




"""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 							lowercase   (     SCREAMING_SNAKE_CASE__):
				__lowerCAmelCase     :     str								= ["""image_processor""", """tokenizer"""]
				__lowerCAmelCase     :     Dict								= """LayoutLMv2ImageProcessor"""
				__lowerCAmelCase     :     str								= ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       Dict=None   ,  _lowerCamelCase :       Any=None   ,  **_lowerCamelCase :       Any							):



					"""simple docstring"""

					A_	:					Tuple						 =		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_	:					str						 =		kwargs.pop('''feature_extractor'''							)

					A_	:					str						 =		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 :       Optional[int]   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Any = None   ,  _lowerCamelCase :       Optional[Any] = None   ,  _lowerCamelCase :       Optional[Any] = None   ,  _lowerCamelCase :       Tuple = None   ,  _lowerCamelCase :       int = True   ,  _lowerCamelCase :       List[Any] = False   ,  _lowerCamelCase :       Any = False   ,  _lowerCamelCase :       int = None   ,  _lowerCamelCase :       List[str] = 0   ,  _lowerCamelCase :       Optional[int] = None   ,  _lowerCamelCase :       str = None   ,  _lowerCamelCase :       str = None   ,  _lowerCamelCase :       Tuple = False   ,  _lowerCamelCase :       Dict = False   ,  _lowerCamelCase :       Union[str, Any] = False   ,  _lowerCamelCase :       Optional[int] = False   ,  _lowerCamelCase :       Optional[Any] = True   ,  _lowerCamelCase :       Optional[int] = None   ,  **_lowerCamelCase :       Union[str, Any]   ,  ):



					"""simple docstring"""

					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_	:					Dict						 =		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_	:					List[Any]						 =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
						A_	:					Any						 =		features["words"]

					A_	:					str						 =		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_	:					int						 =		features.pop('''pixel_values'''							)
					if return_overflowing_tokens is True:
						A_	:					List[str]						 =		self.get_overflowing_images(_UpperCAmelCase   ,  encoded_inputs['''overflow_to_sample_mapping''']							)
					A_	:					Any						 =		images

					return encoded_inputs







				def        a_	(				self :       Optional[Any]   ,  _lowerCamelCase :       Union[str, Any]   ,  _lowerCamelCase :       Optional[int]							):



					"""simple docstring"""

					A_	:					Any						 =		[]
					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 :       Tuple   ,  *_lowerCamelCase :       str   ,  **_lowerCamelCase :       str							):



					"""simple docstring"""

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







				def        a_	(				self :       List[Any]   ,  *_lowerCamelCase :       List[Any]   ,  **_lowerCamelCase :       str							):



					"""simple docstring"""

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







				@property
				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







				@property
				def        a_	(				self :       str							):



					"""simple docstring"""

					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 :       List[Any]							):



					"""simple docstring"""

					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						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  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					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



