

"""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   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      A				  =		model.config

      A				  =		DonutSwinConfig(
          image_size=original_config.input_size     ,   patch_size=4     ,   depths=original_config.encoder_layer     ,   num_heads=[4, 8, 1_6, 3_2]     ,   window_size=original_config.window_size     ,   embed_dim=1_2_8     ,   )
      A				  =		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	:						List[Any]				):



      """simple docstring"""






      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   _A					(	_a	:						Tuple     ,   _a	:						Optional[Any]				):



      """simple docstring"""






      for key in orig_state_dict.copy().keys():
            A				  =		orig_state_dict.pop(_a				)

            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   _A					(	_a	:						int     ,   _a	:						str=None     ,   _a	:						Any=False				):



      """simple docstring"""






      A				  =		DonutModel.from_pretrained(_a				).eval()

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

      A				  =		original_model.state_dict()
      A				  =		convert_state_dict(_a     ,   _a				)
      model.load_state_dict(_a				)

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

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

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

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

      # verify decoder hidden states
      A				  =		original_model(_a     ,   _a     ,   _a				).logits
      A				  =		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__":
  UpperCAmelCase													=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.",
  )

  UpperCAmelCase													=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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase													={
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "InformerForPrediction",
      "InformerModel",
      "InformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




