






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


UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class        lowerCAmelCase_				(      _lowercase						,      _lowercase	):







				"""simple docstring"""




				_snake_case				:   Any        =			'''bit'''
				_snake_case				:   List[Any]        =			['''preactivation''', '''bottleneck''']
				_snake_case				:   Tuple        =			['''SAME''', '''VALID''']







				def __init__( self     :Union[str, Any]  ,     lowerCamelCase__     :Tuple=3  ,     lowerCamelCase__     :List[Any]=64  ,     lowerCamelCase__     :Optional[int]=[2_56, 5_12, 10_24, 20_48]  ,     lowerCamelCase__     :List[Any]=[3, 4, 6, 3]  ,     lowerCamelCase__     :Optional[Any]="preactivation"  ,     lowerCamelCase__     :Optional[int]="relu"  ,     lowerCamelCase__     :Optional[Any]=None  ,     lowerCamelCase__     :Any=32  ,     lowerCamelCase__     :Union[str, Any]=0.0  ,     lowerCamelCase__     :Optional[int]=False  ,     lowerCamelCase__     :Tuple=32  ,     lowerCamelCase__     :Dict=1  ,     lowerCamelCase__     :str=None  ,     lowerCamelCase__     :List[str]=None  ,     **lowerCamelCase__     :Dict  ,     ):
					super().__init__(**A_     )
					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:
							UpperCamelCase__ :str						=      global_padding.upper()
						else:
							raise ValueError(f"""Padding strategy {global_padding} not supported"""     )
					UpperCamelCase__ :Tuple						=      num_channels
					UpperCamelCase__ :List[Any]						=      embedding_size
					UpperCamelCase__ :List[Any]						=      hidden_sizes
					UpperCamelCase__ :Tuple						=      depths
					UpperCamelCase__ :List[Any]						=      layer_type
					UpperCamelCase__ :str						=      hidden_act
					UpperCamelCase__ :Optional[int]						=      global_padding
					UpperCamelCase__ :Union[str, Any]						=      num_groups
					UpperCamelCase__ :Optional[int]						=      drop_path_rate
					UpperCamelCase__ :List[Any]						=      embedding_dynamic_padding
					UpperCamelCase__ :Union[str, Any]						=      output_stride
					UpperCamelCase__ :Dict						=      width_factor

					UpperCamelCase__ :str						=      ["""stem"""] + [f"""stage{idx}""" for idx in range(1  ,     len(A_     ) + 1     )]
					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      get_aligned_output_features_output_indices(
					    out_features=A_  ,     out_indices=A_  ,     stage_names=self.stage_names     )

"""simple docstring"""


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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							if images is None and text is None:
								raise ValueError('You have to specify either images or text.'   )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

							if text_encoding is not None:
								encoding_image_processor.update(A_   )

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )