


"""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


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

lowerCAmelCase__ :    Optional[int]				    =       {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}


class 				SCREAMING_SNAKE_CASE__    (		UpperCAmelCase__    ):
       """simple docstring"""


       a  :    int       ='layoutlmv3'

       def __init__(						self		,					snake_case__=50_265		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=512		,					snake_case__=2		,					snake_case__=0.02		,					snake_case__=1e-5		,					snake_case__=1		,					snake_case__=0		,					snake_case__=2		,					snake_case__=1_024		,					snake_case__=128		,					snake_case__=128		,					snake_case__=True		,					snake_case__=32		,					snake_case__=128		,					snake_case__=64		,					snake_case__=256		,					snake_case__=True		,					snake_case__=True		,					snake_case__=True		,					snake_case__=224		,					snake_case__=3		,					snake_case__=16		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(
             vocab_size=_SCREAMING_SNAKE_CASE		,					hidden_size=_SCREAMING_SNAKE_CASE		,					num_hidden_layers=_SCREAMING_SNAKE_CASE		,					num_attention_heads=_SCREAMING_SNAKE_CASE		,					intermediate_size=_SCREAMING_SNAKE_CASE		,					hidden_act=_SCREAMING_SNAKE_CASE		,					hidden_dropout_prob=_SCREAMING_SNAKE_CASE		,					attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE		,					max_position_embeddings=_SCREAMING_SNAKE_CASE		,					type_vocab_size=_SCREAMING_SNAKE_CASE		,					initializer_range=_SCREAMING_SNAKE_CASE		,					layer_norm_eps=_SCREAMING_SNAKE_CASE		,					pad_token_id=_SCREAMING_SNAKE_CASE		,					bos_token_id=_SCREAMING_SNAKE_CASE		,					eos_token_id=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		,					)
         lowerCAmelCase					:					List[str]								=							max_ad_position_embeddings
         lowerCAmelCase					:					List[Any]								=							coordinate_size
         lowerCAmelCase					:					List[Any]								=							shape_size
         lowerCAmelCase					:					Any								=							has_relative_attention_bias
         lowerCAmelCase					:					Optional[Any]								=							rel_pos_bins
         lowerCAmelCase					:					int								=							max_rel_pos
         lowerCAmelCase					:					int								=							has_spatial_attention_bias
         lowerCAmelCase					:					Optional[int]								=							rel_ad_pos_bins
         lowerCAmelCase					:					str								=							max_rel_ad_pos
         lowerCAmelCase					:					List[Any]								=							text_embed
         lowerCAmelCase					:					Tuple								=							visual_embed
         lowerCAmelCase					:					List[Any]								=							input_size
         lowerCAmelCase					:					Union[str, Any]								=							num_channels
         lowerCAmelCase					:					Dict								=							patch_size
         lowerCAmelCase					:					Dict								=							classifier_dropout


class 				SCREAMING_SNAKE_CASE__    (		UpperCAmelCase__    ):
       """simple docstring"""


       a  :    Optional[int]       =version.parse("1.12"    )

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         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 	lowercase__							(						self						):






         """simple docstring"""





         return 1e-5

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 12







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = -1		,					snake_case__ = -1		,					snake_case__ = False		,					snake_case__ = None		,					snake_case__ = 3		,					snake_case__ = 40		,					snake_case__ = 40		,					):






         """simple docstring"""





         setattr(processor.image_processor		,					"apply_ocr"		,					_SCREAMING_SNAKE_CASE						)

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

         # Generate dummy bounding boxes
         lowerCAmelCase					:					Optional[Any]								=							[[[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					:					Tuple								=							self._generate_dummy_images(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE						)

         lowerCAmelCase					:					Tuple								=							dict(
             processor(
                 _SCREAMING_SNAKE_CASE		,					text=_SCREAMING_SNAKE_CASE		,					boxes=_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					)						)

         return inputs









"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     lowerCAmelCase__				    =       argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
     def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''simple docstring'''







       if string == "True":
         return True
       elif string == "False":
         return False
       else:
         raise ValueError(f"""could not parse string as bool {string}"""    )

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





