





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

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

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "layoutlmv3"




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=128      ,  _lowerCAmelCase=64      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(
         vocab_size=_lowerCAmelCase      ,  hidden_size=_lowerCAmelCase      ,  num_hidden_layers=_lowerCAmelCase      ,  num_attention_heads=_lowerCAmelCase      ,  intermediate_size=_lowerCAmelCase      ,  hidden_act=_lowerCAmelCase      ,  hidden_dropout_prob=_lowerCAmelCase      ,  attention_probs_dropout_prob=_lowerCAmelCase      ,  max_position_embeddings=_lowerCAmelCase      ,  type_vocab_size=_lowerCAmelCase      ,  initializer_range=_lowerCAmelCase      ,  layer_norm_eps=_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   max_ad_position_embeddings
     _lowerCAmelCase						      =   coordinate_size
     _lowerCAmelCase						      =   shape_size
     _lowerCAmelCase						      =   has_relative_attention_bias
     _lowerCAmelCase						      =   rel_pos_bins
     _lowerCAmelCase						      =   max_rel_pos
     _lowerCAmelCase						      =   has_spatial_attention_bias
     _lowerCAmelCase						      =   rel_ad_pos_bins
     _lowerCAmelCase						      =   max_rel_ad_pos
     _lowerCAmelCase						      =   text_embed
     _lowerCAmelCase						      =   visual_embed
     _lowerCAmelCase						      =   input_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   classifier_dropout







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       version.parse("1.12"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     # The order of inputs is different for question answering and sequence classification
     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 				_snake_case (						self		)     ->				float:
     return 1E-5




 @property
 def 				_snake_case (						self		)     ->				int:
     return 12





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = 3      ,  _lowerCAmelCase = 40      ,  _lowerCAmelCase = 40      ,  )     ->				Mapping[str, Any]:
     setattr(processor.image_processor      ,  "apply_ocr"      ,  _lowerCAmelCase		)

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  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
     _lowerCAmelCase						      =   processor.tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
     _lowerCAmelCase						      =   compute_effective_axis_dimension(
         _lowerCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_sequence      ,  num_token_to_add=_lowerCAmelCase		)
     # Generate dummy inputs according to compute batch and sequence
     _lowerCAmelCase						      =   [[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     _lowerCAmelCase						      =   [[[48, 84, 73, 128]]] * 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)
     _lowerCAmelCase						      =   self._generate_dummy_images(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   dict(
         processor(
             _lowerCAmelCase      ,  text=_lowerCAmelCase      ,  boxes=_lowerCAmelCase      ,  return_tensors=_lowerCAmelCase      ,  )		)

     return inputs









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


