


'''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      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Dict        =     model.config

			UpperCAmelCase__  :					Optional[Any]        =     DonutSwinConfig(
			    image_size=original_config.input_size ,      patch_size=4 ,      depths=original_config.encoder_layer ,      num_heads=[4, 8, 1_6, 3_2] ,      window_size=original_config.window_size ,      embed_dim=1_2_8 ,      )
			UpperCAmelCase__  :					str        =     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      (		UpperCamelCase__					):
			if "encoder.model" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""encoder.model""" ,      """encoder"""					)
			if "decoder.model" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""decoder.model""" ,      """decoder"""					)
			if "patch_embed.proj" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""patch_embed.proj""" ,      """embeddings.patch_embeddings.projection"""					)
			if "patch_embed.norm" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""patch_embed.norm""" ,      """embeddings.norm"""					)
			if name.startswith("""encoder"""					):
						if "layers" in name:
									UpperCAmelCase__  :					Optional[int]        =     """encoder.""" + name
						if "attn.proj" in name:
									UpperCAmelCase__  :					int        =     name.replace("""attn.proj""" ,      """attention.output.dense"""					)
						if "attn" in name and "mask" not in name:
									UpperCAmelCase__  :					str        =     name.replace("""attn""" ,      """attention.self"""					)
						if "norm1" in name:
									UpperCAmelCase__  :					Dict        =     name.replace("""norm1""" ,      """layernorm_before"""					)
						if "norm2" in name:
									UpperCAmelCase__  :					str        =     name.replace("""norm2""" ,      """layernorm_after"""					)
						if "mlp.fc1" in name:
									UpperCAmelCase__  :					List[Any]        =     name.replace("""mlp.fc1""" ,      """intermediate.dense"""					)
						if "mlp.fc2" in name:
									UpperCAmelCase__  :					List[Any]        =     name.replace("""mlp.fc2""" ,      """output.dense"""					)

						if name == "encoder.norm.weight":
									UpperCAmelCase__  :					Optional[Any]        =     """encoder.layernorm.weight"""
						if name == "encoder.norm.bias":
									UpperCAmelCase__  :					Tuple        =     """encoder.layernorm.bias"""

			return name







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__  :					Optional[Any]        =     orig_state_dict.pop(UpperCamelCase__					)

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

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

			return orig_state_dict







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=None ,      UpperCamelCase__=False					):
			# load original model
			UpperCAmelCase__  :					Dict        =     DonutModel.from_pretrained(UpperCamelCase__					).eval()

			# load HuggingFace model
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     get_configs(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     DonutSwinModel(UpperCamelCase__					)
			UpperCAmelCase__  :					str        =     MBartForCausalLM(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     VisionEncoderDecoderModel(encoder=UpperCamelCase__ ,      decoder=UpperCamelCase__					)
			model.eval()

			UpperCAmelCase__  :					Any        =     original_model.state_dict()
			UpperCAmelCase__  :					Optional[Any]        =     convert_state_dict(UpperCamelCase__ ,      UpperCamelCase__					)
			model.load_state_dict(UpperCamelCase__					)

			# verify results on scanned document
			UpperCAmelCase__  :					Tuple        =     load_dataset("""hf-internal-testing/example-documents"""					)
			UpperCAmelCase__  :					Union[str, Any]        =     dataset["""test"""][0]["""image"""].convert("""RGB"""					)

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

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

			UpperCAmelCase__  :					List[Any]        =     original_model.encoder.model.patch_embed(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     model.encoder.embeddings(UpperCamelCase__					)
			assert torch.allclose(UpperCamelCase__ ,      UpperCamelCase__ ,      atol=1e-3					)

			# verify encoder hidden states
			UpperCAmelCase__  :					str        =     original_model.encoder(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     model.encoder(UpperCamelCase__					).last_hidden_state
			assert torch.allclose(UpperCamelCase__ ,      UpperCamelCase__ ,      atol=1e-2					)

			# verify decoder hidden states
			UpperCAmelCase__  :					Tuple        =     original_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					).logits
			UpperCAmelCase__  :					Tuple        =     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	=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	=parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

								super().__init__(_lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=_lowerCamelCase)])




					@property
					def 				snake_case__						(      self):
								return self.base_dist.mean * self.scale + self.loc




					@property
					def 				snake_case__						(      self):
								return self.base_dist.variance * self.scale**2




					@property
					def 				snake_case__						(      self):
								return self.variance.sqrt()


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




					def __init__(      self							,							_lowerCamelCase = 1):
								UpperCAmelCase__  :					Optional[Any]        =     dim
								UpperCAmelCase__  :					int        =     {k: dim * self.args_dim[k] for k in self.args_dim}




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




					@property
					def 				snake_case__						(      self):
								return () if self.dim == 1 else (self.dim,)




					@property
					def 				snake_case__						(      self):
								return len(self.event_shape)




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

								if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

								return self._base_distribution((total_count, logits))