


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							(     _lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						__snake_case				=		model.config

						__snake_case				=		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   ,						)
						__snake_case				=		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 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						if "encoder.model" in name:
												__snake_case				=		name.replace("encoder.model"   ,						"encoder"			)
						if "decoder.model" in name:
												__snake_case				=		name.replace("decoder.model"   ,						"decoder"			)
						if "patch_embed.proj" in name:
												__snake_case				=		name.replace("patch_embed.proj"   ,						"embeddings.patch_embeddings.projection"			)
						if "patch_embed.norm" in name:
												__snake_case				=		name.replace("patch_embed.norm"   ,						"embeddings.norm"			)
						if name.startswith("encoder"			):
												if "layers" in name:
																		__snake_case				=		"encoder." + name
												if "attn.proj" in name:
																		__snake_case				=		name.replace("attn.proj"   ,						"attention.output.dense"			)
												if "attn" in name and "mask" not in name:
																		__snake_case				=		name.replace("attn"   ,						"attention.self"			)
												if "norm1" in name:
																		__snake_case				=		name.replace("norm1"   ,						"layernorm_before"			)
												if "norm2" in name:
																		__snake_case				=		name.replace("norm2"   ,						"layernorm_after"			)
												if "mlp.fc1" in name:
																		__snake_case				=		name.replace("mlp.fc1"   ,						"intermediate.dense"			)
												if "mlp.fc2" in name:
																		__snake_case				=		name.replace("mlp.fc2"   ,						"output.dense"			)

												if name == "encoder.norm.weight":
																		__snake_case				=		"encoder.layernorm.weight"
												if name == "encoder.norm.bias":
																		__snake_case				=		"encoder.layernorm.bias"

						return name





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												__snake_case				=		orig_state_dict.pop(_lowerCAmelCase			)

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

																		if "weight" in key:
																								__snake_case				=		val[:dim, :]
																								__snake_case				=		val[dim : dim * 2, :]
																								__snake_case				=		val[-dim:, :]
																		else:
																								__snake_case				=		val[:dim]
																								__snake_case				=		val[dim : dim * 2]
																								__snake_case				=		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:
																		__snake_case				=		val

						return orig_state_dict





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=None   ,						_lowerCAmelCase=False			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		DonutModel.from_pretrained(_lowerCAmelCase			).eval()

						# load HuggingFace model
						__snake_case		,       __snake_case				=		get_configs(_lowerCAmelCase			)
						__snake_case				=		DonutSwinModel(_lowerCAmelCase			)
						__snake_case				=		MBartForCausalLM(_lowerCAmelCase			)
						__snake_case				=		VisionEncoderDecoderModel(encoder=_lowerCAmelCase   ,						decoder=_lowerCAmelCase			)
						model.eval()

						__snake_case				=		original_model.state_dict()
						__snake_case				=		convert_state_dict(_lowerCAmelCase   ,						_lowerCAmelCase			)
						model.load_state_dict(_lowerCAmelCase			)

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

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

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

						__snake_case				=		original_model.encoder.model.patch_embed(_lowerCAmelCase			)
						__snake_case		,       __snake_case				=		model.encoder.embeddings(_lowerCAmelCase			)
						assert torch.allclose(_lowerCAmelCase   ,						_lowerCAmelCase   ,						atol=1E-3			)

						# verify encoder hidden states
						__snake_case				=		original_model.encoder(_lowerCAmelCase			)
						__snake_case				=		model.encoder(_lowerCAmelCase			).last_hidden_state
						assert torch.allclose(_lowerCAmelCase   ,						_lowerCAmelCase   ,						atol=1E-2			)

						# verify decoder hidden states
						__snake_case				=		original_model(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			).logits
						__snake_case				=		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__":
	A				:       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.',
	)

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









from __future__ import annotations





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						list[int]:


						'''simple docstring'''






						__snake_case				=		2
						__snake_case				=		[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase			)
						if n > 1:
												factors.append(_lowerCAmelCase			)
						return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()





