





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__(			UpperCamelCase__     :			Union[str, Any]    )->int:
       A__		   =		model.config

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

       return encoder_config, decoder_config


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

              if name == "encoder.norm.weight":
                     A__		   =		'''encoder.layernorm.weight'''
              if name == "encoder.norm.bias":
                     A__		   =		'''encoder.layernorm.bias'''

       return name


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Optional[Any]    )->List[Any]:
       for key in orig_state_dict.copy().keys():
              A__		   =		orig_state_dict.pop(__lowerCAmelCase    )

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

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

       return orig_state_dict


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Tuple=None					,	UpperCamelCase__     :			Union[str, Any]=False    )->Tuple:
       # load original model
       A__		   =		DonutModel.from_pretrained(__lowerCAmelCase    ).eval()

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

       A__		   =		original_model.state_dict()
       A__		   =		convert_state_dict(__lowerCAmelCase					,	__lowerCAmelCase    )
       model.load_state_dict(__lowerCAmelCase    )

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

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

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

       A__		   =		original_model.encoder.model.patch_embed(__lowerCAmelCase    )
       A__   ,      A__		   =		model.encoder.embeddings(__lowerCAmelCase    )
       assert torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1e-3    )

       # verify encoder hidden states
       A__		   =		original_model.encoder(__lowerCAmelCase    )
       A__		   =		model.encoder(__lowerCAmelCase    ).last_hidden_state
       assert torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1e-2    )

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

       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__: Union[str, 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__: int							   =						parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[int]      ,    __lowerCAmelCase       :							List[Any]=False		)		->  List[str]:
   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("""cls_token""", """vit.embeddings.cls_token"""),
           ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
           ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
           ("""pos_embed""", """vit.embeddings.position_embeddings"""),
       ]		)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
              ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
              ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
          ]		)

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      snake_case 						=   [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""		) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""norm.weight""", """vit.layernorm.weight"""),
              ("""norm.bias""", """vit.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int=False		)		->  Union[str, Any]:
   for i in range(config.num_hidden_layers		):
      if base_model:
         snake_case 						=   """"""
      else:
         snake_case 						=   """vit."""
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   in_proj_bias[: config.hidden_size]
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   in_proj_bias[-config.hidden_size :]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  List[str]:
   snake_case 						=   ["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Tuple		)		->  int:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Any		)		->  Dict:
   snake_case 						=   ViTConfig()
   snake_case 						=   False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      snake_case 						=   True
      snake_case 						=   int(vit_name[-12:-10]		)
      snake_case 						=   int(vit_name[-9:-6]		)
   else:
      snake_case 						=   10_00
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """imagenet-1k-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}
      snake_case 						=   int(vit_name[-6:-4]		)
      snake_case 						=   int(vit_name[-3:]		)
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith("""tiny"""		):
         snake_case 						=   1_92
         snake_case 						=   7_68
         snake_case 						=   12
         snake_case 						=   3
      elif vit_name[9:].startswith("""small"""		):
         snake_case 						=   3_84
         snake_case 						=   15_36
         snake_case 						=   12
         snake_case 						=   6
      else:
         pass
   else:
      if vit_name[4:].startswith("""small"""		):
         snake_case 						=   7_68
         snake_case 						=   23_04
         snake_case 						=   8
         snake_case 						=   8
      elif vit_name[4:].startswith("""base"""		):
         pass
      elif vit_name[4:].startswith("""large"""		):
         snake_case 						=   10_24
         snake_case 						=   40_96
         snake_case 						=   24
         snake_case 						=   16
      elif vit_name[4:].startswith("""huge"""		):
         snake_case 						=   12_80
         snake_case 						=   51_20
         snake_case 						=   32
         snake_case 						=   16

    # load original model from timm
   snake_case 						=   timm.create_model(__lowerCAmelCase      ,    pretrained=__lowerCAmelCase		)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   timm_model.state_dict()
   if base_model:
      remove_classification_head_(__lowerCAmelCase		)
   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    __lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      snake_case 						=   ViTModel(__lowerCAmelCase		).eval()
   else:
      snake_case 						=   ViTForImageClassification(__lowerCAmelCase		).eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      snake_case 						=   DeiTImageProcessor(size=config.image_size		)
   else:
      snake_case 						=   ViTImageProcessor(size=config.image_size		)
   snake_case 						=   image_processor(images=prepare_img()      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]
   snake_case 						=   model(__lowerCAmelCase		)

   if base_model:
      snake_case 						=   timm_model.forward_features(__lowerCAmelCase		)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.pooler_output      ,    atol=1e-3		)
   else:
      snake_case 						=   timm_model(__lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.logits      ,    atol=1e-3		)

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--vit_name",
     default="vit_base_patch16_224",
     type=str,
     help="Name of the ViT timm model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

