


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
					"""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, 16, 32]				,  window_size=original_config.window_size				,  embed_dim=128				,  )
					__a       =   MBartConfig(
					    is_decoder=_lowerCAmelCase				,  is_encoder_decoder=_lowerCAmelCase				,  add_cross_attention=_lowerCAmelCase				,  decoder_layers=original_config.decoder_layer				,  max_position_embeddings=original_config.max_position_embeddings				,  vocab_size=len(
					        model.decoder.tokenizer		)				,  scale_embedding=_lowerCAmelCase				,  add_final_layer_norm=_lowerCAmelCase				,  )

					return encoder_config, decoder_config




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
					"""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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
					"""simple docstring"""




					for key in orig_state_dict.copy().keys():
										__a       =   orig_state_dict.pop(_lowerCAmelCase		)

										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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=False		)				->					Tuple:
					"""simple docstring"""




					__a       =   DonutModel.from_pretrained(_lowerCAmelCase		).eval()

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

					__a       =   original_model.state_dict()
					__a       =   convert_state_dict(_lowerCAmelCase				,  _lowerCAmelCase		)
					model.load_state_dict(_lowerCAmelCase		)

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

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

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

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

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

					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__":
	SCREAMING_SNAKE_CASE_      =			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.',
	)

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










'''simple docstring'''







from __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE_      =			'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE_      =			BASE_URL + '/user'

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE_      =			os.environ.get('USER_TOKEN', '')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					dict[Any, Any]:
     """simple docstring"""




     __a       =   {
         """Authorization""": F'''token {auth_token}''',
         """Accept""": """application/vnd.github.v3+json""",
     }
     return requests.get(__SCREAMING_SNAKE_CASE				,  headers=__SCREAMING_SNAKE_CASE		).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(f"""{key}: {value}""")
 else:
  raise ValueError('\'USER_TOKEN\' field cannot be empty.')






