




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

UpperCAmelCase_		:				str									=     {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""deit"""

						def __init__(		self   :List[Any]    , __snake_case   :int=7_68    , __snake_case   :Tuple=12    , __snake_case   :str=12    , __snake_case   :List[str]=30_72    , __snake_case   :Optional[Any]="gelu"    , __snake_case   :Optional[Any]=0.0    , __snake_case   :Dict=0.0    , __snake_case   :Any=0.02    , __snake_case   :str=1E-12    , __snake_case   :str=2_24    , __snake_case   :List[str]=16    , __snake_case   :Dict=3    , __snake_case   :Dict=True    , __snake_case   :str=16    , **__snake_case   :Tuple    , ):
										'''simple docstring'''

										super().__init__(**__snake_case )

										__magic_name__	:	int								=hidden_size
										__magic_name__	:	int								=num_hidden_layers
										__magic_name__	:	Tuple								=num_attention_heads
										__magic_name__	:	Union[str, Any]								=intermediate_size
										__magic_name__	:	Union[str, Any]								=hidden_act
										__magic_name__	:	Optional[int]								=hidden_dropout_prob
										__magic_name__	:	Optional[Any]								=attention_probs_dropout_prob
										__magic_name__	:	List[Any]								=initializer_range
										__magic_name__	:	Tuple								=layer_norm_eps
										__magic_name__	:	List[Any]								=image_size
										__magic_name__	:	List[Any]								=patch_size
										__magic_name__	:	int								=num_channels
										__magic_name__	:	int								=qkv_bias
										__magic_name__	:	Union[str, Any]								=encoder_stride




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					version.parse("""1.11"""			)

						@property
						def     A__						(		self   :Any ):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ] )


						@property
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										return 1E-4










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

										if self._in_target_context_manager:
														return self.current_processor(*__snake_case    , **__snake_case )

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=args[1:]

										if audio is None and text is None:
														raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )

										if audio is not None:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case    , **__snake_case )


						@contextmanager
						def     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your audio inputs, or in a separate call.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




