


"""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_   (					UpperCamelCase__						:			Dict			):






 """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=UpperCamelCase__					, is_encoder_decoder=UpperCamelCase__					, add_cross_attention=UpperCamelCase__					, decoder_layers=original_config.decoder_layer					, max_position_embeddings=original_config.max_position_embeddings					, vocab_size=len(
         model.decoder.tokenizer			)					, scale_embedding=UpperCamelCase__					, add_final_layer_norm=UpperCamelCase__					, )

 return encoder_config, decoder_config


def lowerCAmelCase_   (					UpperCamelCase__						:			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 lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Tuple			):






 """simple docstring"""







 for key in orig_state_dict.copy().keys():
  __lowercase						=						orig_state_dict.pop(UpperCamelCase__			)

  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 lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]					, UpperCamelCase__						:			Tuple=None					, UpperCamelCase__						:			Tuple=False			):






 """simple docstring"""







 __lowercase						=						DonutModel.from_pretrained(UpperCamelCase__			).eval()

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

 __lowercase						=						original_model.state_dict()
 __lowercase						=						convert_state_dict(UpperCamelCase__					, UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)

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

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

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

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

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

 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"""

def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return x if y == 0 else greatest_common_divisor(UpperCamelCase__					, x % y			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 return (x * y) // greatest_common_divisor(UpperCamelCase__					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			int = 20			):






 """simple docstring"""







 __lowercase						=						1
 for i in range(1					, n + 1			):
  __lowercase						=						lcm(UpperCamelCase__					, UpperCamelCase__			)
 return g


if __name__ == "__main__":
       print(f"""{solution() = }""")
