"""simple docstring"""







import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)

UpperCamelCase_							:							int				 =		{
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							'vit-hybrid'

				def __init__(  self							:     Optional[int]							,     _lowercase							:     Dict=None							,     _lowercase							:     str=7_6_8							,     _lowercase							:     int=1_2							,     _lowercase							:     str=1_2							,     _lowercase							:     Union[str, Any]=3_0_7_2							,     _lowercase							:     Optional[Any]="gelu"							,     _lowercase							:     Tuple=0.0							,     _lowercase							:     Tuple=0.0							,     _lowercase							:     List[str]=0.02							,     _lowercase							:     Tuple=1e-12							,     _lowercase							:     Tuple=2_2_4							,     _lowercase							:     Tuple=1							,     _lowercase							:     List[Any]=3							,     _lowercase							:     Dict=[1, 1_0_2_4, 2_4, 2_4]							,     _lowercase							:     Any=True							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										if backbone_config is None:
																logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""  )
																UpperCAmelCase__       						=		{
																    """global_padding""": """same""",
																    """layer_type""": """bottleneck""",
																    """depths""": [3, 4, 9],
																    """out_features""": ["""stage3"""],
																    """embedding_dynamic_padding""": True,
																}

										if isinstance(_lowercase							,     _lowercase  ):
																if "model_type" in backbone_config:
																						UpperCAmelCase__       						=		CONFIG_MAPPING[backbone_config["""model_type"""]]
																else:
																						logger.info(
																						    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""  )
																						UpperCAmelCase__       						=		BitConfig
																UpperCAmelCase__       						=		backbone_config_class(**_lowercase  )

										UpperCAmelCase__       						=		backbone_featmap_shape
										UpperCAmelCase__       						=		backbone_config
										UpperCAmelCase__       						=		hidden_size
										UpperCAmelCase__       						=		num_hidden_layers
										UpperCAmelCase__       						=		num_attention_heads
										UpperCAmelCase__       						=		intermediate_size
										UpperCAmelCase__       						=		hidden_act
										UpperCAmelCase__       						=		hidden_dropout_prob
										UpperCAmelCase__       						=		attention_probs_dropout_prob
										UpperCAmelCase__       						=		initializer_range
										UpperCAmelCase__       						=		layer_norm_eps
										UpperCAmelCase__       						=		image_size
										UpperCAmelCase__       						=		patch_size
										UpperCAmelCase__       						=		num_channels
										UpperCAmelCase__       						=		qkv_bias

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		copy.deepcopy(self.__dict__  )
										UpperCAmelCase__       						=		self.backbone_config.to_dict()
										UpperCAmelCase__       						=		self.__class__.model_type
										return output


"""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


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)

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


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


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

				def __init__(  self							:     Any							,     *_lowercase							:     Optional[int]							,     **_lowercase							:     int  ):


										'''simple docstring'''
										# Sanitize args and kwargs
										for key in kwargs:
																if key not in self.attributes:
																						raise TypeError(f'Unexpected keyword argument {key}.'  )
										for arg, attribute_name in zip(_lowercase							,     self.attributes  ):
																if attribute_name in kwargs:
																						raise TypeError(f'Got multiple values for argument {attribute_name}.'  )
																else:
																						UpperCAmelCase__       						=		arg

										if len(_lowercase  ) != len(self.attributes  ):
																raise ValueError(
																    f'This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '
																    f'{len(_lowercase  )} 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__       						=		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__       						=		AUTO_TO_BASE_CLASS_MAPPING.get(_lowercase							,     _lowercase  )
																if isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		tuple(getattr(_lowercase							,     _lowercase  ) for n in class_name if n is not None  )
																else:
																						UpperCAmelCase__       						=		getattr(_lowercase							,     _lowercase  )

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

																setattr(self							,     _lowercase							,     _lowercase  )

				def __repr__(  self							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		[f'- {name}: {repr(getattr(self							,     _lowercase  )  )}' for name in self.attributes]
										UpperCAmelCase__       						=		"""\n""".join(_lowercase  )
										return f'{self.__class__.__name__}:\n{attributes_repr}'

				def 							A			(  self							:     Tuple							,     _lowercase							:     Dict							,     _lowercase							:     bool = False							,     **_lowercase							:     List[str]  ):


										'''simple docstring'''
										os.makedirs(_lowercase							,     exist_ok=_lowercase  )

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

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

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

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

				@classmethod
				def 							A			(  cls							:     Dict							,     _lowercase							:     Union[str, os.PathLike]							,     _lowercase							:     Optional[Union[str, os.PathLike]] = None							,     _lowercase							:     bool = False							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, bool]] = None							,     _lowercase							:     str = "main"							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		cache_dir
										UpperCAmelCase__       						=		force_download
										UpperCAmelCase__       						=		local_files_only
										UpperCAmelCase__       						=		revision

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

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

										UpperCAmelCase__       						=		cls._get_arguments_from_pretrained(_lowercase							,     **_lowercase  )
										return cls(*_lowercase  )

				@classmethod
				def 							A			(  cls							:     List[Any]							,     _lowercase							:     Dict="AutoProcessor"  ):


										'''simple docstring'''
										if not isinstance(_lowercase							,     _lowercase  ):
																UpperCAmelCase__       						=		auto_class.__name__

										import transformers.models.auto as auto_module

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

										UpperCAmelCase__       						=		auto_class

				@classmethod
				def 							A			(  cls							:     Any							,     _lowercase							:     Tuple							,     **_lowercase							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		[]
										for attribute_name in cls.attributes:
																UpperCAmelCase__       						=		getattr(cls							,     f'{attribute_name}_class'  )
																if isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		tuple(getattr(_lowercase							,     _lowercase  ) if n is not None else None for n in class_name  )
																						UpperCAmelCase__       						=		kwargs.get("""use_fast"""							,     _lowercase  )
																						if use_fast and classes[1] is not None:
																												UpperCAmelCase__       						=		classes[1]
																						else:
																												UpperCAmelCase__       						=		classes[0]
																else:
																						UpperCAmelCase__       						=		getattr(_lowercase							,     _lowercase  )

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

				@property
				def 							A			(  self							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		getattr(self							,     self.attributes[0]  )
										return getattr(_lowercase							,     """model_input_names"""							,     _lowercase  )
UpperCamelCase_							:							List[Any]				 =		copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
						UpperCamelCase_							:							Dict				 =		ProcessorMixin.push_to_hub.__doc__.format(
						    object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
						)

