






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 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		Any					='''LayoutLMv2ImageProcessor'''
       __lowerCAmelCase    :		Any					=('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')





       def __init__(				self	:Tuple,      snake_case	:Dict=None,      snake_case	:List[Any]=None,      **snake_case	:int):



          """simple docstring"""

          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.',      snake_case,      )
             _lowercase              =kwargs.pop('feature_extractor')

          _lowercase              =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__(snake_case,      snake_case)





       def __call__(				self	:Optional[int],      snake_case	:List[str],      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,      snake_case	:Union[List[List[int]], List[List[List[int]]]] = None,      snake_case	:Optional[Union[List[int], List[List[int]]]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:List[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
          _lowercase              =self.image_processor(images=snake_case,      return_tensors=snake_case)

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

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

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

          return encoded_inputs





       def 				UpperCamelCase__						(				self	:Tuple,      snake_case	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          _lowercase              =[]
          for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx])

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

          return images_with_overflow





       def 				UpperCamelCase__						(				self	:Union[str, Any],      *snake_case	:Dict,      **snake_case	:Union[str, Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Dict,      *snake_case	:Tuple,      **snake_case	:Dict):



          """simple docstring"""

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





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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





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



          """simple docstring"""

          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',      snake_case,      )
          return self.image_processor_class





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



          """simple docstring"""

          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',      snake_case,      )
          return self.image_processor










import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

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

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =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.')


