





from typing import Dict, List, Optional, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_   :      Tuple			=   logging.get_logger(__name__)

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		['''pixel_values''']




	def __init__(						self							:     List[Any]					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Optional[Dict[str, int]] = None					,      __lowerCamelCase							:     PILImageResampling = PILImageResampling.BICUBIC					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Union[int, float] = 1 / 255					,      __lowerCamelCase							:     Dict[str, int] = None					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = None					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = None					,      **__lowerCamelCase							:     Optional[Any]					,      )       -> None:

			'''simple docstring'''




			super().__init__(**__lowerCamelCase   )
			__lowercase											=     size if size is not None else {'height': 224, 'width': 224}
			__lowercase											=     get_size_dict(__lowerCamelCase   )
			__lowercase											=     crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__lowercase											=     get_size_dict(__lowerCamelCase					,      default_to_square=__lowerCamelCase					,      param_name='crop_size'   )

			__lowercase											=     do_resize
			__lowercase											=     do_rescale
			__lowercase											=     do_normalize
			__lowercase											=     do_center_crop
			__lowercase											=     crop_size
			__lowercase											=     size
			__lowercase											=     resample
			__lowercase											=     rescale_factor
			__lowercase											=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
			__lowercase											=     image_std if image_std is not None else IMAGENET_DEFAULT_STD




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Dict[str, int]					,      __lowerCamelCase							:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     Dict					,      )       -> np.ndarray:

			'''simple docstring'''




			__lowercase											=     get_size_dict(__lowerCamelCase   )

			if "shortest_edge" in size:
					__lowercase											=     get_resize_output_image_size(__lowerCamelCase					,      size=size['shortest_edge']					,      default_to_square=__lowerCamelCase   )
					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
			elif "height" in size and "width" in size:
					__lowercase											=     (size['height'], size['width'])
			else:
					raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"   )
			return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Dict[str, int]					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     str					,      )       -> np.ndarray:

			'''simple docstring'''




			__lowercase											=     get_size_dict(__lowerCamelCase   )
			if "height" not in size or "width" not in size:
					raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"   )
			return center_crop(__lowerCamelCase					,      size=(size['height'], size['width'])					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     float					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     Optional[int]   )       -> np.ndarray:

			'''simple docstring'''




			return rescale(__lowerCamelCase					,      scale=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Union[float, List[float]]					,      __lowerCamelCase							:     Union[float, List[float]]					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     Union[str, Any]					,      )       -> np.ndarray:

			'''simple docstring'''




			return normalize(__lowerCamelCase					,      mean=__lowerCamelCase					,      std=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     ImageInput					,      __lowerCamelCase							:     Optional[bool] = None					,      __lowerCamelCase							:     Dict[str, int] = None					,      __lowerCamelCase							:     PILImageResampling = None					,      __lowerCamelCase							:     bool = None					,      __lowerCamelCase							:     int = None					,      __lowerCamelCase							:     Optional[bool] = None					,      __lowerCamelCase							:     Optional[float] = None					,      __lowerCamelCase							:     Optional[bool] = None					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = None					,      __lowerCamelCase							:     Optional[Union[float, List[float]]] = None					,      __lowerCamelCase							:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase							:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__lowerCamelCase							:     int					,      )       -> BatchFeature:

			'''simple docstring'''




			__lowercase											=     do_resize if do_resize is not None else self.do_resize
			__lowercase											=     do_rescale if do_rescale is not None else self.do_rescale
			__lowercase											=     do_normalize if do_normalize is not None else self.do_normalize
			__lowercase											=     do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowercase											=     crop_size if crop_size is not None else self.crop_size
			__lowercase											=     get_size_dict(__lowerCamelCase					,      param_name='crop_size'					,      default_to_square=__lowerCamelCase   )
			__lowercase											=     resample if resample is not None else self.resample
			__lowercase											=     rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowercase											=     image_mean if image_mean is not None else self.image_mean
			__lowercase											=     image_std if image_std is not None else self.image_std

			__lowercase											=     size if size is not None else self.size
			__lowercase											=     get_size_dict(__lowerCamelCase   )

			if not is_batched(__lowerCamelCase   ):
					__lowercase											=     [images]

			if not valid_images(__lowerCamelCase   ):
					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.'   )

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

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

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

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

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

			__lowercase											=     [to_channel_dimension_format(__lowerCamelCase					,      __lowerCamelCase   ) for image in images]

			__lowercase											=     {'pixel_values': images}
			return BatchFeature(data=__lowerCamelCase					,      tensor_type=__lowerCamelCase   )











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				(      snake_case							)		->    Optional[int]:
		__lowercase											=     model.config

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

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

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

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

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

		return orig_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Any:
		# load original model
		__lowercase											=     DonutModel.from_pretrained(snake_case							).eval()

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

		__lowercase											=     original_model.state_dict()
		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case							)

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

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

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

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

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

		# verify decoder hidden states
		__lowercase											=     original_model(snake_case						,   snake_case						,   snake_case							).logits
		__lowercase											=     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__":
		SCREAMING_SNAKE_CASE_   :      Dict			=   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.''',
		)

		SCREAMING_SNAKE_CASE_   :      Tuple			=   parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




