


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_   (	_a	):




		"""simple docstring"""






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

		lowerCAmelCase__       :      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     ,       )
		lowerCAmelCase__       :      List[str]   					= 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 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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

				if name == "encoder.norm.weight":
						lowerCAmelCase__       :      Any   					= '''encoder.layernorm.weight'''
				if name == "encoder.norm.bias":
						lowerCAmelCase__       :      int   					= '''encoder.layernorm.bias'''

		return name






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				lowerCAmelCase__       :      List[str]   					= orig_state_dict.pop(_a	)

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

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

		return orig_state_dict






def 					lowerCamelCase_   (	_a     ,       _a=None     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= DonutModel.from_pretrained(_a	).eval()

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

		lowerCAmelCase__       :      Dict   					= original_model.state_dict()
		lowerCAmelCase__       :      List[str]   					= convert_state_dict(_a     ,       _a	)
		model.load_state_dict(_a	)

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

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

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

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

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

		# verify decoder hidden states
		lowerCAmelCase__       :      Dict   					= original_model(_a     ,       _a     ,       _a	).logits
		lowerCAmelCase__       :      str   					= 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__":
		lowerCamelCase :    int						=		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 :    List[str]						=		parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import math





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 10)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 10)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)




