


"""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   UpperCAmelCase__			(lowerCAmelCase_  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							model.config

					__SCREAMING_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    ,		)
					__SCREAMING_SNAKE_CASE		  =							MBartConfig(
					    is_decoder=UpperCamelCase__    ,		is_encoder_decoder=UpperCamelCase__    ,		add_cross_attention=UpperCamelCase__    ,		decoder_layers=original_config.decoder_layer    ,		max_position_embeddings=original_config.max_position_embeddings    ,		vocab_size=len(
					        model.decoder.tokenizer  )    ,		scale_embedding=UpperCamelCase__    ,		add_final_layer_norm=UpperCamelCase__    ,		)

					return encoder_config, decoder_config




def   UpperCAmelCase__			(lowerCAmelCase_  ):




					'''simple docstring'''


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

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

					return name




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_  ):




					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										__SCREAMING_SNAKE_CASE		  =							orig_state_dict.pop(UpperCamelCase__  )

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

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

					return orig_state_dict




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_=None    ,		lowerCAmelCase_=False  ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		  =							DonutModel.from_pretrained(UpperCamelCase__  ).eval()

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

					__SCREAMING_SNAKE_CASE		  =							original_model.state_dict()
					__SCREAMING_SNAKE_CASE		  =							convert_state_dict(UpperCamelCase__    ,		UpperCamelCase__  )
					model.load_state_dict(UpperCamelCase__  )

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

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

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

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

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

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

					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__							:				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.''',
		)

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




"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
