

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__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['image_processor', 'tokenizer']
			__lowerCamelCase       				=				'Pix2StructImageProcessor'
			__lowerCamelCase       				=				('T5Tokenizer', 'T5TokenizerFast')


			def __init__(			self					,					lowercase					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	False
							super().__init__(lowercase					,					lowercase				)


			def __call__(			self					,					lowercase=None					,					lowercase = None					,					lowercase = True					,					lowercase = False					,					lowercase = None					,					lowercase = None					,					lowercase = 2048					,					lowercase = 0					,					lowercase = None					,					lowercase = None					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = False					,					lowercase = True					,					lowercase = None					,					**lowercase					,					)     ->  BatchEncoding:
							'''simple docstring'''






							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=lowercase					,					add_special_tokens=lowercase					,					padding=lowercase					,					truncation=lowercase					,					max_length=lowercase					,					stride=lowercase					,					pad_to_multiple_of=lowercase					,					return_attention_mask=lowercase					,					return_overflowing_tokens=lowercase					,					return_special_tokens_mask=lowercase					,					return_offsets_mapping=lowercase					,					return_token_type_ids=lowercase					,					return_length=lowercase					,					verbose=lowercase					,					return_tensors=lowercase					,					**lowercase					,					)
											return text_encoding

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

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

											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(lowercase				)

							return encoding_image_processor


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Tuple:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			@property
			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.tokenizer.model_input_names
							A__						=	self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



