





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.size
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		SCREAMING_SNAKE_CASE__								=   image.resize((w, h)       , resample=PIL_INTERPOLATION["""lanczos"""]     )
		SCREAMING_SNAKE_CASE__								=   np.array(UpperCamelCase__     ).astype(np.floataa     ) / 2_5_5.0
		SCREAMING_SNAKE_CASE__								=   image[None].transpose(0       , 3       , 1       , 2     )
		SCREAMING_SNAKE_CASE__								=   torch.from_numpy(UpperCamelCase__     )
		return 2.0 * image - 1.0




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :Any				,					__A     :VQModel				,					__A     :UNetaDModel				,					__A     :Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]				,					) -> Tuple:



									"""simple docstring"""



									super().__init__()
									self.register_modules(vqvae=__A				,					unet=__A				,					scheduler=__A		)


							@torch.no_grad()
							def __call__(				self     :List[Any]				,					__A     :Union[torch.Tensor, PIL.Image.Image] = None				,					__A     :Optional[int] = 1				,					__A     :Optional[int] = 100				,					__A     :Optional[float] = 0.0				,					__A     :Optional[Union[torch.Generator, List[torch.Generator]]] = None				,					__A     :Optional[str] = "pil"				,					__A     :bool = True				,					) -> Union[Tuple, ImagePipelineOutput]:



									"""simple docstring"""



									if isinstance(__A				,					PIL.Image.Image		):
											SCREAMING_SNAKE_CASE__								=   1
									elif isinstance(__A				,					torch.Tensor		):
											SCREAMING_SNAKE_CASE__								=   image.shape[0]
									else:
											raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__A		)}'''		)

									if isinstance(__A				,					PIL.Image.Image		):
											SCREAMING_SNAKE_CASE__								=   preprocess(__A		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.shape[-2:]

									# in_channels should be 6: 3 for latents, 3 for low resolution image
									SCREAMING_SNAKE_CASE__								=   (batch_size, self.unet.config.in_channels // 2, height, width)
									SCREAMING_SNAKE_CASE__								=   next(self.unet.parameters()		).dtype

									SCREAMING_SNAKE_CASE__								=   randn_tensor(__A				,					generator=__A				,					device=self.device				,					dtype=__A		)

									SCREAMING_SNAKE_CASE__								=   image.to(device=self.device				,					dtype=__A		)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(__A				,					device=self.device		)
									SCREAMING_SNAKE_CASE__								=   self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									SCREAMING_SNAKE_CASE__								=   latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									SCREAMING_SNAKE_CASE__								=   """eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
									SCREAMING_SNAKE_CASE__								=   {}
									if accepts_eta:
											SCREAMING_SNAKE_CASE__								=   eta

									for t in self.progress_bar(__A		):
											# concat latents and low resolution image in the channel dimension.
											SCREAMING_SNAKE_CASE__								=   torch.cat([latents, image]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   self.scheduler.scale_model_input(__A				,					__A		)
											# predict the noise residual
											SCREAMING_SNAKE_CASE__								=   self.unet(__A				,					__A		).sample
											# compute the previous noisy sample x_t -> x_t-1
											SCREAMING_SNAKE_CASE__								=   self.scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample

									# decode the image latents with the VQVAE
									SCREAMING_SNAKE_CASE__								=   self.vqvae.decode(__A		).sample
									SCREAMING_SNAKE_CASE__								=   torch.clamp(__A				,					-1.0				,					1.0		)
									SCREAMING_SNAKE_CASE__								=   image / 2 + 0.5
									SCREAMING_SNAKE_CASE__								=   image.cpu().permute(0				,					2				,					3				,					1		).numpy()

									if output_type == "pil":
											SCREAMING_SNAKE_CASE__								=   self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)






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__				(							UpperCamelCase__:				str     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int=None       , UpperCamelCase__:				str=False     ):
		# load original model
		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__":
	_lowerCamelCase 					=      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 					=      parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)