'''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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	model.config

						lowercase_     :					Tuple													=	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				,				)
						lowercase_     :					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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





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

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

						return name
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





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

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

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

						return orig_state_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			str=None				,				__SCREAMING_SNAKE_CASE :			Tuple=False							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	DonutModel.from_pretrained(__lowerCamelCase							).eval()

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

						lowercase_     :					Optional[int]													=	original_model.state_dict()
						lowercase_     :					List[str]													=	convert_state_dict(__lowerCamelCase				,				__lowerCamelCase							)
						model.load_state_dict(__lowerCamelCase							)

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

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

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

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

						# verify encoder hidden states
						lowercase_     :					Any													=	original_model.encoder(__lowerCamelCase							)
						lowercase_     :					str													=	model.encoder(__lowerCamelCase							).last_hidden_state
						assert torch.allclose(__lowerCamelCase				,				__lowerCamelCase				,				atol=1E-2							)

						# verify decoder hidden states
						lowercase_     :					Optional[Any]													=	original_model(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase							).logits
						lowercase_     :					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__":
							_lowercase			:  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.",
							)

							_lowercase			:  Dict   							=	parser.parse_args()
							convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float(moles / volume							) * nfactor							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (volume)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((moles * 0.0821 * temperature) / (pressure)							)							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float				,				__SCREAMING_SNAKE_CASE :			float							):



						"""simple docstring"""





						return round(float((pressure * volume) / (0.0821 * moles)							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



