
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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       Optional[Any]:

				"""simple docstring"""







				A__  :	Union[str, Any]         =							model.config

				A__  :	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=1_28							,						)
				A__  :	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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       List[Any]:

				"""simple docstring"""







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

								if name == "encoder.norm.weight":
												A__  :	Union[str, Any]         =							'''encoder.layernorm.weight'''
								if name == "encoder.norm.bias":
												A__  :	List[str]         =							'''encoder.layernorm.bias'''

				return name

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						List[str]       ) ->       List[Any]:

				"""simple docstring"""







				for key in orig_state_dict.copy().keys():
								A__  :	Optional[Any]         =							orig_state_dict.pop(_lowerCAmelCase       )

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

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

				return orig_state_dict

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						int=None							,						__UpperCamelCase :						Optional[Any]=False       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Any         =							DonutModel.from_pretrained(_lowerCAmelCase       ).eval()

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

				A__  :	Optional[Any]         =							original_model.state_dict()
				A__  :	Any         =							convert_state_dict(_lowerCAmelCase							,						_lowerCAmelCase       )
				model.load_state_dict(_lowerCAmelCase       )

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

				A__  :	Any         =							XLMRobertaTokenizerFast.from_pretrained(_lowerCAmelCase							,						from_slow=_lowerCAmelCase       )
				A__  :	Optional[Any]         =							DonutImageProcessor(
				    do_align_long_axis=original_model.config.align_long_axis							,						size=original_model.config.input_size[::-1]       )
				A__  :	Tuple         =							DonutProcessor(_lowerCAmelCase							,						_lowerCAmelCase       )
				A__  :	Tuple         =							processor(_lowerCAmelCase							,						return_tensors='''pt'''       ).pixel_values

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

				A__  :	List[str]         =							original_model.encoder.model.patch_embed(_lowerCAmelCase       )
				A__       ,							A__  :	Optional[int]         =							model.encoder.embeddings(_lowerCAmelCase       )
				assert torch.allclose(_lowerCAmelCase							,						_lowerCAmelCase							,						atol=1e-3       )

				# verify encoder hidden states
				A__  :	List[str]         =							original_model.encoder(_lowerCAmelCase       )
				A__  :	Dict         =							model.encoder(_lowerCAmelCase       ).last_hidden_state
				assert torch.allclose(_lowerCAmelCase							,						_lowerCAmelCase							,						atol=1e-2       )

				# verify decoder hidden states
				A__  :	Tuple         =							original_model(_lowerCAmelCase							,						_lowerCAmelCase							,						_lowerCAmelCase       ).logits
				A__  :	Optional[Any]         =							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    :      List[Any]          =			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    :      int          =			parser.parse_args()
					convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE    :      Dict          =			logging.get_logger(__name__)

# TODO Update this
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			{
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"esm"





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=768				,       UpperCamelCase__=12				,       UpperCamelCase__=12				,       UpperCamelCase__=3072				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=1026				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-12				,       UpperCamelCase__="absolute"				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       **UpperCamelCase__				,       ):
					super().__init__(pad_token_id=UpperCamelCase__				,       mask_token_id=UpperCamelCase__				,       **UpperCamelCase__		)

					A__  :	Optional[Any]         =							vocab_size
					A__  :	int         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Tuple         =							num_attention_heads
					A__  :	str         =							intermediate_size
					A__  :	List[str]         =							hidden_dropout_prob
					A__  :	Optional[Any]         =							attention_probs_dropout_prob
					A__  :	int         =							max_position_embeddings
					A__  :	List[str]         =							initializer_range
					A__  :	List[Any]         =							layer_norm_eps
					A__  :	int         =							position_embedding_type
					A__  :	Optional[Any]         =							use_cache
					A__  :	Optional[int]         =							emb_layer_norm_before
					A__  :	List[str]         =							token_dropout
					A__  :	Tuple         =							is_folding_model
					if is_folding_model:
									if esmfold_config is None:
													logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
													A__  :	List[Any]         =							EsmFoldConfig()
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Optional[int]         =							EsmFoldConfig(**UpperCamelCase__		)
									A__  :	int         =							esmfold_config
									if vocab_list is None:
													logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
													A__  :	Any         =							get_default_vocab_list()
									else:
													A__  :	Dict         =							vocab_list
					else:
									A__  :	Optional[Any]         =							None
									A__  :	Tuple         =							None
					if self.esmfold_config is not None and getattr(self.esmfold_config				,       '''use_esm_attn_map'''				,       UpperCamelCase__		):
									raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							super().to_dict()
					if isinstance(self.esmfold_config				,       UpperCamelCase__		):
									A__  :	Dict         =							self.esmfold_config.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None
	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				0

	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False

	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.trunk is None:
									A__  :	Tuple         =							TrunkConfig()
					elif isinstance(self.trunk				,       UpperCamelCase__		):
									A__  :	List[Any]         =							TrunkConfig(**self.trunk		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							asdict(self		)
					A__  :	int         =							self.trunk.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				48
	_lowerCAmelCase							       =				1_024
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.structure_module is None:
									A__  :	str         =							StructureModuleConfig()
					elif isinstance(self.structure_module				,       UpperCamelCase__		):
									A__  :	str         =							StructureModuleConfig(**self.structure_module		)

					if self.max_recycles <= 0:
									raise ValueError(F"`max_recycles` should be positive, got {self.max_recycles}."		)
					if self.sequence_state_dim % self.sequence_state_dim != 0:
									raise ValueError(
									    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
									    F" {self.sequence_state_dim} and {self.sequence_state_dim}."		)
					if self.pairwise_state_dim % self.pairwise_state_dim != 0:
									raise ValueError(
									    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
									    F" {self.pairwise_state_dim} and {self.pairwise_state_dim}."		)

					A__  :	Tuple         =							self.sequence_state_dim // self.sequence_head_width
					A__  :	int         =							self.pairwise_state_dim // self.pairwise_head_width

					if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
									raise ValueError(
									    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
									    F" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."		)
					if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
									raise ValueError(
									    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
									    F" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."		)
					if self.pairwise_state_dim % 2 != 0:
									raise ValueError(F"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."		)

					if self.dropout >= 0.4:
									raise ValueError(F"`dropout` should not be greater than 0.4, got {self.dropout}."		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							asdict(self		)
					A__  :	Optional[int]         =							self.structure_module.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				384
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				16
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				12
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				0.1
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				1
	_lowerCAmelCase							       =				2
	_lowerCAmelCase							       =				7
	_lowerCAmelCase							       =				10
	_lowerCAmelCase							       =				1e-8
	_lowerCAmelCase							       =				1e5





	def   __snake_case				(			self		):
					return asdict(self		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				return (
				    "<cls>",
				    "<pad>",
				    "<eos>",
				    "<unk>",
				    "L",
				    "A",
				    "G",
				    "V",
				    "S",
				    "E",
				    "R",
				    "T",
				    "I",
				    "D",
				    "P",
				    "K",
				    "Q",
				    "N",
				    "F",
				    "Y",
				    "M",
				    "H",
				    "W",
				    "C",
				    "X",
				    "B",
				    "U",
				    "Z",
				    "O",
				    ".",
				    "-",
				    "<null_1>",
				    "<mask>",
				)