
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      ['image_processor', 'tokenizer']
	a     :Optional[int]       	=      'ViltImageProcessor'
	a     :int       	=      ('BertTokenizer', 'BertTokenizerFast')
	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			)			->	List[str]:
					lowercase_	    =   None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
									lowercase_	    =   kwargs.pop('''feature_extractor'''			)

					lowercase_	    =   image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError('''You need to specify an `image_processor`.'''			)
					if tokenizer is None:
									raise ValueError('''You need to specify a `tokenizer`.'''			)

					super().__init__(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.image_processor
	def __call__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[bool, str, PaddingStrategy] = False			,				SCREAMING_SNAKE_CASE_    :     Union[bool, str, TruncationStrategy] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     int = 0			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[bool] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[bool] = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	BatchEncoding:
					lowercase_	    =   self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_			,				add_special_tokens=SCREAMING_SNAKE_CASE_			,				padding=SCREAMING_SNAKE_CASE_			,				truncation=SCREAMING_SNAKE_CASE_			,				max_length=SCREAMING_SNAKE_CASE_			,				stride=SCREAMING_SNAKE_CASE_			,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_			,				return_token_type_ids=SCREAMING_SNAKE_CASE_			,				return_attention_mask=SCREAMING_SNAKE_CASE_			,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_			,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_			,				return_offsets_mapping=SCREAMING_SNAKE_CASE_			,				return_length=SCREAMING_SNAKE_CASE_			,				verbose=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)
					# add pixel_values + pixel_mask
					lowercase_	    =   self.image_processor(SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			)
					encoding.update(SCREAMING_SNAKE_CASE_			)

					return encoding
	def 		_lowercase    (			self    :     str			,				*SCREAMING_SNAKE_CASE_    :     int			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Union[str, Any]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[Any]			,				*SCREAMING_SNAKE_CASE_    :     Dict			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	str:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	@property
	def 		_lowercase    (			self    :     Any			)			->	Union[str, Any]:
					lowercase_	    =   self.tokenizer.model_input_names
					lowercase_	    =   self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)
	@property
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
					return self.image_processor_class




	@property
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
					return self.image_processor












'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class UpperCAmelCase_       (     __A					,							__A					):




      """simple docstring"""




      UpperCamelCase_             =      '''bit'''
      UpperCamelCase_             =      ['''preactivation''', '''bottleneck''']
      UpperCamelCase_             =      ['''SAME''', '''VALID''']






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Optional[int]=3  ,   UpperCAmelCase      :		Any=64  ,   UpperCAmelCase      :		List[str]=[256, 512, 1024, 2048]  ,   UpperCAmelCase      :		Dict=[3, 4, 6, 3]  ,   UpperCAmelCase      :		List[Any]="preactivation"  ,   UpperCAmelCase      :		Tuple="relu"  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Tuple=32  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Dict=32  ,   UpperCAmelCase      :		Any=1  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   **UpperCAmelCase      :		str  ,   )	->	Dict:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       if layer_type not in self.layer_types:
        raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
       if global_padding is not None:
        if global_padding.upper() in self.supported_padding:
         lowercase       : Any						=global_padding.upper()
        else:
         raise ValueError(f'Padding strategy {global_padding} not supported'		)
       lowercase       : List[Any]						=num_channels
       lowercase       : List[str]						=embedding_size
       lowercase       : Union[str, Any]						=hidden_sizes
       lowercase       : Union[str, Any]						=depths
       lowercase       : Optional[int]						=layer_type
       lowercase       : Optional[Any]						=hidden_act
       lowercase       : List[str]						=global_padding
       lowercase       : Dict						=num_groups
       lowercase       : Optional[Any]						=drop_path_rate
       lowercase       : Union[str, Any]						=embedding_dynamic_padding
       lowercase       : Any						=output_stride
       lowercase       : Union[str, Any]						=width_factor

       lowercase       : Union[str, Any]						=['''stem'''] + [f'stage{idx}' for idx in range(1  ,   len(UpperCAmelCase		) + 1		)]
       lowercase	,		lowercase       : List[str]						=get_aligned_output_features_output_indices(
           out_features=UpperCAmelCase  ,   out_indices=UpperCAmelCase  ,   stage_names=self.stage_names		)



