



'''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   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  model.config

	__lowerCAmelCase												=  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=1_28				,		)
	__lowerCAmelCase												=  MBartConfig(
	    is_decoder=lowercase_				,		is_encoder_decoder=lowercase_				,		add_cross_attention=lowercase_				,		decoder_layers=original_config.decoder_layer				,		max_position_embeddings=original_config.max_position_embeddings				,		vocab_size=len(
	        model.decoder.tokenizer					)				,		scale_embedding=lowercase_				,		add_final_layer_norm=lowercase_				,		)

	return encoder_config, decoder_config






def   __lowerCAmelCase					(							lowerCamelCase   :						Tuple					):






	'''simple docstring'''




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

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

	return name






def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase												=  orig_state_dict.pop(lowercase_					)

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

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

	return orig_state_dict






def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Any=None				,		lowerCamelCase   :						Tuple=False					):






	'''simple docstring'''




	__lowerCAmelCase												=  DonutModel.from_pretrained(lowercase_					).eval()

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

	__lowerCAmelCase												=  original_model.state_dict()
	__lowerCAmelCase												=  convert_state_dict(lowercase_				,		lowercase_					)
	model.load_state_dict(lowercase_					)

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

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

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

	__lowerCAmelCase												=  original_model.encoder.model.patch_embed(lowercase_					)
	__lowerCAmelCase												=  model.encoder.embeddings(lowercase_					)
	assert torch.allclose(lowercase_				,		lowercase_				,		atol=1e-3					)

	# verify encoder hidden states
	__lowerCAmelCase												=  original_model.encoder(lowercase_					)
	__lowerCAmelCase												=  model.encoder(lowercase_					).last_hidden_state
	assert torch.allclose(lowercase_				,		lowercase_				,		atol=1e-2					)

	# verify decoder hidden states
	__lowerCAmelCase												=  original_model(lowercase_				,		lowercase_				,		lowercase_					).logits
	__lowerCAmelCase												=  model(lowercase_				,		decoder_input_ids=lowercase_					).logits
	assert torch.allclose(lowercase_				,		lowercase_				,		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(lowercase_					)
		processor.save_pretrained(lowercase_					)

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




'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


lowerCAmelCase	:		Optional[Any]				=   '''scheduler_config.json'''






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     str        =							1
					a      :     Optional[int]        =							2
					a      :     int        =							3
					a      :     Union[str, Any]        =							4
					a      :     int        =							5
					a      :     Optional[int]        =							6
					a      :     str        =							7
					a      :     List[Any]        =							8
					a      :     List[str]        =							9
					a      :     List[str]        =							1_0
					a      :     int        =							1_1
					a      :     Any        =							1_2
					a      :     Any        =							1_3
					a      :     Tuple        =							1_4






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	:
					a      :     Tuple        =							SCHEDULER_CONFIG_NAME
					a      :     Union[str, Any]        =							[]
					a      :     str        =							True



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase=False			,				**UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  cls.load_config(
						    pretrained_model_name_or_path=UpperCamelCase			,				subfolder=UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				return_commit_hash=UpperCamelCase			,				**UpperCamelCase			,				)
						return cls.from_config(UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				**UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = False			,				**UpperCamelCase     )     ->			Dict:
						self.save_config(save_directory=UpperCamelCase			,				push_to_hub=UpperCamelCase			,				**UpperCamelCase     )



					@property
					def        UpperCAmelCase_ (    self     )     ->			str:
						return self._get_compatibles()



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Tuple:
						__lowerCAmelCase												=  list(set([cls.__name__] + cls._compatibles     )     )
						__lowerCAmelCase												=  importlib.import_module(__name__.split("."     )[0]     )
						__lowerCAmelCase												=  [
						    getattr(UpperCamelCase			,				UpperCamelCase     ) for c in compatible_classes_str if hasattr(UpperCamelCase			,				UpperCamelCase     )
						]
						return compatible_classes