import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A  :	Union[str, Any]         =							logging.get_logger(__name__)


def   a__			(		__UpperCamelCase					):




      if "resnet-50" in model_name:
            SCREAMING_SNAKE_CASE_    =	ResNetConfig.from_pretrained("microsoft/resnet-50"					)
      elif "resnet-101" in model_name:
            SCREAMING_SNAKE_CASE_    =	ResNetConfig.from_pretrained("microsoft/resnet-101"					)
      else:
            raise ValueError("Model name should include either resnet50 or resnet101"					)

      SCREAMING_SNAKE_CASE_    =	DetrConfig(use_timm_backbone=__UpperCamelCase  ,   backbone_config=__UpperCamelCase					)

      # set label attributes
      SCREAMING_SNAKE_CASE_    =	"panoptic" in model_name
      if is_panoptic:
            SCREAMING_SNAKE_CASE_    =	2_5_0
      else:
            SCREAMING_SNAKE_CASE_    =	9_1
            SCREAMING_SNAKE_CASE_    =	"huggingface/label-files"
            SCREAMING_SNAKE_CASE_    =	"coco-detection-id2label.json"
            SCREAMING_SNAKE_CASE_    =	json.load(open(hf_hub_download(__UpperCamelCase  ,   __UpperCamelCase  ,   repo_type="dataset"					)  ,   "r"					)					)
            SCREAMING_SNAKE_CASE_    =	{int(__UpperCamelCase					): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_    =	idalabel
            SCREAMING_SNAKE_CASE_    =	{v: k for k, v in idalabel.items()}

      return config, is_panoptic


def   a__			(		__UpperCamelCase					):
      SCREAMING_SNAKE_CASE_    =	[]

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")					)
      rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")					)
      rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")					)
      rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")					)
      rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")					)
      # stages
      for stage_idx in range(len(config.backbone_config.depths					)					):
            for layer_idx in range(config.backbone_config.depths[stage_idx]					):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                            )					)
                  # 3 convs
                  for i in range(3					):
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                            )					)
                        rename_keys.append(
                            (
                                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                            )					)
    # fmt: on

      for i in range(config.encoder_layers					):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (
                    F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
                    F'''encoder.layers.{i}.self_attn.out_proj.weight''',
                )					)
            rename_keys.append(
                (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')					)
            rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')					)
            rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')					)
            rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')					)
            rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')					)
            rename_keys.append(
                (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')					)
            rename_keys.append(
                (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')					)
            rename_keys.append(
                (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')					)
            rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')					)
            # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
            rename_keys.append(
                (
                    F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
                    F'''decoder.layers.{i}.self_attn.out_proj.weight''',
                )					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')					)
            rename_keys.append(
                (
                    F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
                    F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
                )					)
            rename_keys.append(
                (
                    F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
                    F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
                )					)
            rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')					)
            rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')					)
            rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')					)
            rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')					)
            rename_keys.append(
                (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')					)
            rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')					)

      # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
      rename_keys.extend(
          [
              ("input_proj.weight", "input_projection.weight"),
              ("input_proj.bias", "input_projection.bias"),
              ("query_embed.weight", "query_position_embeddings.weight"),
              ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
              ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
              ("class_embed.weight", "class_labels_classifier.weight"),
              ("class_embed.bias", "class_labels_classifier.bias"),
              ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
              ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
              ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
              ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
              ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
              ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
          ]					)

      return rename_keys


def   a__			(		__UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase					):
      SCREAMING_SNAKE_CASE_    =	state_dict.pop(__UpperCamelCase					)
      SCREAMING_SNAKE_CASE_    =	val


def   a__			(		__UpperCamelCase  ,   __UpperCamelCase=False					):
      SCREAMING_SNAKE_CASE_    =	""
      if is_panoptic:
            SCREAMING_SNAKE_CASE_    =	"detr."

      # first: transformer encoder
      for i in range(6					):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''					)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[:2_5_6, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[:2_5_6]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[-2_5_6:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6					):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''					)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[:2_5_6, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[:2_5_6]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias[-2_5_6:]
            # read in weights + bias of input projection layer of cross-attention
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(
                F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
            SCREAMING_SNAKE_CASE_    =	state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''					)
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            SCREAMING_SNAKE_CASE_    =	in_proj_weight_cross_attn[:2_5_6, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias_cross_attn[:2_5_6]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight_cross_attn[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias_cross_attn[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_    =	in_proj_weight_cross_attn[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_    =	in_proj_bias_cross_attn[-2_5_6:]


def   a__			(		):
      SCREAMING_SNAKE_CASE_    =	"http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_    =	Image.open(requests.get(__UpperCamelCase  ,   stream=__UpperCamelCase					).raw					)

      return im


@torch.no_grad()
def   a__			(		__UpperCamelCase  ,   __UpperCamelCase=None  ,   __UpperCamelCase=False					):
      SCREAMING_SNAKE_CASE_  ,			SCREAMING_SNAKE_CASE_    =	get_detr_config(__UpperCamelCase					)

      # load original model from torch hub
      SCREAMING_SNAKE_CASE_    =	{
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(F'''Converting model {model_name}...'''					)
      SCREAMING_SNAKE_CASE_    =	torch.hub.load("facebookresearch/detr"  ,   model_name_to_original_name[model_name]  ,   pretrained=__UpperCamelCase					).eval()
      SCREAMING_SNAKE_CASE_    =	detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__UpperCamelCase					):
            if is_panoptic:
                  SCREAMING_SNAKE_CASE_    =	"detr." + src
            rename_key(__UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase					)
      # query, key and value matrices need special treatment
      read_in_q_k_v(__UpperCamelCase  ,   is_panoptic=__UpperCamelCase					)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      SCREAMING_SNAKE_CASE_    =	"detr.model." if is_panoptic else "model."
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("detr"					)
                      and not key.startswith("class_labels_classifier"					)
                      and not key.startswith("bbox_predictor"					)
                  ):
                        SCREAMING_SNAKE_CASE_    =	state_dict.pop(__UpperCamelCase					)
                        SCREAMING_SNAKE_CASE_    =	val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        SCREAMING_SNAKE_CASE_    =	state_dict.pop(__UpperCamelCase					)
                        SCREAMING_SNAKE_CASE_    =	val
                  elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                        continue
                  else:
                        SCREAMING_SNAKE_CASE_    =	state_dict.pop(__UpperCamelCase					)
                        SCREAMING_SNAKE_CASE_    =	val
            else:
                  if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                        SCREAMING_SNAKE_CASE_    =	state_dict.pop(__UpperCamelCase					)
                        SCREAMING_SNAKE_CASE_    =	val

    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE_    =	DetrForSegmentation(__UpperCamelCase					) if is_panoptic else DetrForObjectDetection(__UpperCamelCase					)
      model.load_state_dict(__UpperCamelCase					)
      model.eval()

      # verify our conversion on an image
      SCREAMING_SNAKE_CASE_    =	"coco_panoptic" if is_panoptic else "coco_detection"
      SCREAMING_SNAKE_CASE_    =	DetrImageProcessor(format=__UpperCamelCase					)

      SCREAMING_SNAKE_CASE_    =	processor(images=prepare_img()  ,   return_tensors="pt"					)
      SCREAMING_SNAKE_CASE_    =	encoding["pixel_values"]

      SCREAMING_SNAKE_CASE_    =	detr(__UpperCamelCase					)
      SCREAMING_SNAKE_CASE_    =	model(__UpperCamelCase					)

      assert torch.allclose(outputs.logits  ,   original_outputs["pred_logits"]  ,   atol=1E-3					)
      assert torch.allclose(outputs.pred_boxes  ,   original_outputs["pred_boxes"]  ,   atol=1E-3					)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks  ,   original_outputs["pred_masks"]  ,   atol=1E-4					)
      print("Looks ok!"					)

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''					)
            Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
            model.save_pretrained(__UpperCamelCase					)
            processor.save_pretrained(__UpperCamelCase					)

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("Uploading PyTorch model and image processor to the hub..."					)
            model.push_to_hub(F'''nielsr/{model_name}'''					)
            processor.push_to_hub(F'''nielsr/{model_name}'''					)


if __name__ == "__main__":
      A  :	Any         =							argparse.ArgumentParser()

      parser.add_argument(
          "--model_name",
          default="detr-resnet-50",
          type=str,
          choices=["detr-resnet-50", "detr-resnet-101"],
          help="Name of the DETR model you\'d like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
      )
      parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
      A  :	List[Any]         =							parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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 					UpperCamelCase   (      __magic_name__	:				Any	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						model.config

		lowercase__	     =						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=128       ,       )
		lowercase__	     =						MBartConfig(
		    is_decoder=__magic_name__       ,       is_encoder_decoder=__magic_name__       ,       add_cross_attention=__magic_name__       ,       decoder_layers=original_config.decoder_layer       ,       max_position_embeddings=original_config.max_position_embeddings       ,       vocab_size=len(
		        model.decoder.tokenizer	)       ,       scale_embedding=__magic_name__       ,       add_final_layer_norm=__magic_name__       ,       )

		return encoder_config, decoder_config






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		if "encoder.model" in name:
				lowercase__	     =						name.replace("""encoder.model"""       ,       """encoder"""	)
		if "decoder.model" in name:
				lowercase__	     =						name.replace("""decoder.model"""       ,       """decoder"""	)
		if "patch_embed.proj" in name:
				lowercase__	     =						name.replace("""patch_embed.proj"""       ,       """embeddings.patch_embeddings.projection"""	)
		if "patch_embed.norm" in name:
				lowercase__	     =						name.replace("""patch_embed.norm"""       ,       """embeddings.norm"""	)
		if name.startswith("""encoder"""	):
				if "layers" in name:
						lowercase__	     =						"""encoder.""" + name
				if "attn.proj" in name:
						lowercase__	     =						name.replace("""attn.proj"""       ,       """attention.output.dense"""	)
				if "attn" in name and "mask" not in name:
						lowercase__	     =						name.replace("""attn"""       ,       """attention.self"""	)
				if "norm1" in name:
						lowercase__	     =						name.replace("""norm1"""       ,       """layernorm_before"""	)
				if "norm2" in name:
						lowercase__	     =						name.replace("""norm2"""       ,       """layernorm_after"""	)
				if "mlp.fc1" in name:
						lowercase__	     =						name.replace("""mlp.fc1"""       ,       """intermediate.dense"""	)
				if "mlp.fc2" in name:
						lowercase__	     =						name.replace("""mlp.fc2"""       ,       """output.dense"""	)

				if name == "encoder.norm.weight":
						lowercase__	     =						"""encoder.layernorm.weight"""
				if name == "encoder.norm.bias":
						lowercase__	     =						"""encoder.layernorm.bias"""

		return name






def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				str	)  ->    Optional[int]:
		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__	     =						orig_state_dict.pop(__magic_name__	)

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

						if "weight" in key:
								lowercase__	     =						val[:dim, :]
								lowercase__	     =						val[dim : dim * 2, :]
								lowercase__	     =						val[-dim:, :]
						else:
								lowercase__	     =						val[:dim]
								lowercase__	     =						val[dim : dim * 2]
								lowercase__	     =						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:
						lowercase__	     =						val

		return orig_state_dict






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				List[Any]=None       ,       __magic_name__	:				Dict=False	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						DonutModel.from_pretrained(__magic_name__	).eval()

		# load HuggingFace model
		lowercase__     ,      lowercase__	     =						get_configs(__magic_name__	)
		lowercase__	     =						DonutSwinModel(__magic_name__	)
		lowercase__	     =						MBartForCausalLM(__magic_name__	)
		lowercase__	     =						VisionEncoderDecoderModel(encoder=__magic_name__       ,       decoder=__magic_name__	)
		model.eval()

		lowercase__	     =						original_model.state_dict()
		lowercase__	     =						convert_state_dict(__magic_name__       ,       __magic_name__	)
		model.load_state_dict(__magic_name__	)

		# verify results on scanned document
		lowercase__	     =						load_dataset("""hf-internal-testing/example-documents"""	)
		lowercase__	     =						dataset["""test"""][0]["""image"""].convert("""RGB"""	)

		lowercase__	     =						XLMRobertaTokenizerFast.from_pretrained(__magic_name__       ,       from_slow=__magic_name__	)
		lowercase__	     =						DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis       ,       size=original_model.config.input_size[::-1]	)
		lowercase__	     =						DonutProcessor(__magic_name__       ,       __magic_name__	)
		lowercase__	     =						processor(__magic_name__       ,       return_tensors="""pt"""	).pixel_values

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

		lowercase__	     =						original_model.encoder.model.patch_embed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						model.encoder.embeddings(__magic_name__	)
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1E-3	)

		# verify encoder hidden states
		lowercase__	     =						original_model.encoder(__magic_name__	)
		lowercase__	     =						model.encoder(__magic_name__	).last_hidden_state
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1E-2	)

		# verify decoder hidden states
		lowercase__	     =						original_model(__magic_name__       ,       __magic_name__       ,       __magic_name__	).logits
		lowercase__	     =						model(__magic_name__       ,       decoder_input_ids=__magic_name__	).logits
		assert torch.allclose(__magic_name__       ,       __magic_name__       ,       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(__magic_name__	)
				processor.save_pretrained(__magic_name__	)

		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			:						Dict        =  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[int]        =  parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

