


'''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       a				(      __a       )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			model.config

					UpperCamelCase__       :List[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			, )
					UpperCamelCase__       :int							 =			MBartConfig(
					    is_decoder=__a			, is_encoder_decoder=__a			, add_cross_attention=__a			, decoder_layers=original_config.decoder_layer			, max_position_embeddings=original_config.max_position_embeddings			, vocab_size=len(
					        model.decoder.tokenizer       )			, scale_embedding=__a			, add_final_layer_norm=__a			, )

					return encoder_config, decoder_config


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



					'''simple docstring'''






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

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

					return name


def       a				(      __a			, __a       )  ->     str:



					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__       :Union[str, Any]							 =			orig_state_dict.pop(__a       )

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

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

					return orig_state_dict


def       a				(      __a			, __a=None			, __a=False       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			DonutModel.from_pretrained(__a       ).eval()

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

					UpperCamelCase__       :Union[str, Any]							 =			original_model.state_dict()
					UpperCamelCase__       :Tuple							 =			convert_state_dict(__a			, __a       )
					model.load_state_dict(__a       )

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

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

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

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

					# verify encoder hidden states
					UpperCamelCase__       :Union[str, Any]							 =			original_model.encoder(__a       )
					UpperCamelCase__       :str							 =			model.encoder(__a       ).last_hidden_state
					assert torch.allclose(__a			, __a			, atol=1e-2       )

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

					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__":
		__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.''',
		)

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



'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case											=					logging.getLogger(__name__)
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'masked_bert'




		def __init__(		self   ,       UpperCamelCase_=30522   ,       UpperCamelCase_=768   ,       UpperCamelCase_=12   ,       UpperCamelCase_=12   ,       UpperCamelCase_=3072   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=512   ,       UpperCamelCase_=2   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=1e-12   ,       UpperCamelCase_=0   ,       UpperCamelCase_="topK"   ,       UpperCamelCase_="constant"   ,       UpperCamelCase_=0.0   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_   ,       **UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			vocab_size
							UpperCamelCase__       :Dict							 =			hidden_size
							UpperCamelCase__       :Optional[int]							 =			num_hidden_layers
							UpperCamelCase__       :Any							 =			num_attention_heads
							UpperCamelCase__       :Optional[Any]							 =			hidden_act
							UpperCamelCase__       :str							 =			intermediate_size
							UpperCamelCase__       :List[Any]							 =			hidden_dropout_prob
							UpperCamelCase__       :List[str]							 =			attention_probs_dropout_prob
							UpperCamelCase__       :List[Any]							 =			max_position_embeddings
							UpperCamelCase__       :int							 =			type_vocab_size
							UpperCamelCase__       :List[Any]							 =			initializer_range
							UpperCamelCase__       :Any							 =			layer_norm_eps
							UpperCamelCase__       :str							 =			pruning_method
							UpperCamelCase__       :int							 =			mask_init
							UpperCamelCase__       :Optional[Any]							 =			mask_scale