

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      snake_case_				(__A					:						Tuple					)      ->     Any:
   __lowerCAmelCase  :      Union[str, Any]    =						model.config

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

      if name == "encoder.norm.weight":
         __lowerCAmelCase  :      Optional[Any]    =						"""encoder.layernorm.weight"""
      if name == "encoder.norm.bias":
         __lowerCAmelCase  :      List[str]    =						"""encoder.layernorm.bias"""

   return name





def      snake_case_				(__A					:						List[str] ,       __A					:						Optional[int]					)      ->     Tuple:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Optional[Any]    =						orig_state_dict.pop(__A					)

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

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      int    =						val[dim : dim * 2, :]
            __lowerCAmelCase  :      int    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      Tuple    =						val[:dim]
            __lowerCAmelCase  :      Dict    =						val[dim : dim * 2]
            __lowerCAmelCase  :      int    =						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:
         __lowerCAmelCase  :      List[Any]    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Optional[int] ,       __A					:						List[str]=None ,       __A					:						Optional[Any]=False					)      ->     List[Any]:
   # load original model
   __lowerCAmelCase  :      str    =						DonutModel.from_pretrained(__A					).eval()

   # load HuggingFace model
   __lowerCAmelCase   ,__lowerCAmelCase  :      Union[str, Any]    =						get_configs(__A					)
   __lowerCAmelCase  :      int    =						DonutSwinModel(__A					)
   __lowerCAmelCase  :      List[Any]    =						MBartForCausalLM(__A					)
   __lowerCAmelCase  :      List[Any]    =						VisionEncoderDecoderModel(encoder=__A ,       decoder=__A					)
   model.eval()

   __lowerCAmelCase  :      Any    =						original_model.state_dict()
   __lowerCAmelCase  :      Union[str, Any]    =						convert_state_dict(__A ,       __A					)
   model.load_state_dict(__A					)

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

   __lowerCAmelCase  :      Union[str, Any]    =						XLMRobertaTokenizerFast.from_pretrained(__A ,       from_slow=__A					)
   __lowerCAmelCase  :      Any    =						DonutImageProcessor(
       do_align_long_axis=original_model.config.align_long_axis ,       size=original_model.config.input_size[::-1]					)
   __lowerCAmelCase  :      Tuple    =						DonutProcessor(__A ,       __A					)
   __lowerCAmelCase  :      List[str]    =						processor(__A ,       return_tensors="""pt"""					).pixel_values

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

   __lowerCAmelCase  :      int    =						original_model.encoder.model.patch_embed(__A					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						model.encoder.embeddings(__A					)
   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   # verify encoder hidden states
   __lowerCAmelCase  :      List[str]    =						original_model.encoder(__A					)
   __lowerCAmelCase  :      Optional[Any]    =						model.encoder(__A					).last_hidden_state
   assert torch.allclose(__A ,       __A ,       atol=1e-2					)

   # verify decoder hidden states
   __lowerCAmelCase  :      List[str]    =						original_model(__A ,       __A ,       __A					).logits
   __lowerCAmelCase  :      List[str]    =						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)





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=a_			)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(default="audio-classification"       ,							metadata={"include_in_asdict_even_if_is_default": True}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"audio": Audio()}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"labels": ClassLabel}			)
  lowerCamelCase   :      str										="audio"
  lowerCamelCase   :      str										="labels"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """simple docstring"""







     if self.label_column not in features:
        raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
     if not isinstance(features[self.label_column]	,							lowerCAmelCase   ):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self   )
     __lowerCAmelCase  :      Tuple    =						self.label_schema.copy()
     __lowerCAmelCase  :      Union[str, Any]    =						features[self.label_column]
     __lowerCAmelCase  :      Any    =						label_schema
     return task_template







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict[str, str]:






     """simple docstring"""







     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }


