



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       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : str       =						["image_processor", "tokenizer"]
   A__      : str       =						"LayoutLMv2ImageProcessor"
   A__      : str       =						("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")


   def __init__(    self:     str  ,lowerCamelCase_:     Optional[int]=None  ,lowerCamelCase_:     str=None  ,**lowerCamelCase_:     Dict							)	->      Optional[Any]:
       UpperCAmelCase_     :       str  		=					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_  ,)
           UpperCAmelCase_     :       Union[str, Any]  		=					kwargs.pop("""feature_extractor"""							)

       UpperCAmelCase_     :       Dict  		=					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:     Tuple  ,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] = 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_:     Optional[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."""							)

       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
       UpperCAmelCase_     :       Optional[int]  		=					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_							):
               UpperCAmelCase_     :       int  		=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCAmelCase_     :       str  		=					features["""words"""]

       UpperCAmelCase_     :       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=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
       UpperCAmelCase_     :       Optional[int]  		=					features.pop("""pixel_values"""							)
       if return_overflowing_tokens is True:
           UpperCAmelCase_     :       str  		=					self.get_overflowing_images(lowerCamelCase_  ,encoded_inputs["""overflow_to_sample_mapping"""]							)
       UpperCAmelCase_     :       Tuple  		=					images

       return encoded_inputs


   def       A__    (    self:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[int]							)	->      str:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_     :       List[Any]  		=					[]
       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       A__    (    self:     Optional[int]  ,*lowerCamelCase_:     Union[str, Any]  ,**lowerCamelCase_:     List[str]							)	->      Optional[Any]:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Tuple  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     int							)	->      str:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   @property
   def       A__    (    self:     str							)	->      Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


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

'''simple docstring'''





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


UpperCamelCase       :     Optional[Any]			=    logging.get_logger(__name__)

UpperCamelCase       :     Union[str, Any]			=    {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"tvlt"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Any=2_2_4							,							UpperCAmelCase_     :						Optional[Any]=2_0_4_8							,							UpperCAmelCase_     :						Dict=1_2_8							,							UpperCAmelCase_     :						str=[1_6, 1_6]							,							UpperCAmelCase_     :						int=[1_6, 1_6]							,							UpperCAmelCase_     :						int=3							,							UpperCAmelCase_     :						Optional[int]=1							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						List[Any]=7_6_8							,							UpperCAmelCase_     :						int=1_2							,							UpperCAmelCase_     :						List[Any]=1_2							,							UpperCAmelCase_     :						Optional[Any]=3_0_7_2							,							UpperCAmelCase_     :						List[str]="gelu"							,							UpperCAmelCase_     :						Tuple=0.0							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=1e-6							,							UpperCAmelCase_     :						Dict=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						str=5_1_2							,							UpperCAmelCase_     :						List[str]=8							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=0.75							,							UpperCAmelCase_     :						Optional[Any]=0.15							,							UpperCAmelCase_     :						Union[str, Any]="frame-level"							,							UpperCAmelCase_     :						List[str]=True							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Optional[int]="classification"							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)

         if audio_mask_type not in ("frame-level", "patch_level"):
              raise ValueError(
                  'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                  f"""got {audio_mask_type}""")

         a					:							str							= image_size
         a					:							List[Any]							= spectrogram_length
         a					:							Union[str, Any]							= frequency_length
         a					:							str							= image_patch_size
         a					:							Dict							= audio_patch_size
         a					:							Dict							= num_image_channels
         a					:							Tuple							= num_audio_channels
         a					:							Optional[int]							= num_frames

         a					:							str							= hidden_size
         a					:							List[Any]							= num_hidden_layers
         a					:							Optional[Any]							= num_attention_heads
         a					:							Any							= intermediate_size
         a					:							List[Any]							= hidden_act
         a					:							int							= hidden_dropout_prob
         a					:							Optional[Any]							= attention_probs_dropout_prob
         a					:							Optional[Any]							= initializer_range
         a					:							List[str]							= layer_norm_eps
         a					:							List[Any]							= qkv_bias
         a					:							Dict							= use_mean_pooling

         a					:							List[Any]							= decoder_num_attention_heads
         a					:							Any							= decoder_hidden_size
         a					:							int							= decoder_num_hidden_layers
         a					:							Tuple							= decoder_intermediate_size
         a					:							Tuple							= pixel_mask_ratio
         a					:							List[Any]							= audio_mask_ratio
         a					:							Optional[Any]							= audio_mask_type

         a					:							Any							= task_matching
         a					:							Optional[Any]							= task_mae
         a					:							List[str]							= loss_type

