


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[Any]:
				A__  :				Optional[Any] 					=model.config

				A__  :				Union[str, 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,						)
				A__  :				List[str] 					=MBartConfig(
				    is_decoder=lowerCamelCase_,						is_encoder_decoder=lowerCamelCase_,						add_cross_attention=lowerCamelCase_,						decoder_layers=original_config.decoder_layer,						max_position_embeddings=original_config.max_position_embeddings,						vocab_size=len(
				        model.decoder.tokenizer						),						scale_embedding=lowerCamelCase_,						add_final_layer_norm=lowerCamelCase_,						)

				return encoder_config, decoder_config



def 	SCREAMING_SNAKE_CASE__    (				snake_case_						)      ->				int:





				if "encoder.model" in name:
								A__  :				List[str] 					=name.replace('''encoder.model''',						'''encoder'''						)
				if "decoder.model" in name:
								A__  :				int 					=name.replace('''decoder.model''',						'''decoder'''						)
				if "patch_embed.proj" in name:
								A__  :				Any 					=name.replace('''patch_embed.proj''',						'''embeddings.patch_embeddings.projection'''						)
				if "patch_embed.norm" in name:
								A__  :				Any 					=name.replace('''patch_embed.norm''',						'''embeddings.norm'''						)
				if name.startswith('''encoder'''						):
								if "layers" in name:
												A__  :				Any 					='encoder.' + name
								if "attn.proj" in name:
												A__  :				Dict 					=name.replace('''attn.proj''',						'''attention.output.dense'''						)
								if "attn" in name and "mask" not in name:
												A__  :				List[str] 					=name.replace('''attn''',						'''attention.self'''						)
								if "norm1" in name:
												A__  :				Union[str, Any] 					=name.replace('''norm1''',						'''layernorm_before'''						)
								if "norm2" in name:
												A__  :				List[Any] 					=name.replace('''norm2''',						'''layernorm_after'''						)
								if "mlp.fc1" in name:
												A__  :				Tuple 					=name.replace('''mlp.fc1''',						'''intermediate.dense'''						)
								if "mlp.fc2" in name:
												A__  :				Optional[Any] 					=name.replace('''mlp.fc2''',						'''output.dense'''						)

								if name == "encoder.norm.weight":
												A__  :				Union[str, Any] 					='encoder.layernorm.weight'
								if name == "encoder.norm.bias":
												A__  :				Tuple 					='encoder.layernorm.bias'

				return name



def 	SCREAMING_SNAKE_CASE__    (				snake_case_,						snake_case_						)      ->				Optional[int]:





				for key in orig_state_dict.copy().keys():
								A__  :				List[str] 					=orig_state_dict.pop(lowerCamelCase_						)

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

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

				return orig_state_dict



def 	SCREAMING_SNAKE_CASE__    (				snake_case_,						snake_case_=None,						snake_case_=False						)      ->				Optional[int]:
				A__  :				List[Any] 					=DonutModel.from_pretrained(lowerCamelCase_						).eval()

				# load HuggingFace model
				A__  :				Optional[int] 					=get_configs(lowerCamelCase_						)
				A__  :				int 					=DonutSwinModel(lowerCamelCase_						)
				A__  :				Any 					=MBartForCausalLM(lowerCamelCase_						)
				A__  :				List[str] 					=VisionEncoderDecoderModel(encoder=lowerCamelCase_,						decoder=lowerCamelCase_						)
				model.eval()

				A__  :				Union[str, Any] 					=original_model.state_dict()
				A__  :				Any 					=convert_state_dict(lowerCamelCase_,						lowerCamelCase_						)
				model.load_state_dict(lowerCamelCase_						)

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

				A__  :				List[Any] 					=XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase_,						from_slow=lowerCamelCase_						)
				A__  :				Optional[int] 					=DonutImageProcessor(
				    do_align_long_axis=original_model.config.align_long_axis,						size=original_model.config.input_size[::-1]						)
				A__  :				List[Any] 					=DonutProcessor(lowerCamelCase_,						lowerCamelCase_						)
				A__  :				Tuple 					=processor(lowerCamelCase_,						return_tensors='''pt'''						).pixel_values

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

				A__  :				int 					=original_model.encoder.model.patch_embed(lowerCamelCase_						)
				A__  :				Any 					=model.encoder.embeddings(lowerCamelCase_						)
				assert torch.allclose(lowerCamelCase_,						lowerCamelCase_,						atol=1e-3						)

				# verify encoder hidden states
				A__  :				Any 					=original_model.encoder(lowerCamelCase_						)
				A__  :				Optional[Any] 					=model.encoder(lowerCamelCase_						).last_hidden_state
				assert torch.allclose(lowerCamelCase_,						lowerCamelCase_,						atol=1e-2						)

				# verify decoder hidden states
				A__  :				Dict 					=original_model(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_						).logits
				A__  :				int 					=model(lowerCamelCase_,						decoder_input_ids=lowerCamelCase_						).logits
				assert torch.allclose(lowerCamelCase_,						lowerCamelCase_,						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(lowerCamelCase_						)
								processor.save_pretrained(lowerCamelCase_						)

				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							:							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.",
						)

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









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						UpperCamelCase__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="</s>"      ,snake_case__="<s>"      ,snake_case__="<unk>"      ,snake_case__="<pad>"      ,snake_case__="<mask>"      ,**snake_case__      ,):
							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_   : str					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else mask_token

							super().__init__(
							    snake_case__      ,tokenizer_file=snake_case__      ,bos_token=snake_case__      ,eos_token=snake_case__      ,unk_token=snake_case__      ,sep_token=snake_case__      ,cls_token=snake_case__      ,pad_token=snake_case__      ,mask_token=snake_case__      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Any					  =      [self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
										    'tokenizer.' )

							if not os.path.isdir(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
										copyfile(self.vocab_file      ,snake_case__ )

							return (out_vocab_file,)



