

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_             =     logging.get_logger()





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 =len(list(m.modules()      )      ) == 1 or isinstance(lowerCAmelCase							,     nn.Convad      ) or isinstance(lowerCAmelCase							,     nn.BatchNormad      )
        if has_not_submodules:
              self.traced.append(lowerCAmelCase      )




  def __call__(       self							,     lowerCAmelCase      )						->				Dict:
        '''simple docstring'''






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook      )      )
        self.module(lowerCAmelCase      )
        [x.remove() for x in self.handles]
        return self




  @property
  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        return list(filter(lambda lowerCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0							,     self.traced      )      )





@dataclass
class 				__lowerCAmelCase  :
  _a		    =						42
  _a		    =						42
  _a		    =						1
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						field(default_factory=SCREAMING_SNAKE_CASE							)
  _a		    =						True




  def __call__(       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 =Tracker(self.dest      )(lowerCAmelCase      ).parametrized
        _lowercase			 =Tracker(self.src      )(lowerCAmelCase      ).parametrized

        _lowercase			 =list(filter(lambda lowerCAmelCase      : type(lowerCAmelCase      ) not in self.src_skip							,     lowerCAmelCase      )      )
        _lowercase			 =list(filter(lambda lowerCAmelCase      : type(lowerCAmelCase      ) not in self.dest_skip							,     lowerCAmelCase      )      )

        if len(lowerCAmelCase      ) != len(lowerCAmelCase      ) and self.raise_if_mismatch:
              raise Exception(
                  F'''Numbers of operations are different. Source module has {len(lowerCAmelCase      )} operations while'''
                  F''' destination module has {len(lowerCAmelCase      )}.'''      )

        for dest_m, src_m in zip(lowerCAmelCase							,     lowerCAmelCase      ):
              dest_m.load_state_dict(src_m.state_dict()      )
              if self.verbose == 1:
                    print(F'''Transfered from={src_m} to={dest_m}'''      )





class 				__lowerCAmelCase  (      nn.Module							):




  def __init__(       self							,     lowerCAmelCase      )						->				Dict:
        '''simple docstring'''






        super().__init__()

        _lowercase			 =[]
        # - get the stem
        feature_blocks.append(('conv1', model.stem)      )
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
              assert k.startswith('block'      ), F'''Unexpected layer name {k}'''
              _lowercase			 =len(lowerCAmelCase      ) + 1
              feature_blocks.append((F'''res{block_index}''', v)      )

        _lowercase			 =nn.ModuleDict(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        return get_trunk_forward_outputs(
            lowerCAmelCase							,     out_feat_keys=lowerCAmelCase							,     feature_blocks=self._feature_blocks							,     )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =x.split('-'      )
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )




  def __getitem__(       self							,     lowerCAmelCase      )						->				Callable[[], Tuple[nn.Module, Dict]]:
        '''simple docstring'''






        if x not in self:
              _lowercase			 =self.convert_name_to_timm(lowerCAmelCase      )
              _lowercase			 =partial(lambda: (timm.create_model(lowerCAmelCase							,     pretrained=lowerCAmelCase      ).eval(), None)      )

        else:
              _lowercase			 =super().__getitem__(lowerCAmelCase      )

        return val





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  def __getitem__(       self							,     lowerCAmelCase      )						->				Callable[[], nn.Module]:
        '''simple docstring'''






        if "seer" in x and "in1k" not in x:
              _lowercase			 =RegNetModel
        else:
              _lowercase			 =RegNetForImageClassification
        return val
def   a   (    A__			:     Union[str, Any]   ,     A__			:     Optional[int]   ,     A__			:     List[Tuple[str, str]]  )					->   Union[str, Any]:





      """simple docstring"""



      for from_key, to_key in keys:
            _lowercase			 =from_state_dict[from_key].clone()
            print(F'''Copied key={from_key} to={to_key}'''  )
      return to_state_dict
def   a   (    A__			:     str   ,     A__			:     Callable[[], nn.Module]   ,     A__			:     Callable[[], nn.Module]   ,     A__			:     RegNetConfig   ,     A__			:     Path   ,     A__			:     bool = True   ,     )					->   str:





      """simple docstring"""



      print(F'''Converting {name}...'''  )
      with torch.no_grad():
            _lowercase , _lowercase			 =from_model_func()
            _lowercase			 =our_model_func(A__  ).eval()
            _lowercase			 =ModuleTransfer(src=A__   ,     dest=A__   ,     raise_if_mismatch=A__  )
            _lowercase			 =torch.randn((1, 3, 224, 224)  )
            module_transfer(A__  )

      if from_state_dict is not None:
            _lowercase			 =[]
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  _lowercase			 =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
            _lowercase			 =manually_copy_vissl_head(A__   ,     our_model.state_dict()   ,     A__  )
            our_model.load_state_dict(A__  )

      _lowercase			 =our_model(A__   ,     output_hidden_states=A__  )
      _lowercase			 =(
          our_outputs.logits if isinstance(A__   ,     A__  ) else our_outputs.last_hidden_state
      )

      _lowercase			 =from_model(A__  )
      _lowercase			 =from_output[-1] if type(A__  ) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            _lowercase			 =our_outputs.hidden_states[-1]

      assert torch.allclose(A__   ,     A__  ), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name   ,     commit_message='Add model'   ,     use_temp_dir=A__   ,     )

            _lowercase			 =224 if 'seer' not in name else 384
            # we can use the convnext one
            _lowercase			 =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'   ,     size=A__  )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name   ,     commit_message='Add image processor'   ,     use_temp_dir=A__   ,     )

            print(F'''Pushed {name}'''  )
def   a   (    A__			:     Path   ,     A__			:     str = None   ,     A__			:     bool = True  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 ='imagenet-1k-id2label.json'
      _lowercase			 =1000
      _lowercase			 =(1, num_labels)

      _lowercase			 ='huggingface/label-files'
      _lowercase			 =num_labels
      _lowercase			 =json.load(open(cached_download(hf_hub_url(A__   ,     A__   ,     repo_type='dataset'  )  )   ,     'r'  )  )
      _lowercase			 ={int(A__  ): v for k, v in idalabel.items()}

      _lowercase			 =idalabel
      _lowercase			 ={v: k for k, v in idalabel.items()}

      _lowercase			 =partial(A__   ,     num_labels=A__   ,     idalabel=A__   ,     labelaid=A__  )

      _lowercase			 ={
          'regnet-x-002': ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]   ,     hidden_sizes=[24, 56, 152, 368]   ,     groups_width=8   ,     layer_type='x'  ),
          'regnet-x-004': ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]   ,     hidden_sizes=[32, 64, 160, 384]   ,     groups_width=16   ,     layer_type='x'  ),
          'regnet-x-006': ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]   ,     hidden_sizes=[48, 96, 240, 528]   ,     groups_width=24   ,     layer_type='x'  ),
          'regnet-x-008': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]   ,     hidden_sizes=[64, 128, 288, 672]   ,     groups_width=16   ,     layer_type='x'  ),
          'regnet-x-016': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]   ,     hidden_sizes=[72, 168, 408, 912]   ,     groups_width=24   ,     layer_type='x'  ),
          'regnet-x-032': ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]   ,     hidden_sizes=[96, 192, 432, 1008]   ,     groups_width=48   ,     layer_type='x'  ),
          'regnet-x-040': ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]   ,     hidden_sizes=[80, 240, 560, 1360]   ,     groups_width=40   ,     layer_type='x'  ),
          'regnet-x-064': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]   ,     hidden_sizes=[168, 392, 784, 1624]   ,     groups_width=56   ,     layer_type='x'  ),
          'regnet-x-080': ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]   ,     hidden_sizes=[80, 240, 720, 1920]   ,     groups_width=120   ,     layer_type='x'  ),
          'regnet-x-120': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]   ,     hidden_sizes=[224, 448, 896, 2240]   ,     groups_width=112   ,     layer_type='x'  ),
          'regnet-x-160': ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]   ,     hidden_sizes=[256, 512, 896, 2048]   ,     groups_width=128   ,     layer_type='x'  ),
          'regnet-x-320': ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]   ,     hidden_sizes=[336, 672, 1344, 2520]   ,     groups_width=168   ,     layer_type='x'  ),
          # y variant
          'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]   ,     hidden_sizes=[24, 56, 152, 368]   ,     groups_width=8  ),
          'regnet-y-004': ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]   ,     hidden_sizes=[48, 104, 208, 440]   ,     groups_width=8  ),
          'regnet-y-006': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]   ,     hidden_sizes=[48, 112, 256, 608]   ,     groups_width=16  ),
          'regnet-y-008': ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]   ,     hidden_sizes=[64, 128, 320, 768]   ,     groups_width=16  ),
          'regnet-y-016': ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]   ,     hidden_sizes=[48, 120, 336, 888]   ,     groups_width=24  ),
          'regnet-y-032': ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]   ,     hidden_sizes=[72, 216, 576, 1512]   ,     groups_width=24  ),
          'regnet-y-040': ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]   ,     hidden_sizes=[128, 192, 512, 1088]   ,     groups_width=64  ),
          'regnet-y-064': ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]   ,     hidden_sizes=[144, 288, 576, 1296]   ,     groups_width=72  ),
          'regnet-y-080': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]   ,     hidden_sizes=[168, 448, 896, 2016]   ,     groups_width=56  ),
          'regnet-y-120': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]   ,     hidden_sizes=[224, 448, 896, 2240]   ,     groups_width=112  ),
          'regnet-y-160': ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]   ,     hidden_sizes=[224, 448, 1232, 3024]   ,     groups_width=112  ),
          'regnet-y-320': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]   ,     hidden_sizes=[232, 696, 1392, 3712]   ,     groups_width=232  ),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]   ,     hidden_sizes=[232, 696, 1392, 3712]   ,     groups_width=232  ),
          'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]   ,     hidden_sizes=[328, 984, 1968, 4920]   ,     groups_width=328  ),
          'regnet-y-1280-seer': RegNetConfig(
              depths=[2, 7, 17, 1]   ,     hidden_sizes=[528, 1056, 2904, 7392]   ,     groups_width=264  ),
          'regnet-y-2560-seer': RegNetConfig(
              depths=[3, 7, 16, 1]   ,     hidden_sizes=[640, 1696, 2544, 5088]   ,     groups_width=640  ),
          'regnet-y-10b-seer': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]   ,     hidden_sizes=[2020, 4040, 11110, 28280]   ,     groups_width=1010  ),
          # finetuned on imagenet
          'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]   ,     hidden_sizes=[232, 696, 1392, 3712]   ,     groups_width=232  ),
          'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]   ,     hidden_sizes=[328, 984, 1968, 4920]   ,     groups_width=328  ),
          'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]   ,     hidden_sizes=[528, 1056, 2904, 7392]   ,     groups_width=264  ),
          'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]   ,     hidden_sizes=[640, 1696, 2544, 5088]   ,     groups_width=640  ),
          'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]   ,     hidden_sizes=[2020, 4040, 11110, 28280]   ,     groups_width=1010  ),
      }

      _lowercase			 =NameToOurModelFuncMap()
      _lowercase			 =NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(A__			:     str   ,     A__			:     Callable[[], nn.Module]  ) -> Tuple[nn.Module, Dict]:
            _lowercase			 =torch.hub.load_state_dict_from_url(A__   ,     model_dir=str(A__  )   ,     map_location='cpu'  )
            _lowercase			 =model_func()
            # check if we have a head, if yes add it
            _lowercase			 =files['classy_state_dict']['base_model']['model']
            _lowercase			 =model_state_dict['trunk']
            model.load_state_dict(A__  )
            return model.eval(), model_state_dict["heads"]

      # pretrained
      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'   ,     lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27   ,     group_width=1010   ,     w_a=1744   ,     w_a=620.83   ,     w_m=2.52  )  )  )   ,     )

      # IN1K finetuned
      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()  )   ,     )

      _lowercase			 =partial(
          A__   ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'   ,     lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27   ,     group_width=1010   ,     w_a=1744   ,     w_a=620.83   ,     w_m=2.52  )  )  )   ,     )

      if model_name:
            convert_weight_and_push(
                A__   ,     names_to_from_model_map[model_name]   ,     names_to_ours_model_map[model_name]   ,     names_to_config[model_name]   ,     A__   ,     A__   ,     )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      A__   ,     names_to_from_model_map[model_name]   ,     names_to_ours_model_map[model_name]   ,     A__   ,     A__   ,     A__   ,     )
      return config, expected_shape


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help=(
          'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
          ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=Path,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      default=True,
      type=bool,
      required=False,
      help='If True, push model and image processor to the hub.',
  )

  lowercase_             =     parser.parse_args()

  lowercase_             =     args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, 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   a   (    A__			:     Tuple  )					->   Any:





      """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=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   a   (    A__			:     Union[str, Any]  )					->   str:





      """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   a   (    A__			:     Optional[Any]   ,     A__			:     List[Any]  )					->   List[str]:





      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            _lowercase			 =orig_state_dict.pop(A__  )

            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   a   (    A__			:     str   ,     A__			:     List[str]=None   ,     A__			:     List[Any]=False  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =DonutModel.from_pretrained(A__  ).eval()

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

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

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

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

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

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

      # verify decoder hidden states
      _lowercase			 =original_model(A__   ,     A__   ,     A__  ).logits
      _lowercase			 =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__":
  lowercase_             =     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.',
  )

  lowercase_             =     parser.parse_args()
  convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


