





'''simple docstring'''


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 				__UpperCAmelCase	(							snake_case__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     ["""image_processor""", """tokenizer"""]
		__lowerCAmelCase								=     """LayoutLMv2ImageProcessor"""
		__lowerCAmelCase								=     ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Optional[int]=None   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   Union[str, Any]   ):
			A					=   None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
				    """ instead."""   ,   UpperCAmelCase_   ,   )
				A					=   kwargs.pop("""feature_extractor"""   )

			A					=   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__(UpperCAmelCase_   ,   UpperCAmelCase_   )







		def __call__(self    :   Tuple   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   _lowerCAmelCase    :   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   _lowerCAmelCase    :   Union[List[List[int]], List[List[List[int]]]] = None   ,   _lowerCAmelCase    :   Optional[Union[List[int], List[List[int]]]] = None   ,   _lowerCAmelCase    :   bool = True   ,   _lowerCAmelCase    :   Union[bool, str, PaddingStrategy] = False   ,   _lowerCAmelCase    :   Union[bool, str, TruncationStrategy] = False   ,   _lowerCAmelCase    :   Optional[int] = None   ,   _lowerCAmelCase    :   int = 0   ,   _lowerCAmelCase    :   Optional[int] = None   ,   _lowerCAmelCase    :   Optional[bool] = None   ,   _lowerCAmelCase    :   Optional[bool] = None   ,   _lowerCAmelCase    :   bool = False   ,   _lowerCAmelCase    :   bool = False   ,   _lowerCAmelCase    :   bool = False   ,   _lowerCAmelCase    :   bool = False   ,   _lowerCAmelCase    :   bool = True   ,   _lowerCAmelCase    :   Optional[Union[str, TensorType]] = None   ,   **_lowerCAmelCase    :   List[Any]   ,   ):



			if self.image_processor.apply_ocr and (boxes is not None):
				raise ValueError(
				    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

			if self.image_processor.apply_ocr and (word_labels is not None):
				raise ValueError(
				    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

			if return_overflowing_tokens is True and return_offsets_mapping is False:
				raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

			# first, apply the image processor
			A					=   self.image_processor(images=UpperCAmelCase_   ,   return_tensors=UpperCAmelCase_   )

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
				if isinstance(UpperCAmelCase_   ,   UpperCAmelCase_   ):
					A					=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
				A					=   features["""words"""]

			A					=   self.tokenizer(
			    text=text if text is not None else features["""words"""]   ,   text_pair=text_pair if text_pair is not None else None   ,   boxes=boxes if boxes is not None else features["""boxes"""]   ,   word_labels=UpperCAmelCase_   ,   add_special_tokens=UpperCAmelCase_   ,   padding=UpperCAmelCase_   ,   truncation=UpperCAmelCase_   ,   max_length=UpperCAmelCase_   ,   stride=UpperCAmelCase_   ,   pad_to_multiple_of=UpperCAmelCase_   ,   return_token_type_ids=UpperCAmelCase_   ,   return_attention_mask=UpperCAmelCase_   ,   return_overflowing_tokens=UpperCAmelCase_   ,   return_special_tokens_mask=UpperCAmelCase_   ,   return_offsets_mapping=UpperCAmelCase_   ,   return_length=UpperCAmelCase_   ,   verbose=UpperCAmelCase_   ,   return_tensors=UpperCAmelCase_   ,   **UpperCAmelCase_   ,   )

			# add pixel values
			A					=   features.pop("""pixel_values"""   )
			if return_overflowing_tokens is True:
				A					=   self.get_overflowing_images(UpperCAmelCase_   ,   encoded_inputs["""overflow_to_sample_mapping"""]   )
			A					=   images

			return encoded_inputs







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   int   ):
			A					=   []
			for sample_idx in overflow_to_sample_mapping:
				images_with_overflow.append(images[sample_idx]   )

			if len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
				raise ValueError(
				    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
				    F""" {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )

			return images_with_overflow







		def 							A  (self    :   str   ,   *_lowerCAmelCase    :   Tuple   ,   **_lowerCAmelCase    :   Dict   ):
			return self.tokenizer.batch_decode(*UpperCAmelCase_   ,   **UpperCAmelCase_   )







		def 							A  (self    :   List[str]   ,   *_lowerCAmelCase    :   int   ,   **_lowerCAmelCase    :   Dict   ):
			return self.tokenizer.decode(*UpperCAmelCase_   ,   **UpperCAmelCase_   )







		@property
		def 							A  (self    :   str   ):
			return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







		@property
		def 							A  (self    :   Optional[int]   ):
			warnings.warn(
			    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   UpperCAmelCase_   ,   )
			return self.image_processor_class







		@property
		def 							A  (self    :   Tuple   ):
			warnings.warn(
			    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   UpperCAmelCase_   ,   )
			return self.image_processor





"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



