
import warnings

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


_A   =			logging.get_logger(__name__)

_A   =			{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		"mvp"
     UpperCAmelCase__    :							Dict									=		["past_key_values"]
     UpperCAmelCase__    :							int									=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(self				:   Optional[Any]    ,   _A				:   Tuple=5_0_2_6_7    ,   _A				:   Optional[Any]=1_0_2_4    ,   _A				:   Tuple=1_2    ,   _A				:   List[Any]=4_0_9_6    ,   _A				:   str=1_6    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Tuple=4_0_9_6    ,   _A				:   Any=1_6    ,   _A				:   Dict=0.0    ,   _A				:   int=0.0    ,   _A				:   Dict="gelu"    ,   _A				:   Union[str, Any]=1_0_2_4    ,   _A				:   List[str]=0.1    ,   _A				:   Union[str, Any]=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.02    ,   _A				:   Union[str, Any]=0.0    ,   _A				:   Optional[Any]=False    ,   _A				:   int=True    ,   _A				:   Optional[int]=1    ,   _A				:   Optional[Any]=0    ,   _A				:   Tuple=2    ,   _A				:   Union[str, Any]=True    ,   _A				:   List[str]=2    ,   _A				:   int=2    ,   _A				:   Union[str, Any]=False    ,   _A				:   int=1_0_0    ,   _A				:   Any=8_0_0    ,   **_A				:   Tuple    ,   )						->						Optional[Any]:
         snake_case								= vocab_size
         snake_case								= max_position_embeddings
         snake_case								= d_model
         snake_case								= encoder_ffn_dim
         snake_case								= encoder_layers
         snake_case								= encoder_attention_heads
         snake_case								= decoder_ffn_dim
         snake_case								= decoder_layers
         snake_case								= decoder_attention_heads
         snake_case								= dropout
         snake_case								= attention_dropout
         snake_case								= activation_dropout
         snake_case								= activation_function
         snake_case								= init_std
         snake_case								= encoder_layerdrop
         snake_case								= decoder_layerdrop
         snake_case								= classifier_dropout
         snake_case								= use_cache
         snake_case								= encoder_layers
         snake_case								= scale_embedding  # scale factor will be sqrt(d_model) if True
         snake_case								= use_prompt
         snake_case								= prompt_length
         snake_case								= prompt_mid_dim

         super().__init__(
             pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   is_encoder_decoder=_A    ,   decoder_start_token_id=_A    ,   forced_eos_token_id=_A    ,   **_A    ,   )

         if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"    ,   _A       ):
             snake_case								= self.bos_token_id
             warnings.warn(
                 f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                 "The config can simply be saved and uploaded again to be fixed."       )



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_  ):
     UpperCAmelCase__    :							Dict									=		["image_processor", "tokenizer"]
     UpperCAmelCase__    :							Dict									=		"LayoutLMv2ImageProcessor"
     UpperCAmelCase__    :							Optional[Any]									=		("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")





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

         snake_case								= 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__(_A    ,   _A       )





     def __call__(self				:   int    ,   _A				:   List[str]    ,   _A				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   _A				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,   _A				:   Union[List[List[int]], List[List[List[int]]]] = None    ,   _A				:   Optional[Union[List[int], List[List[int]]]] = None    ,   _A				:   bool = True    ,   _A				:   Union[bool, str, PaddingStrategy] = False    ,   _A				:   Union[bool, str, TruncationStrategy] = None    ,   _A				:   Optional[int] = None    ,   _A				:   int = 0    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = True    ,   _A				:   Optional[Union[str, TensorType]] = None    ,   **_A				:   Dict    ,   )						->						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."       )

         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
         snake_case								= self.image_processor(images=_A    ,   return_tensors=_A       )

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

         snake_case								= 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=_A    ,   add_special_tokens=_A    ,   padding=_A    ,   truncation=_A    ,   max_length=_A    ,   stride=_A    ,   pad_to_multiple_of=_A    ,   return_token_type_ids=_A    ,   return_attention_mask=_A    ,   return_overflowing_tokens=_A    ,   return_special_tokens_mask=_A    ,   return_offsets_mapping=_A    ,   return_length=_A    ,   verbose=_A    ,   return_tensors=_A    ,   **_A    ,   )

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

         return encoded_inputs





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

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

         return images_with_overflow





     def 		UpperCAmelCase(self				:   Tuple    ,   *_A				:   int    ,   **_A				:   Dict       )						->						str:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   str    ,   *_A				:   List[Any]    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         return ["input_ids", "bbox", "attention_mask", "image"]





     @property
     def 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,   _A    ,   )
         return self.image_processor_class





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   _A    ,   )
         return self.image_processor

