
'''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        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_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=__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        SCREAMING_SNAKE_CASE__			(     __A			) ->			Union[str, Any]:
	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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			str:
	for key in orig_state_dict.copy().keys():
		_snake_case     		=	orig_state_dict.pop(__A			)

		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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=None    ,       __A=False			) ->			List[Any]:
	# load original model
	_snake_case     		=	DonutModel.from_pretrained(__A			).eval()

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

	_snake_case     		=	original_model.state_dict()
	_snake_case     		=	convert_state_dict(__A    ,       __A			)
	model.load_state_dict(__A			)

	# 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(__A    ,       from_slow=__A			)
	_snake_case     		=	DonutImageProcessor(
	    do_align_long_axis=original_model.config.align_long_axis    ,       size=original_model.config.input_size[::-1]			)
	_snake_case     		=	DonutProcessor(__A    ,       __A			)
	_snake_case     		=	processor(__A    ,       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}'    ,       __A			)
	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(__A    ,       add_special_tokens=__A    ,       return_tensors='pt'			)[
	    'input_ids'
	]

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

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

	# verify decoder hidden states
	_snake_case     		=	original_model(__A    ,       __A    ,       __A			).logits
	_snake_case     		=	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__":
					lowercase					:							Tuple   =      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					:							List[Any]   =      parser.parse_args()
					convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size_divisor'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'resample'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     numpify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     np.ndarray						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

