
"""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"""


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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(A_  ,size=size['shortest_edge']  ,default_to_square=A_      )
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
							return center_crop(A_  ,size=(size['height'], size['width'])  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      float  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]      )     ->   np.ndarray:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Any  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   make_list_of_images(A_      )

							if not valid_images(A_      ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

							if do_resize and size is None:
								raise ValueError('Size must be specified if do_resize is True.'      )

							if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.'      )

							if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

							if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

							# All transformations expect numpy arrays.
							A						   =   [to_numpy_array(A_      ) for image in images]

							if do_resize:
								A						   =   [self.resize(image=A_  ,size=A_  ,resample=A_      ) for image in images]

							if do_center_crop:
								A						   =   [self.center_crop(image=A_  ,size=A_      ) for image in images]

							if do_rescale:
								A						   =   [self.rescale(image=A_  ,scale=A_      ) for image in images]

							if do_normalize:
								A						   =   [self.normalize(image=A_  ,mean=A_  ,std=A_      ) for image in images]

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
								if len(A_      ) != len(A_      ):
									raise ValueError(
									    'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

								for idx in range(len(A_      )      ):
									A						   =   torch.nn.functional.interpolate(
									    logits[idx].unsqueeze(dim=0      )  ,size=target_sizes[idx]  ,mode='bilinear'  ,align_corners=A_      )
									A						   =   resized_logits[0].argmax(dim=0      )
									semantic_segmentation.append(A_      )
							else:
								A						   =   logits.argmax(dim=1      )
								A						   =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

							return semantic_segmentation