



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       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      str     		=	model.config

				snake_case__	:      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  ,  )
				snake_case__	:      Optional[Any]     		=	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 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

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

								if name == "encoder.norm.weight":
												snake_case__	:      str     		=	'''encoder.layernorm.weight'''
								if name == "encoder.norm.bias":
												snake_case__	:      str     		=	'''encoder.layernorm.bias'''

				return name



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				for key in orig_state_dict.copy().keys():
								snake_case__	:      Optional[int]     		=	orig_state_dict.pop(__lowerCAmelCase      )

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

												if "weight" in key:
																snake_case__	:      Optional[Any]     		=	val[:dim, :]
																snake_case__	:      Any     		=	val[dim : dim * 2, :]
																snake_case__	:      Any     		=	val[-dim:, :]
												else:
																snake_case__	:      Dict     		=	val[:dim]
																snake_case__	:      Dict     		=	val[dim : dim * 2]
																snake_case__	:      Union[str, 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:
												snake_case__	:      Tuple     		=	val

				return orig_state_dict



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=None  ,  __lowerCAmelCase=False      )   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Any     		=	DonutModel.from_pretrained(__lowerCAmelCase      ).eval()

				# load HuggingFace model
				snake_case__		, snake_case__	:      Optional[Any]     		=	get_configs(__lowerCAmelCase      )
				snake_case__	:      int     		=	DonutSwinModel(__lowerCAmelCase      )
				snake_case__	:      Dict     		=	MBartForCausalLM(__lowerCAmelCase      )
				snake_case__	:      Optional[Any]     		=	VisionEncoderDecoderModel(encoder=__lowerCAmelCase  ,  decoder=__lowerCAmelCase      )
				model.eval()

				snake_case__	:      List[str]     		=	original_model.state_dict()
				snake_case__	:      Optional[Any]     		=	convert_state_dict(__lowerCAmelCase  ,  __lowerCAmelCase      )
				model.load_state_dict(__lowerCAmelCase      )

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

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

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

				snake_case__	:      Any     		=	original_model.encoder.model.patch_embed(__lowerCAmelCase      )
				snake_case__		, snake_case__	:      str     		=	model.encoder.embeddings(__lowerCAmelCase      )
				assert torch.allclose(__lowerCAmelCase  ,  __lowerCAmelCase  ,  atol=1E-3      )

				# verify encoder hidden states
				snake_case__	:      Tuple     		=	original_model.encoder(__lowerCAmelCase      )
				snake_case__	:      Dict     		=	model.encoder(__lowerCAmelCase      ).last_hidden_state
				assert torch.allclose(__lowerCAmelCase  ,  __lowerCAmelCase  ,  atol=1E-2      )

				# verify decoder hidden states
				snake_case__	:      Optional[int]     		=	original_model(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      ).logits
				snake_case__	:      Optional[int]     		=	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__":
				A__								=						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.''',
				)

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






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__								=						abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(__lowerCAmelCase      )





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				from transformers.testing_utils import pytest_terminal_summary_main

				snake_case__	:      Dict     		=	terminalreporter.config.getoption('''--make-reports'''      )
				if make_reports:
								pytest_terminal_summary_main(__lowerCAmelCase  ,  id=__lowerCAmelCase      )

