



'''simple docstring'''






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    ):
  a						: Any					       =					model.config

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

    if name == "encoder.norm.weight":
      a						: Optional[Any]					       =					'''encoder.layernorm.weight'''
    if name == "encoder.norm.bias":
      a						: Tuple					       =					'''encoder.layernorm.bias'''

  return name
def 				lowerCamelCase__	(						_A			,      _A    ):



  for key in orig_state_dict.copy().keys():
    a						: Optional[Any]					       =					orig_state_dict.pop(a__    )

    if "qkv" in key:
      a						: List[str]					       =					key.split('.'    )
      a						: Union[str, Any]					       =					int(key_split[3]    )
      a						: Dict					       =					int(key_split[5]    )
      a						: str					       =					model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        a						: Optional[Any]					       =					val[:dim, :]
        a						: str					       =					val[dim : dim * 2, :]
        a						: Optional[Any]					       =					val[-dim:, :]
      else:
        a						: List[Any]					       =					val[:dim]
        a						: Any					       =					val[dim : dim * 2]
        a						: Any					       =					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						: Optional[Any]					       =					val

  return orig_state_dict
def 				lowerCamelCase__	(						_A			,      _A=None			,      _A=False    ):
  a						: Union[str, Any]					       =					DonutModel.from_pretrained(a__    ).eval()

  # load HuggingFace model
  a						: List[Any]					       =					get_configs(a__    )
  a						: List[Any]					       =					DonutSwinModel(a__    )
  a						: List[Any]					       =					MBartForCausalLM(a__    )
  a						: List[Any]					       =					VisionEncoderDecoderModel(encoder=a__			,      decoder=a__    )
  model.eval()

  a						: Any					       =					original_model.state_dict()
  a						: str					       =					convert_state_dict(a__			,      a__    )
  model.load_state_dict(a__    )

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

  a						: Union[str, Any]					       =					XLMRobertaTokenizerFast.from_pretrained(a__			,      from_slow=a__    )
  a						: List[str]					       =					DonutImageProcessor(
      do_align_long_axis=original_model.config.align_long_axis			,      size=original_model.config.input_size[::-1]    )
  a						: int					       =					DonutProcessor(a__			,      a__    )
  a						: Dict					       =					processor(a__			,      return_tensors='pt'    ).pixel_values

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

  a						: Optional[Any]					       =					original_model.encoder.model.patch_embed(a__    )
  a						: Optional[Any]					       =					model.encoder.embeddings(a__    )
  assert torch.allclose(a__			,      a__			,      atol=1E-3    )

  # verify encoder hidden states
  a						: Dict					       =					original_model.encoder(a__    )
  a						: int					       =					model.encoder(a__    ).last_hidden_state
  assert torch.allclose(a__			,      a__			,      atol=1E-2    )

  # verify decoder hidden states
  a						: Optional[Any]					       =					original_model(a__			,      a__			,      a__    ).logits
  a						: Union[str, 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__":
     lowerCAmelCase: 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.',
     )

     lowerCAmelCase: Tuple							=					parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase: Any							=						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: List[str]							=						['PoolFormerFeatureExtractor']
     lowerCAmelCase: Tuple							=						['PoolFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: str							=						[
         'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PoolFormerForImageClassification',
         'PoolFormerModel',
         'PoolFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: Dict							=						_LazyModule(__name__, globals()['__file__'], _import_structure)