for n in class_name  )
																											A__						  =				kwargs.get('use_fast'		,__UpperCAmelCase  )
																											if use_fast and classes[1] is not None:
																																		A__						  =				classes[1]
																											else:
																																		A__						  =				classes[0]
																				else:
																											A__						  =				getattr(__UpperCAmelCase		,__UpperCAmelCase  )

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





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				getattr(self		,self.attributes[0]  )
													return getattr(__UpperCAmelCase		,'model_input_names'		,__UpperCAmelCase  )






__lowerCamelCase									=	copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
				__lowerCamelCase									=	ProcessorMixin.push_to_hub.__doc__.format(
				    object="processor", object_class="AutoProcessor", object_files="processor files"
				)













"""simple docstring"""





import numpy as np



def      __lowercase       (     a			:					Union[str, Any]							,		a			:					Optional[Any]							,		a			:					List[str]			)       ->       Dict:
      __snake_case   :     Dict	      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      __snake_case   :     int	      =np.floor(a			)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      __snake_case   :     Dict	      =int(n_draws - floored.sum()			)
      if need_to_add > 0:
            __snake_case   :     List[str]	      =continuous - floored
            __snake_case   :     str	      =np.sort(np.unique(a			)			)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((__snake_case)      ,					)   :     Tuple	      =np.where(remainder == value			)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  __snake_case   :     int	      =min(len(a			)							,		a			)
                  __snake_case   :     Any	      =rng.choice(a							,		size=a							,		replace=a			)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa			)



def      __lowercase       (     a			:					Any							,		a			:					str							,		a			:					Dict							,		a			:					Union[str, Any]							,		a			:					Union[str, Any]=10			)       ->       Any:
      __snake_case      ,					__snake_case   :     int	      =np.unique(a							,		return_inverse=a			)
      __snake_case   :     Dict	      =classes.shape[0]
      __snake_case   :     int	      =np.bincount(a			)
      if np.min(a			) < 2:
            raise ValueError('''Minimum class count error'''			)
      if n_train < n_classes:
            raise ValueError(
                '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)			)
      if n_test < n_classes:
            raise ValueError(
                '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)			)
      __snake_case   :     str	      =np.split(np.argsort(a							,		kind='''mergesort'''			)							,		np.cumsum(a			)[:-1]			)
      for _ in range(a			):
            __snake_case   :     str	      =approximate_mode(a							,		a							,		a			)
            __snake_case   :     List[str]	      =class_counts - n_i
            __snake_case   :     Union[str, Any]	      =approximate_mode(a							,		a							,		a			)

            __snake_case   :     int	      =[]
            __snake_case   :     Union[str, Any]	      =[]

            for i in range(a			):
                  __snake_case   :     Union[str, Any]	      =rng.permutation(class_counts[i]			)
                  __snake_case   :     Dict	      =class_indices[i].take(a							,		mode='''clip'''			)
                  train.extend(perm_indices_class_i[: n_i[i]]			)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
            __snake_case   :     Any	      =rng.permutation(a			)
            __snake_case   :     int	      =rng.permutation(a			)

            yield train, test


