




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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  Optional[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.config

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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  ,							)
			SCREAMING_SNAKE_CASE_   : List[str]					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  )       ->  int:







			"""simple docstring"""





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

						if name == "encoder.norm.weight":
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'encoder.layernorm.weight'
						if name == "encoder.norm.bias":
									SCREAMING_SNAKE_CASE_   : Tuple					  =      'encoder.layernorm.bias'

			return name





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     Tuple  )       ->  Optional[int]:







			"""simple docstring"""





			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_   : List[str]					  =      orig_state_dict.pop(lowerCamelCase_  )

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

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

			return orig_state_dict





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Dict=None  ,							lowerCamelCase_ :     int=False  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DonutModel.from_pretrained(lowerCamelCase_  ).eval()

			# load HuggingFace model
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      get_configs(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      DonutSwinModel(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Any					  =      MBartForCausalLM(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      VisionEncoderDecoderModel(encoder=lowerCamelCase_  ,							decoder=lowerCamelCase_  )
			model.eval()

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_model.state_dict()
			SCREAMING_SNAKE_CASE_   : Any					  =      convert_state_dict(lowerCamelCase_  ,							lowerCamelCase_  )
			model.load_state_dict(lowerCamelCase_  )

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

			SCREAMING_SNAKE_CASE_   : List[Any]					  =      XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase_  ,							from_slow=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis  ,							size=original_model.config.input_size[::-1]  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DonutProcessor(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      processor(lowerCamelCase_  ,							return_tensors='pt'  ).pixel_values

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

			SCREAMING_SNAKE_CASE_   : int					  =      original_model.encoder.model.patch_embed(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      model.encoder.embeddings(lowerCamelCase_  )
			assert torch.allclose(lowerCamelCase_  ,							lowerCamelCase_  ,							atol=1E-3  )

			# verify encoder hidden states
			SCREAMING_SNAKE_CASE_   : Any					  =      original_model.encoder(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.encoder(lowerCamelCase_  ).last_hidden_state
			assert torch.allclose(lowerCamelCase_  ,							lowerCamelCase_  ,							atol=1E-2  )

			# verify decoder hidden states
			SCREAMING_SNAKE_CASE_   : Dict					  =      original_model(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ).logits
			SCREAMING_SNAKE_CASE_   : int					  =      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__":
						UpperCamelCase__ :   Union[str, 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.''',
						)

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










'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


