



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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: List[str] =  ["image_processor", "tokenizer"]
 a__					: Optional[Any] =  "LayoutLMv3ImageProcessor"
 a__					: Union[str, Any] =  ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

 def __init__(    self					:					str			,						__lowerCAmelCase					:					Any=None			,						__lowerCAmelCase					:					Any=None			,						**__lowerCAmelCase					:					Optional[int]      )   ->	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.'''			,						__lowerCAmelCase			,						)
       _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__(__lowerCAmelCase			,						__lowerCAmelCase      )

 def __call__(    self					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__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] = None			,						__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					:					int			,						)   ->	BatchEncoding:
    # verify input
    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.'''      )

    # first, apply the image processor
    _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=__lowerCAmelCase      )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _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=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase			,						padding=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						max_length=__lowerCAmelCase			,						stride=__lowerCAmelCase			,						pad_to_multiple_of=__lowerCAmelCase			,						return_token_type_ids=__lowerCAmelCase			,						return_attention_mask=__lowerCAmelCase			,						return_overflowing_tokens=__lowerCAmelCase			,						return_special_tokens_mask=__lowerCAmelCase			,						return_offsets_mapping=__lowerCAmelCase			,						return_length=__lowerCAmelCase			,						verbose=__lowerCAmelCase			,						return_tensors=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

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

    return encoded_inputs

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	int:
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    _A      =       []
    for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]      )

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

    return images_with_overflow

 def     snake_case_      (    self					:					List[Any]			,						*__lowerCAmelCase					:					str			,						**__lowerCAmelCase					:					str      )   ->	Optional[int]:
    return self.tokenizer.batch_decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]			,						*__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	str:
    return self.tokenizer.decode(*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    return ["input_ids", "bbox", "attention_mask", "pixel_values"]

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[str]:
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,						__lowerCAmelCase			,						)
    return self.image_processor_class

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	Tuple:
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,						__lowerCAmelCase			,						)
    return self.image_processor






'''simple docstring'''



from typing import Optional, Union

import torch
from torch import nn

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







class 		lowerCAmelCase__  ( a					,   a   ):



 """simple docstring"""



 @register_to_config
 def __init__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			int = 768   ,				)    ->					int:


      """simple docstring"""




      super().__init__()

      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.zeros(1   ,				__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.ones(1   ,				__SCREAMING_SNAKE_CASE     )     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, torch.device]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[torch.dtype] = None   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.mean.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.std.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      return self
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds - self.mean) * 1.0 / self.std
      return embeds


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds * self.std) + self.mean
      return embeds






