


"""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 _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase :       Any							  =  '''LayoutLMv3ImageProcessor'''
   __UpperCAmelCase :       List[str]							  =  ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







   def __init__(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Dict      					=    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__						,	)
           snake_case    :      Optional[int]      					=    kwargs.pop("feature_extractor"						)

       snake_case    :      Any      					=    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						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = True						,	UpperCamelCase__ = False						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 0						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = False						,	UpperCamelCase__ = True						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		BatchEncoding:

       '''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."						)

       # first, apply the image processor
       snake_case    :      Optional[int]      					=    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__						):
               snake_case    :      str      					=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case    :      Optional[Any]      					=    features["words"]

       snake_case    :      Dict      					=    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
       snake_case    :      Dict      					=    features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case    :      Optional[Any]      					=    self.get_overflowing_images(UpperCamelCase__						,	encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case    :      Any      					=    images

       return encoded_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      List[str]      					=    []
       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  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




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







   @property
   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




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







   @property
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''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  lowerCamelCase		(	self						)	->		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"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  '''xmod'''







   def __init__(	self						,	UpperCamelCase__=3_0522						,	UpperCamelCase__=768						,	UpperCamelCase__=12						,	UpperCamelCase__=12						,	UpperCamelCase__=3072						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=1e-12						,	UpperCamelCase__=1						,	UpperCamelCase__=0						,	UpperCamelCase__=2						,	UpperCamelCase__="absolute"						,	UpperCamelCase__=True						,	UpperCamelCase__=None						,	UpperCamelCase__=False						,	UpperCamelCase__=2						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=("en_XX",)						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		int:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,	bos_token_id=UpperCamelCase__						,	eos_token_id=UpperCamelCase__						,	**UpperCamelCase__						)

       snake_case    :      List[Any]      					=    vocab_size
       snake_case    :      List[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Any      					=    num_attention_heads
       snake_case    :      List[str]      					=    hidden_act
       snake_case    :      Union[str, Any]      					=    intermediate_size
       snake_case    :      int      					=    hidden_dropout_prob
       snake_case    :      Any      					=    attention_probs_dropout_prob
       snake_case    :      Optional[int]      					=    max_position_embeddings
       snake_case    :      Tuple      					=    type_vocab_size
       snake_case    :      List[str]      					=    initializer_range
       snake_case    :      int      					=    layer_norm_eps
       snake_case    :      Optional[Any]      					=    position_embedding_type
       snake_case    :      int      					=    use_cache
       snake_case    :      Dict      					=    classifier_dropout
       snake_case    :      Dict      					=    pre_norm
       snake_case    :      Union[str, Any]      					=    adapter_reduction_factor
       snake_case    :      Any      					=    adapter_layer_norm
       snake_case    :      Optional[int]      					=    adapter_reuse_layer_norm
       snake_case    :      List[Any]      					=    ln_before_adapter
       snake_case    :      str      					=    list(UpperCamelCase__						)
       snake_case    :      int      					=    default_language







class _lowerCAmelCase				(      snake_case_	):







   @property
   def  lowerCamelCase		(	self						)	->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case    :      Optional[int]      					=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case    :      Optional[Any]      					=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]						)





