



"""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     _lowercase       (						__snake_case			):
			__lowerCAmelCase   :						int				= model.config

			__lowerCAmelCase   :						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      ,)
			__lowerCAmelCase   :						List[str]				= MBartConfig(
			    is_decoder=__snake_case      ,is_encoder_decoder=__snake_case      ,add_cross_attention=__snake_case      ,decoder_layers=original_config.decoder_layer      ,max_position_embeddings=original_config.max_position_embeddings      ,vocab_size=len(
			        model.decoder.tokenizer			)      ,scale_embedding=__snake_case      ,add_final_layer_norm=__snake_case      ,)

			return encoder_config, decoder_config


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

						if name == "encoder.norm.weight":
									__lowerCAmelCase   :						Optional[Any]				= "encoder.layernorm.weight"
						if name == "encoder.norm.bias":
									__lowerCAmelCase   :						Dict				= "encoder.layernorm.bias"

			return name


def     _lowercase       (						__snake_case      ,__snake_case			):
			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase   :						Optional[int]				= orig_state_dict.pop(__snake_case			)

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

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

			return orig_state_dict


def     _lowercase       (						__snake_case      ,__snake_case=None      ,__snake_case=False			):
			# load original model
			__lowerCAmelCase   :						List[str]				= DonutModel.from_pretrained(__snake_case			).eval()

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

			__lowerCAmelCase   :						Optional[Any]				= original_model.state_dict()
			__lowerCAmelCase   :						List[str]				= convert_state_dict(__snake_case      ,__snake_case			)
			model.load_state_dict(__snake_case			)

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

			__lowerCAmelCase   :						int				= XLMRobertaTokenizerFast.from_pretrained(__snake_case      ,from_slow=__snake_case			)
			__lowerCAmelCase   :						List[Any]				= DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis      ,size=original_model.config.input_size[::-1]			)
			__lowerCAmelCase   :						int				= DonutProcessor(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						int				= processor(__snake_case      ,return_tensors="pt"			).pixel_values

			if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
						__lowerCAmelCase   :						List[Any]				= "<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}"      ,__snake_case			)
			elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
						__lowerCAmelCase   :						Tuple				= "<s_rvlcdip>"
			elif model_name in [
			    "naver-clova-ix/donut-base-finetuned-cord-v1",
			    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
			]:
						__lowerCAmelCase   :						Any				= "<s_cord>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
						__lowerCAmelCase   :						Optional[int]				= "s_cord-v2>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
						__lowerCAmelCase   :						Optional[int]				= "<s_zhtrainticket>"
			elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
						# use a random prompt
						__lowerCAmelCase   :						Dict				= "hello world"
			else:
						raise ValueError("Model name not supported"			)
			__lowerCAmelCase   :						int				= original_model.decoder.tokenizer(__snake_case      ,add_special_tokens=__snake_case      ,return_tensors="pt"			)[
			    "input_ids"
			]

			__lowerCAmelCase   :						Any				= original_model.encoder.model.patch_embed(__snake_case			)
			__lowerCAmelCase   :						int				= model.encoder.embeddings(__snake_case			)
			assert torch.allclose(__snake_case      ,__snake_case      ,atol=1e-3			)

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

			# verify decoder hidden states
			__lowerCAmelCase   :						Optional[int]				= original_model(__snake_case      ,__snake_case      ,__snake_case			).logits
			__lowerCAmelCase   :						List[str]				= model(__snake_case      ,decoder_input_ids=__snake_case			).logits
			assert torch.allclose(__snake_case      ,__snake_case      ,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(__snake_case			)
						processor.save_pretrained(__snake_case			)

			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							:     List[str]   =					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							:     Optional[int]   =					parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






import math


def     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return math.pow(__snake_case      ,2			) - a


def     _lowercase       (						__snake_case			)		->     float:
			return 2 * x


def     _lowercase       (						__snake_case			)		->     float:
			__lowerCAmelCase   :						List[str]				= 2.0

			while start <= a:
						__lowerCAmelCase   :						Union[str, Any]				= math.pow(__snake_case      ,2			)

			return start


def     _lowercase       (						__snake_case      ,__snake_case = 9_999      ,__snake_case = 0.00000000000001			)		->     float:

			if a < 0:
						raise ValueError("math domain error"			)

			__lowerCAmelCase   :						List[Any]				= get_initial_point(__snake_case			)

			for _ in range(__snake_case			):
						__lowerCAmelCase   :						Union[str, Any]				= value
						__lowerCAmelCase   :						List[str]				= value - fx(__snake_case      ,__snake_case			) / fx_derivative(__snake_case			)
						if abs(prev_value - value			) < tolerance:
									return value

			return value


if __name__ == "__main__":
	from doctest import testmod

	testmod()