
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


snake_case				:	Tuple							   =						logging.get_logger(__name__)

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


snake_case				:	Optional[int]							   =						{
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}





class    _snake_case      (  __UpperCAmelCase       ):
						UpperCamelCase__					   =					["""feature_extractor""", """tokenizer"""]
						# Names need to be attr_class for attr in attributes
						UpperCamelCase__					   =					None
						UpperCamelCase__					   =					None
						UpperCamelCase__					   =					None







						def __init__(       self		,     *_a		,     **_a	):



							for key in kwargs:
								if key not in self.attributes:
									raise TypeError(f'''Unexpected keyword argument {key}.'''	)
							for arg, attribute_name in zip(lowerCamelCase__		,     self.attributes	):
								if attribute_name in kwargs:
									raise TypeError(f'''Got multiple values for argument {attribute_name}.'''	)
								else:
									__magic_name__	:	Optional[Any]			     =  arg

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

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

								setattr(self		,     lowerCamelCase__		,     lowerCamelCase__	)







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







						def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = False		,     **_a	):
							os.makedirs(lowerCamelCase__		,     exist_ok=lowerCamelCase__	)

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

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

							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:
									__magic_name__	:	Optional[Any]			     =  getattr(self		,     lowerCamelCase__	)
									if isinstance(lowerCamelCase__		,     lowerCamelCase__	):
										del attribute.init_kwargs["auto_map"]

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







						@classmethod
						def     SCREAMING_SNAKE_CASE					(       cls		,     _a		,     _a = None		,     _a = False		,     _a = False		,     _a = None		,     _a = "main"		,     **_a		,     ):
							__magic_name__	:	int			     =  cache_dir
							__magic_name__	:	List[str]			     =  force_download
							__magic_name__	:	List[Any]			     =  local_files_only
							__magic_name__	:	Dict			     =  revision

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

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

							__magic_name__	:	Optional[Any]			     =  cls._get_arguments_from_pretrained(lowerCamelCase__		,     **lowerCamelCase__	)
							return cls(*lowerCamelCase__	)







						@classmethod
						def     SCREAMING_SNAKE_CASE					(       cls		,     _a="AutoProcessor"	):



							if not isinstance(lowerCamelCase__		,     lowerCamelCase__	):
								__magic_name__	:	int			     =  auto_class.__name__

							import transformers.models.auto as auto_module

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

							__magic_name__	:	Union[str, Any]			     =  auto_class







						@classmethod
						def     SCREAMING_SNAKE_CASE					(       cls		,     _a		,     **_a	):
							__magic_name__	:	Union[str, Any]			     =  []
							for attribute_name in cls.attributes:
								__magic_name__	:	int			     =  getattr(cls		,     f'''{attribute_name}_class'''	)
								if isinstance(lowerCamelCase__		,     lowerCamelCase__	):
									__magic_name__	:	Tuple			     =  tuple(getattr(lowerCamelCase__		,     lowerCamelCase__	) if n is not None else None for n in class_name	)
									__magic_name__	:	Optional[Any]			     =  kwargs.get("use_fast"		,     lowerCamelCase__	)
									if use_fast and classes[1] is not None:
										__magic_name__	:	Union[str, Any]			     =  classes[1]
									else:
										__magic_name__	:	List[Any]			     =  classes[0]
								else:
									__magic_name__	:	str			     =  getattr(lowerCamelCase__		,     lowerCamelCase__	)

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







						@property
						def     SCREAMING_SNAKE_CASE					(       self	):
							__magic_name__	:	Dict			     =  getattr(self		,     self.attributes[0]	)
							return getattr(lowerCamelCase__		,     "model_input_names"		,     lowerCamelCase__	)







snake_case				:	Any							   =						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
				snake_case				:	Optional[int]							   =						ProcessorMixin.push_to_hub.__doc__.format(
				    object="processor", object_class="AutoProcessor", object_files="processor files"
				)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case				:	Optional[int]							   =						{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case				:	Optional[int]							   =						[
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
