from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType


_lowercase				:   int   				=logging.get_logger(__name__)

_lowercase				:   str   				={
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}



class 			SCREAMING_SNAKE_CASE_			(       __snake_case      ):
  '''simple docstring'''


  lowercase		:  List[str]							  =					'layoutlmv3'







  def __init__(					self     :       Optional[int]   ,						SCREAMING_SNAKE_CASE__     :       str=5_02_65   ,						SCREAMING_SNAKE_CASE__     :       Tuple=7_68   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=12   ,						SCREAMING_SNAKE_CASE__     :       List[str]=12   ,						SCREAMING_SNAKE_CASE__     :       str=30_72   ,						SCREAMING_SNAKE_CASE__     :       Dict="gelu"   ,						SCREAMING_SNAKE_CASE__     :       Tuple=0.1   ,						SCREAMING_SNAKE_CASE__     :       Any=0.1   ,						SCREAMING_SNAKE_CASE__     :       Tuple=5_12   ,						SCREAMING_SNAKE_CASE__     :       Tuple=2   ,						SCREAMING_SNAKE_CASE__     :       Any=0.0_2   ,						SCREAMING_SNAKE_CASE__     :       str=1e-5   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=1   ,						SCREAMING_SNAKE_CASE__     :       int=0   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=2   ,						SCREAMING_SNAKE_CASE__     :       Dict=10_24   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=1_28   ,						SCREAMING_SNAKE_CASE__     :       List[str]=1_28   ,						SCREAMING_SNAKE_CASE__     :       int=True   ,						SCREAMING_SNAKE_CASE__     :       Tuple=32   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=1_28   ,						SCREAMING_SNAKE_CASE__     :       List[str]=64   ,						SCREAMING_SNAKE_CASE__     :       Dict=2_56   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=True   ,						SCREAMING_SNAKE_CASE__     :       str=True   ,						SCREAMING_SNAKE_CASE__     :       Any=True   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=2_24   ,						SCREAMING_SNAKE_CASE__     :       str=3   ,						SCREAMING_SNAKE_CASE__     :       Tuple=16   ,						SCREAMING_SNAKE_CASE__     :       Tuple=None   ,						**SCREAMING_SNAKE_CASE__     :       int   ,						)     ->					str:
         super().__init__(
             vocab_size=A_   ,						hidden_size=A_   ,						num_hidden_layers=A_   ,						num_attention_heads=A_   ,						intermediate_size=A_   ,						hidden_act=A_   ,						hidden_dropout_prob=A_   ,						attention_probs_dropout_prob=A_   ,						max_position_embeddings=A_   ,						type_vocab_size=A_   ,						initializer_range=A_   ,						layer_norm_eps=A_   ,						pad_token_id=A_   ,						bos_token_id=A_   ,						eos_token_id=A_   ,						**A_   ,						)
         A    :  Tuple								=max_ad_position_embeddings
         A    :  Any								=coordinate_size
         A    :  Union[str, Any]								=shape_size
         A    :  Optional[Any]								=has_relative_attention_bias
         A    :  str								=rel_pos_bins
         A    :  Dict								=max_rel_pos
         A    :  List[str]								=has_spatial_attention_bias
         A    :  Dict								=rel_ad_pos_bins
         A    :  List[Any]								=max_rel_ad_pos
         A    :  str								=text_embed
         A    :  List[str]								=visual_embed
         A    :  Dict								=input_size
         A    :  Optional[Any]								=num_channels
         A    :  str								=patch_size
         A    :  str								=classifier_dropout







class 			SCREAMING_SNAKE_CASE_			(       __snake_case      ):
  '''simple docstring'''


  lowercase		:  Tuple							  =					version.parse("1.12"      )







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




         if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ('input_ids', {0: 'batch', 1: 'sequence'}),
                        ('attention_mask', {0: 'batch', 1: 'sequence'}),
                        ('bbox', {0: 'batch', 1: 'sequence'}),
                        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                    ]  )
         else:
                return OrderedDict(
                    [
                        ('input_ids', {0: 'batch', 1: 'sequence'}),
                        ('bbox', {0: 'batch', 1: 'sequence'}),
                        ('attention_mask', {0: 'batch', 1: 'sequence'}),
                        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
                    ]  )







  @property
  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					str:
         return 1e-5







  @property
  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					Any:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]   ,						SCREAMING_SNAKE_CASE__     :       int = -1   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any] = -1   ,						SCREAMING_SNAKE_CASE__     :       Tuple = False   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any] = None   ,						SCREAMING_SNAKE_CASE__     :       Optional[int] = 3   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any] = 40   ,						SCREAMING_SNAKE_CASE__     :       Dict = 40   ,						)     ->					Dict:
         setattr(processor.image_processor   ,						'apply_ocr'   ,						A_  )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         A    :  Optional[Any]								=compute_effective_axis_dimension(
             A_   ,						fixed_dimension=OnnxConfig.default_fixed_batch   ,						num_token_to_add=0  )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         A    :  Optional[Any]								=processor.tokenizer.num_special_tokens_to_add(A_  )
         A    :  Union[str, Any]								=compute_effective_axis_dimension(
             A_   ,						fixed_dimension=OnnxConfig.default_fixed_sequence   ,						num_token_to_add=A_  )
         # Generate dummy inputs according to compute batch and sequence
         A    :  List[Any]								=[[" ".join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         A    :  Optional[Any]								=[[[48, 84, 73, 1_28]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         A    :  Any								=self._generate_dummy_images(A_   ,						A_   ,						A_   ,						A_  )

         A    :  Any								=dict(
             processor(
                 A_   ,						text=A_   ,						boxes=A_   ,						return_tensors=A_   ,						)  )

         return inputs













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase			:	List[Any]					    =	{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	Tuple					    =	["""ChineseCLIPFeatureExtractor"""]
    __lowerCamelCase			:	List[str]					    =	["""ChineseCLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase			:	str					    =	[
        """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ChineseCLIPModel""",
        """ChineseCLIPPreTrainedModel""",
        """ChineseCLIPTextModel""",
        """ChineseCLIPVisionModel""",
    ]

if TYPE_CHECKING:
    from .configuration_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ChineseCLIPConfig,
        ChineseCLIPOnnxConfig,
        ChineseCLIPTextConfig,
        ChineseCLIPVisionConfig,
    )
    from .processing_chinese_clip import ChineseCLIPProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_chinese_clip import (
            CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ChineseCLIPModel,
            ChineseCLIPPreTrainedModel,
            ChineseCLIPTextModel,
            ChineseCLIPVisionModel,
        )

else:
    import sys

    __lowerCamelCase			:	Any					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
