




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					:     Dict					  =  logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_lowerCamelCase					:     Optional[Any]					  =  direct_transformers_import(Path(__file__).parent)


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



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





								'''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(__SCREAMING_SNAKE_CASE						,		self.attributes					):
												if attribute_name in kwargs:
																raise TypeError(f"Got multiple values for argument {attribute_name}."					)
												else:
																_lowerCAmelCase			:			Optional[int]        =					arg

								if len(__SCREAMING_SNAKE_CASE					) != len(self.attributes					):
												raise ValueError(
												    f"This processor requires {len(self.attributes					)} arguments: {', '.join(self.attributes					)}. Got "
												    f"{len(__SCREAMING_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():
												_lowerCAmelCase			:			Optional[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.
												_lowerCAmelCase			:			Any        =					AUTO_TO_BASE_CLASS_MAPPING.get(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)
												if isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					):
																_lowerCAmelCase			:			Dict        =					tuple(getattr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					) for n in class_name if n is not None					)
												else:
																_lowerCAmelCase			:			str        =					getattr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)

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

												setattr(self						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)



				def __repr__(					self					:						int					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					[f"- {name}: {repr(getattr(self						,		__SCREAMING_SNAKE_CASE					)					)}" for name in self.attributes]
								_lowerCAmelCase			:			List[str]        =					"""\n""".join(__SCREAMING_SNAKE_CASE					)
								return f"{self.__class__.__name__}:\n{attributes_repr}"



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						Optional[int]						,		_UpperCAmelCase					:						bool = False						,		**_UpperCAmelCase					:						Any					)     ->			Optional[Any]:





								'''simple docstring'''




								os.makedirs(__SCREAMING_SNAKE_CASE						,		exist_ok=__SCREAMING_SNAKE_CASE					)

								if push_to_hub:
												_lowerCAmelCase			:			List[Any]        =					kwargs.pop("""commit_message"""						,		__SCREAMING_SNAKE_CASE					)
												_lowerCAmelCase			:			Dict        =					kwargs.pop("""repo_id"""						,		save_directory.split(os.path.sep					)[-1]					)
												_lowerCAmelCase			:			Union[str, Any]        =					self._create_repo(__SCREAMING_SNAKE_CASE						,		**__SCREAMING_SNAKE_CASE					)
												_lowerCAmelCase			:			Any        =					self._get_files_timestamps(__SCREAMING_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:
												_lowerCAmelCase			:			Optional[int]        =					[getattr(self						,		__SCREAMING_SNAKE_CASE					) for attribute_name in self.attributes]
												_lowerCAmelCase			:			List[Any]        =					[(a.init_kwargs if isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					) else a) for a in attrs]
												custom_object_save(self						,		__SCREAMING_SNAKE_CASE						,		config=__SCREAMING_SNAKE_CASE					)

								for attribute_name in self.attributes:
												_lowerCAmelCase			:			Optional[Any]        =					getattr(self						,		__SCREAMING_SNAKE_CASE					)
												# Include the processor class in the attribute config so this processor can then be reloaded with the
												# `AutoProcessor` API.
												if hasattr(__SCREAMING_SNAKE_CASE						,		"""_set_processor_class"""					):
																attribute._set_processor_class(self.__class__.__name__					)
												attribute.save_pretrained(__SCREAMING_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:
																_lowerCAmelCase			:			Tuple        =					getattr(self						,		__SCREAMING_SNAKE_CASE					)
																if isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					):
																				del attribute.init_kwargs["auto_map"]

								if push_to_hub:
												self._upload_modified_files(
												    __SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		commit_message=__SCREAMING_SNAKE_CASE						,		token=kwargs.get("""use_auth_token"""					)						,		)



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





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					cache_dir
								_lowerCAmelCase			:			Any        =					force_download
								_lowerCAmelCase			:			Optional[Any]        =					local_files_only
								_lowerCAmelCase			:			Optional[Any]        =					revision

								_lowerCAmelCase			:			str        =					kwargs.pop("""use_auth_token"""						,		__SCREAMING_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."""						,		__SCREAMING_SNAKE_CASE					)
												if token is not None:
																raise ValueError(
																    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""					)
												_lowerCAmelCase			:			int        =					use_auth_token

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

								_lowerCAmelCase			:			Any        =					cls._get_arguments_from_pretrained(__SCREAMING_SNAKE_CASE						,		**__SCREAMING_SNAKE_CASE					)
								return cls(*__SCREAMING_SNAKE_CASE					)



				@classmethod
				def 					SCREAMING_SNAKE_CASE				(					cls					:						List[str]						,		_UpperCAmelCase					:						Tuple="AutoProcessor"					)     ->			Optional[int]:





								'''simple docstring'''




								if not isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					):
												_lowerCAmelCase			:			Dict        =					auto_class.__name__

								import transformers.models.auto as auto_module

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

								_lowerCAmelCase			:			Optional[int]        =					auto_class



				@classmethod
				def 					SCREAMING_SNAKE_CASE				(					cls					:						Optional[Any]						,		_UpperCAmelCase					:						Tuple						,		**_UpperCAmelCase					:						List[str]					)     ->			Union[str, Any]:





								'''simple docstring'''




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

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



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					getattr(self						,		self.attributes[0]					)
								return getattr(__SCREAMING_SNAKE_CASE						,		"""model_input_names"""						,		__SCREAMING_SNAKE_CASE					)






_lowerCamelCase					:     Union[str, Any]					  =  copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
	_lowerCamelCase					:     List[str]					  =  ProcessorMixin.push_to_hub.__doc__.format(
	    object="processor", object_class="AutoProcessor", object_files="processor files"
	)




"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


if __name__ == "__main__":
	doctest.testmod()

	# initialises a new quantum backend
	__A     :    Dict    =		projectq.MainEngine()

	# Generate a list of 10 random numbers
	__A     :    Any    =		[get_random_number(quantum_engine) for _ in range(10)]

	# Flushes the quantum engine from memory
	quantum_engine.flush()

	print('Random numbers', random_numbers_list)





