





import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Any					=     model.config

    UpperCamelCase__:	Optional[Any]					=     DonutSwinConfig(
        image_size=original_config.input_size    ,patch_size=4    ,depths=original_config.encoder_layer    ,num_heads=[4, 8, 16, 32]    ,window_size=original_config.window_size    ,embed_dim=1_28    ,)
    UpperCamelCase__:	str					=     MBartConfig(
        is_decoder=A_    ,is_encoder_decoder=A_    ,add_cross_attention=A_    ,decoder_layers=original_config.decoder_layer    ,max_position_embeddings=original_config.max_position_embeddings    ,vocab_size=len(
            model.decoder.tokenizer)    ,scale_embedding=A_    ,add_final_layer_norm=A_    ,)

    return encoder_config, decoder_config



def 				lowerCAmelCase_ (							A_):
    if "encoder.model" in name:
        UpperCamelCase__:	Tuple					=     name.replace("encoder.model"    ,"encoder")
    if "decoder.model" in name:
        UpperCamelCase__:	Tuple					=     name.replace("decoder.model"    ,"decoder")
    if "patch_embed.proj" in name:
        UpperCamelCase__:	List[Any]					=     name.replace("patch_embed.proj"    ,"embeddings.patch_embeddings.projection")
    if "patch_embed.norm" in name:
        UpperCamelCase__:	Optional[Any]					=     name.replace("patch_embed.norm"    ,"embeddings.norm")
    if name.startswith("encoder"):
        if "layers" in name:
            UpperCamelCase__:	Tuple					=     "encoder." + name
        if "attn.proj" in name:
            UpperCamelCase__:	Optional[Any]					=     name.replace("attn.proj"    ,"attention.output.dense")
        if "attn" in name and "mask" not in name:
            UpperCamelCase__:	Tuple					=     name.replace("attn"    ,"attention.self")
        if "norm1" in name:
            UpperCamelCase__:	Tuple					=     name.replace("norm1"    ,"layernorm_before")
        if "norm2" in name:
            UpperCamelCase__:	Tuple					=     name.replace("norm2"    ,"layernorm_after")
        if "mlp.fc1" in name:
            UpperCamelCase__:	Dict					=     name.replace("mlp.fc1"    ,"intermediate.dense")
        if "mlp.fc2" in name:
            UpperCamelCase__:	str					=     name.replace("mlp.fc2"    ,"output.dense")

        if name == "encoder.norm.weight":
            UpperCamelCase__:	Tuple					=     "encoder.layernorm.weight"
        if name == "encoder.norm.bias":
            UpperCamelCase__:	List[str]					=     "encoder.layernorm.bias"

    return name



def 				lowerCAmelCase_ (							A_    ,A_):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__:	List[str]					=     orig_state_dict.pop(A_)

        if "qkv" in key:
            UpperCamelCase__:	List[str]					=     key.split(".")
            UpperCamelCase__:	int					=     int(key_split[3])
            UpperCamelCase__:	List[str]					=     int(key_split[5])
            UpperCamelCase__:	Optional[int]					=     model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase__:	Optional[Any]					=     val[:dim, :]
                UpperCamelCase__:	Dict					=     val[dim : dim * 2, :]
                UpperCamelCase__:	Optional[Any]					=     val[-dim:, :]
            else:
                UpperCamelCase__:	Any					=     val[:dim]
                UpperCamelCase__:	Optional[int]					=     val[dim : dim * 2]
                UpperCamelCase__:	Optional[int]					=     val[-dim:]
        elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
            # HuggingFace implementation doesn't use attn_mask buffer
            # and model doesn't use final LayerNorms for the encoder
            pass
        else:
            UpperCamelCase__:	Union[str, Any]					=     val

    return orig_state_dict



def 				lowerCAmelCase_ (							A_    ,A_=None    ,A_=False):
    # load original model
    UpperCamelCase__:	List[Any]					=     DonutModel.from_pretrained(A_).eval()

    # load HuggingFace model
    UpperCamelCase__				, UpperCamelCase__:	int					=     get_configs(A_)
    UpperCamelCase__:	Dict					=     DonutSwinModel(A_)
    UpperCamelCase__:	Dict					=     MBartForCausalLM(A_)
    UpperCamelCase__:	Optional[Any]					=     VisionEncoderDecoderModel(encoder=A_    ,decoder=A_)
    model.eval()

    UpperCamelCase__:	List[str]					=     original_model.state_dict()
    UpperCamelCase__:	Optional[int]					=     convert_state_dict(A_    ,A_)
    model.load_state_dict(A_)

    # verify results on scanned document
    UpperCamelCase__:	List[Any]					=     load_dataset("hf-internal-testing/example-documents")
    UpperCamelCase__:	Union[str, Any]					=     dataset["test"][0]["image"].convert("RGB")

    UpperCamelCase__:	Dict					=     XLMRobertaTokenizerFast.from_pretrained(A_    ,from_slow=A_)
    UpperCamelCase__:	Union[str, Any]					=     DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis    ,size=original_model.config.input_size[::-1])
    UpperCamelCase__:	Any					=     DonutProcessor(A_    ,A_)
    UpperCamelCase__:	Any					=     processor(A_    ,return_tensors="pt").pixel_values

    if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
        UpperCamelCase__:	Dict					=     "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
        UpperCamelCase__:	str					=     "When is the coffee break?"
        UpperCamelCase__:	List[Any]					=     task_prompt.replace("{user_input}"    ,A_)
    elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
        UpperCamelCase__:	List[str]					=     "<s_rvlcdip>"
    elif model_name in [
        "naver-clova-ix/donut-base-finetuned-cord-v1",
        "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
    ]:
        UpperCamelCase__:	List[str]					=     "<s_cord>"
    elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
        UpperCamelCase__:	str					=     "s_cord-v2>"
    elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
        UpperCamelCase__:	List[str]					=     "<s_zhtrainticket>"
    elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
        # use a random prompt
        UpperCamelCase__:	Optional[Any]					=     "hello world"
    else:
        raise ValueError("Model name not supported")
    UpperCamelCase__:	int					=     original_model.decoder.tokenizer(A_    ,add_special_tokens=A_    ,return_tensors="pt")[
        "input_ids"
    ]

    UpperCamelCase__:	Optional[Any]					=     original_model.encoder.model.patch_embed(A_)
    UpperCamelCase__				, UpperCamelCase__:	Union[str, Any]					=     model.encoder.embeddings(A_)
    assert torch.allclose(A_    ,A_    ,atol=1e-3)

    # verify encoder hidden states
    UpperCamelCase__:	List[Any]					=     original_model.encoder(A_)
    UpperCamelCase__:	List[str]					=     model.encoder(A_).last_hidden_state
    assert torch.allclose(A_    ,A_    ,atol=1e-2)

    # verify decoder hidden states
    UpperCamelCase__:	Union[str, Any]					=     original_model(A_    ,A_    ,A_).logits
    UpperCamelCase__:	Optional[Any]					=     model(A_    ,decoder_input_ids=A_).logits
    assert torch.allclose(A_    ,A_    ,atol=1e-3)
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        print(F"Saving model and processor to {pytorch_dump_folder_path}")
        model.save_pretrained(A_)
        processor.save_pretrained(A_)

    if push_to_hub:
        model.push_to_hub("nielsr/" + model_name.split("/")[-1]    ,commit_message="Update model")
        processor.push_to_hub("nielsr/" + model_name.split("/")[-1]    ,commit_message="Update model")


if __name__ == "__main__":
   A__:    Optional[Any]  	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''naver-clova-ix/donut-base-finetuned-docvqa''',
       required=False,
       type=str,
       help='''Name of the original model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       required=False,
       type=str,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
       help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
   )

   A__:    Optional[Any]  	=	parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__:    str  	=	logging.get_logger(__name__)

A__:    Union[str, Any]  	=	{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """deformable_detr"""
      UpperCamelCase__  		=       {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }






      def __init__(		self:			Dict	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			List[str]=None	,    __lowerCamelCase:			Tuple=3	,    __lowerCamelCase:			Union[str, Any]=300	,    __lowerCamelCase:			Optional[Any]=1024	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[int]=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Any=6	,    __lowerCamelCase:			Tuple=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			List[str]="relu"	,    __lowerCamelCase:			Tuple=256	,    __lowerCamelCase:			Dict=0.1	,    __lowerCamelCase:			Optional[Any]=0.0	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Any=0.02	,    __lowerCamelCase:			int=1.0	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			str=False	,    __lowerCamelCase:			Any="sine"	,    __lowerCamelCase:			Optional[Any]="resnet50"	,    __lowerCamelCase:			Optional[int]=True	,    __lowerCamelCase:			int=False	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			Any=4	,    __lowerCamelCase:			Any=False	,    __lowerCamelCase:			List[str]=300	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			str=1	,    __lowerCamelCase:			int=5	,    __lowerCamelCase:			str=2	,    __lowerCamelCase:			Dict=1	,    __lowerCamelCase:			Tuple=1	,    __lowerCamelCase:			Union[str, Any]=5	,    __lowerCamelCase:			Optional[Any]=2	,    __lowerCamelCase:			List[str]=0.1	,    __lowerCamelCase:			Dict=0.25	,    __lowerCamelCase:			Dict=False	,    **__lowerCamelCase:			str	,    ):
          '''simple docstring'''


          if backbone_config is not None and use_timm_backbone:
              raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."					)

          if not use_timm_backbone:
              if backbone_config is None:
                  logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
                  UpperCamelCase__:	Union[str, Any]					=     CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
              elif isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                  UpperCamelCase__:	Tuple					=     backbone_config.get("model_type"					)
                  UpperCamelCase__:	Optional[int]					=     CONFIG_MAPPING[backbone_model_type]
                  UpperCamelCase__:	Tuple					=     config_class.from_dict(__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     use_timm_backbone
          UpperCamelCase__:	Any					=     backbone_config
          UpperCamelCase__:	Optional[int]					=     num_channels
          UpperCamelCase__:	int					=     num_queries
          UpperCamelCase__:	List[str]					=     max_position_embeddings
          UpperCamelCase__:	Dict					=     d_model
          UpperCamelCase__:	List[Any]					=     encoder_ffn_dim
          UpperCamelCase__:	Union[str, Any]					=     encoder_layers
          UpperCamelCase__:	Tuple					=     encoder_attention_heads
          UpperCamelCase__:	Tuple					=     decoder_ffn_dim
          UpperCamelCase__:	Optional[int]					=     decoder_layers
          UpperCamelCase__:	int					=     decoder_attention_heads
          UpperCamelCase__:	Optional[Any]					=     dropout
          UpperCamelCase__:	List[str]					=     attention_dropout
          UpperCamelCase__:	List[Any]					=     activation_dropout
          UpperCamelCase__:	List[Any]					=     activation_function
          UpperCamelCase__:	Union[str, Any]					=     init_std
          UpperCamelCase__:	List[str]					=     init_xavier_std
          UpperCamelCase__:	Optional[Any]					=     encoder_layerdrop
          UpperCamelCase__:	List[str]					=     auxiliary_loss
          UpperCamelCase__:	Optional[int]					=     position_embedding_type
          UpperCamelCase__:	Optional[Any]					=     backbone
          UpperCamelCase__:	Any					=     use_pretrained_backbone
          UpperCamelCase__:	Union[str, Any]					=     dilation
          # deformable attributes
          UpperCamelCase__:	Union[str, Any]					=     num_feature_levels
          UpperCamelCase__:	Optional[Any]					=     encoder_n_points
          UpperCamelCase__:	Tuple					=     decoder_n_points
          UpperCamelCase__:	Any					=     two_stage
          UpperCamelCase__:	Optional[int]					=     two_stage_num_proposals
          UpperCamelCase__:	str					=     with_box_refine
          if two_stage is True and with_box_refine is False:
              raise ValueError("If two_stage is True, with_box_refine must be True."					)
          # Hungarian matcher
          UpperCamelCase__:	Any					=     class_cost
          UpperCamelCase__:	str					=     bbox_cost
          UpperCamelCase__:	List[str]					=     giou_cost
          # Loss coefficients
          UpperCamelCase__:	Optional[Any]					=     mask_loss_coefficient
          UpperCamelCase__:	List[Any]					=     dice_loss_coefficient
          UpperCamelCase__:	Optional[Any]					=     bbox_loss_coefficient
          UpperCamelCase__:	List[Any]					=     giou_loss_coefficient
          UpperCamelCase__:	Dict					=     eos_coefficient
          UpperCamelCase__:	List[Any]					=     focal_alpha
          UpperCamelCase__:	Dict					=     disable_custom_kernels
          super().__init__(is_encoder_decoder=__lowerCamelCase	,    **__lowerCamelCase					)






      @property
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          return self.encoder_attention_heads






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return self.d_model






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     copy.deepcopy(self.__dict__					)
          if self.backbone_config is not None:
              UpperCamelCase__:	Tuple					=     self.backbone_config.to_dict()
          UpperCamelCase__:	Optional[int]					=     self.__class__.model_type
          return output

