






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       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Optional[Any]					=					model.config

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

				if name == "encoder.norm.weight":
						UpperCamelCase__				:					Optional[int]					=					'encoder.layernorm.weight'
				if name == "encoder.norm.bias":
						UpperCamelCase__				:					Tuple					=					'encoder.layernorm.bias'

		return name







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     str:
		for key in orig_state_dict.copy().keys():
				UpperCamelCase__				:					List[str]					=					orig_state_dict.pop(lowerCamelCase_)

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

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

		return orig_state_dict







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=False)							->     Dict:
		# load original model
		UpperCamelCase__				:					str					=					DonutModel.from_pretrained(lowerCamelCase_).eval()

		# load HuggingFace model
		UpperCamelCase__				:					Optional[Any]					=					get_configs(lowerCamelCase_)
		UpperCamelCase__				:					int					=					DonutSwinModel(lowerCamelCase_)
		UpperCamelCase__				:					str					=					MBartForCausalLM(lowerCamelCase_)
		UpperCamelCase__				:					List[Any]					=					VisionEncoderDecoderModel(encoder=lowerCamelCase_     ,      decoder=lowerCamelCase_)
		model.eval()

		UpperCamelCase__				:					List[str]					=					original_model.state_dict()
		UpperCamelCase__				:					str					=					convert_state_dict(lowerCamelCase_     ,      lowerCamelCase_)
		model.load_state_dict(lowerCamelCase_)

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

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

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

		UpperCamelCase__				:					Union[str, Any]					=					original_model.encoder.model.patch_embed(lowerCamelCase_)
		UpperCamelCase__				:					Any					=					model.encoder.embeddings(lowerCamelCase_)
		assert torch.allclose(lowerCamelCase_     ,      lowerCamelCase_     ,      atol=1e-3)

		# verify encoder hidden states
		UpperCamelCase__				:					int					=					original_model.encoder(lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					model.encoder(lowerCamelCase_).last_hidden_state
		assert torch.allclose(lowerCamelCase_     ,      lowerCamelCase_     ,      atol=1e-2)

		# verify decoder hidden states
		UpperCamelCase__				:					Optional[int]					=					original_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_).logits
		UpperCamelCase__				:					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__":
		lowerCAmelCase__          =						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.',
		)

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












'''simple docstring'''




from __future__ import annotations







class       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						list[list[int]]):
			UpperCamelCase__				:					int					=					TypeError(
			    'Matrices must be formed from a list of zero or more lists containing at '
			    'least one and the same number of values, each of which must be of type '
			    'int or float.')
			if len(UpperCAmelCase_) != 0:
					UpperCamelCase__				:					str					=					len(rows[0])
					if cols == 0:
							raise error
					for row in rows:
							if len(UpperCAmelCase_) != cols:
									raise error
							for value in row:
									if not isinstance(UpperCAmelCase_  ,					(int, float)):
											raise error
					UpperCamelCase__				:					Optional[int]					=					rows
			else:
					UpperCamelCase__				:					Optional[Any]					=					[]

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return len(self.rows)

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return len(self.rows[0])

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return (self.num_rows, self.num_columns)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self.order[0] == self.order[1]

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[int]					=					[
			    [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
			    for row_num in range(self.num_rows)
			]
			return Matrix(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			if not self.is_square:
					return 0
			if self.order == (0, 0):
					return 1
			if self.order == (1, 1):
					return int(self.rows[0][0])
			if self.order == (2, 2):
					return int(
					    (self.rows[0][0] * self.rows[1][1])
					    - (self.rows[0][1] * self.rows[1][0]))
			else:
					return sum(
					    self.rows[0][column] * self.cofactors().rows[0][column]
					    for column in range(self.num_columns))

	def      __UpperCamelCase							(      self   :						str):
			return bool(self.determinant())

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[Any]					=					[
			    [
			        self.rows[other_row][other_column]
			        for other_column in range(self.num_columns)
			        if other_column != column
			    ]
			    for other_row in range(self.num_rows)
			    if other_row != row
			]
			return Matrix(UpperCAmelCase_).determinant()

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			if (row + column) % 2 == 0:
					return self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_)
			return -1 * self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]):
			return Matrix(
			    [
			        [self.get_minor(UpperCAmelCase_  ,					UpperCAmelCase_) for column in range(self.num_columns)]
			        for row in range(self.num_rows)
			    ])

	def      __UpperCamelCase							(      self   :						Optional[int]):
			return Matrix(
			    [
			        [
			            self.minors().rows[row][column]
			            if (row + column) % 2 == 0
			            else self.minors().rows[row][column] * -1
			            for column in range(self.minors().num_columns)
			        ]
			        for row in range(self.minors().num_rows)
			    ])

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					[
			    [self.cofactors().rows[column][row] for column in range(self.num_columns)]
			    for row in range(self.num_rows)
			]
			return Matrix(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					self.determinant()
			if not determinant:
					raise TypeError('Only matrices with a non-zero determinant have an inverse')
			return self.adjugate() * (1 / determinant)

	def __repr__(      self   :						Any):
			return str(self.rows)

	def __str__(      self   :						List[Any]):
			if self.num_rows == 0:
					return "[]"
			if self.num_rows == 1:
					return "[[" + ". ".join(str(self.rows[0])) + "]]"
			return (
			    "["
			    + "\n ".join(
			        [
			            '[' + '. '.join([str(UpperCAmelCase_) for value in row]) + '.]'
			            for row in self.rows
			        ])
			    + "]"
			)

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						int | None = None):
			UpperCamelCase__				:					List[str]					=					TypeError('Row must be a list containing all ints and/or floats')
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise type_error
			for value in row:
					if not isinstance(UpperCAmelCase_  ,					(int, float)):
							raise type_error
			if len(UpperCAmelCase_) != self.num_columns:
					raise ValueError(
					    'Row must be equal in length to the other rows in the matrix')
			if position is None:
					self.rows.append(UpperCAmelCase_)
			else:
					UpperCamelCase__				:					Tuple					=					self.rows[0:position] + [row] + self.rows[position:]

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						int | None = None):
			UpperCamelCase__				:					int					=					TypeError(
			    'Column must be a list containing all ints and/or floats')
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise type_error
			for value in column:
					if not isinstance(UpperCAmelCase_  ,					(int, float)):
							raise type_error
			if len(UpperCAmelCase_) != self.num_rows:
					raise ValueError(
					    'Column must be equal in length to the other columns in the matrix')
			if position is None:
					UpperCamelCase__				:					Optional[int]					=					[self.rows[i] + [column[i]] for i in range(self.num_rows)]
			else:
					UpperCamelCase__				:					str					=					[
					    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
					    for i in range(self.num_rows)
					]

	def __eq__(      self   :						List[Any]  ,					UpperCAmelCase_   :						object):
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					return NotImplemented
			return self.rows == other.rows

	def __ne__(      self   :						Any  ,					UpperCAmelCase_   :						object):
			return not self == other

	def __neg__(      self   :						Union[str, Any]):
			return self * -1

	def __add__(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Matrix):
			if self.order != other.order:
					raise ValueError('Addition requires matrices of the same order')
			return Matrix(
			    [
			        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
			        for i in range(self.num_rows)
			    ])

	def __sub__(      self   :						Tuple  ,					UpperCAmelCase_   :						Matrix):
			if self.order != other.order:
					raise ValueError('Subtraction requires matrices of the same order')
			return Matrix(
			    [
			        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
			        for i in range(self.num_rows)
			    ])

	def __mul__(      self   :						Any  ,					UpperCAmelCase_   :						Matrix | int | float):
			if isinstance(UpperCAmelCase_  ,					(int, float)):
					return Matrix(
					    [[int(element * other) for element in row] for row in self.rows])
			elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					if self.num_columns != other.num_rows:
							raise ValueError(
							    'The number of columns in the first matrix must '
							    'be equal to the number of rows in the second')
					return Matrix(
					    [
					        [Matrix.dot_product(UpperCAmelCase_  ,					UpperCAmelCase_) for column in other.columns()]
					        for row in self.rows
					    ])
			else:
					raise TypeError(
					    'A Matrix can only be multiplied by an int, float, or another matrix')

	def __pow__(      self   :						Dict  ,					UpperCAmelCase_   :						int):
			if not isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					raise TypeError('A Matrix can only be raised to the power of an int')
			if not self.is_square:
					raise ValueError('Only square matrices can be raised to a power')
			if other == 0:
					return self.identity()
			if other < 0:
					if self.is_invertable():
							return self.inverse() ** (-other)
					raise ValueError(
					    'Only invertable matrices can be raised to a negative power')
			UpperCamelCase__				:					str					=					self
			for _ in range(other - 1):
					result *= self
			return result





	@classmethod
	def      __UpperCamelCase							(      cls   :						Optional[int]  ,					UpperCAmelCase_   :						list[int]  ,					UpperCAmelCase_   :						list[int]):
			return sum(row[i] * column[i] for i in range(len(UpperCAmelCase_)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




