






"""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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["image_processor", "tokenizer"]
					UpperCAmelCase__								=				"Pix2StructImageProcessor"
					UpperCAmelCase__								=				("T5Tokenizer", "T5TokenizerFast")

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Any					)			->							str:
											__magic_name__:	Dict							=       False
											super().__init__(__snake_case     ,		__snake_case					)

					def __call__(			self  :      int     ,		__snake_case  :      List[str]=None     ,		__snake_case  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,		__snake_case  :      bool = True     ,		__snake_case  :      Union[bool, str, PaddingStrategy] = False     ,		__snake_case  :      Union[bool, str, TruncationStrategy] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = 2_0_4_8     ,		__snake_case  :      int = 0     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		**__snake_case  :      Dict     ,		)			->							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:
																	__magic_name__:	Optional[int]							=       self.tokenizer
																	__magic_name__:	Optional[int]							=       self.tokenizer(
																	    text=__snake_case     ,		add_special_tokens=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		max_length=__snake_case     ,		stride=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_attention_mask=__snake_case     ,		return_overflowing_tokens=__snake_case     ,		return_special_tokens_mask=__snake_case     ,		return_offsets_mapping=__snake_case     ,		return_token_type_ids=__snake_case     ,		return_length=__snake_case     ,		verbose=__snake_case     ,		return_tensors=__snake_case     ,		**__snake_case     ,		)
																	return text_encoding

											if not self.image_processor.is_vqa:
																	# add pixel_values
																	__magic_name__:	Optional[int]							=       self.image_processor(
																	    __snake_case     ,		return_tensors=__snake_case     ,		max_patches=__snake_case     ,		**__snake_case					)
											else:
																	# add pixel_values and bbox
																	__magic_name__:	Optional[int]							=       self.image_processor(
																	    __snake_case     ,		return_tensors=__snake_case     ,		max_patches=__snake_case     ,		header_text=__snake_case     ,		**__snake_case					)

											if text is not None and not self.image_processor.is_vqa:
																	__magic_name__:	Optional[int]							=       self.tokenizer(
																	    text=__snake_case     ,		add_special_tokens=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		max_length=__snake_case     ,		stride=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_attention_mask=__snake_case     ,		return_overflowing_tokens=__snake_case     ,		return_special_tokens_mask=__snake_case     ,		return_offsets_mapping=__snake_case     ,		return_token_type_ids=__snake_case     ,		return_length=__snake_case     ,		verbose=__snake_case     ,		return_tensors=__snake_case     ,		**__snake_case     ,		)

																	if "attention_mask" in text_encoding:
																							__magic_name__:	Optional[Any]							=       text_encoding.pop("""attention_mask"""					)
																	if "input_ids" in text_encoding:
																							__magic_name__:	Tuple							=       text_encoding.pop("""input_ids"""					)
											else:
																	__magic_name__:	int							=       None

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

											return encoding_image_processor

					def   lowerCamelCase__   (			self  :      List[Any]     ,		*__snake_case  :      Optional[Any]     ,		**__snake_case  :      Tuple					)			->							Optional[int]:
											return self.tokenizer.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		*__snake_case  :      Tuple     ,		**__snake_case  :      Any					)			->							List[str]:
											return self.tokenizer.decode(*__snake_case     ,		**__snake_case					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Dict							=       self.tokenizer.model_input_names
											__magic_name__:	List[Any]							=       self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)











"""simple docstring"""





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"efficientnet"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 6_0_0     ,		__snake_case  :      float = 2.0     ,		__snake_case  :      float = 3.1     ,		__snake_case  :      int = 8     ,		__snake_case  :      List[int] = [3, 3, 5, 3, 5, 5, 3]     ,		__snake_case  :      List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,		__snake_case  :      List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,		__snake_case  :      List[int] = []     ,		__snake_case  :      List[int] = [1, 2, 2, 2, 1, 2, 1]     ,		__snake_case  :      List[int] = [1, 2, 2, 3, 3, 4, 1]     ,		__snake_case  :      List[int] = [1, 6, 6, 6, 6, 6, 6]     ,		__snake_case  :      float = 0.25     ,		__snake_case  :      str = "swish"     ,		__snake_case  :      int = 2_5_6_0     ,		__snake_case  :      str = "mean"     ,		__snake_case  :      float = 0.02     ,		__snake_case  :      float = 0.001     ,		__snake_case  :      float = 0.99     ,		__snake_case  :      float = 0.5     ,		__snake_case  :      float = 0.2     ,		**__snake_case  :      List[Any]     ,		)			->							List[Any]:
											super().__init__(**__snake_case					)

											__magic_name__:	str							=       num_channels
											__magic_name__:	List[str]							=       image_size
											__magic_name__:	List[str]							=       width_coefficient
											__magic_name__:	Optional[Any]							=       depth_coefficient
											__magic_name__:	Tuple							=       depth_divisor
											__magic_name__:	Dict							=       kernel_sizes
											__magic_name__:	int							=       in_channels
											__magic_name__:	str							=       out_channels
											__magic_name__:	Dict							=       depthwise_padding
											__magic_name__:	Union[str, Any]							=       strides
											__magic_name__:	Dict							=       num_block_repeats
											__magic_name__:	Tuple							=       expand_ratios
											__magic_name__:	List[str]							=       squeeze_expansion_ratio
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Tuple							=       hidden_dim
											__magic_name__:	int							=       pooling_type
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[str]							=       batch_norm_eps
											__magic_name__:	str							=       batch_norm_momentum
											__magic_name__:	List[str]							=       dropout_rate
											__magic_name__:	Dict							=       drop_connect_rate
											__magic_name__:	Optional[Any]							=       sum(__snake_case					) * 4



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							float:
											return 1E-5



