
"""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				(		snake_case__   :     Optional[int]     ):
	A						   =   model.config

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

		if name == "encoder.norm.weight":
			A						   =   'encoder.layernorm.weight'
		if name == "encoder.norm.bias":
			A						   =   'encoder.layernorm.bias'

	return name






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     str     ):
	for key in orig_state_dict.copy().keys():
		A						   =   orig_state_dict.pop(snake_case__     )

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

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

	return orig_state_dict






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Tuple=None	,      snake_case__   :     List[Any]=False     ):
	# load original model
	A						   =   DonutModel.from_pretrained(snake_case__     ).eval()

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

	A						   =   original_model.state_dict()
	A						   =   convert_state_dict(snake_case__	,      snake_case__     )
	model.load_state_dict(snake_case__     )

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

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

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

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

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

	# verify decoder hidden states
	A						   =   original_model(snake_case__	,      snake_case__	,      snake_case__     ).logits
	A						   =   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__":
		_lowercase							   =				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							   =				parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )