

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__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





     lowercase__   :       str				=	model.config

     lowercase__   :       Tuple				=	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__   :       Union[str, Any]				=	MBartConfig(
         is_decoder=SCREAMING_SNAKE_CASE_     ,					is_encoder_decoder=SCREAMING_SNAKE_CASE_     ,					add_cross_attention=SCREAMING_SNAKE_CASE_     ,					decoder_layers=original_config.decoder_layer     ,					max_position_embeddings=original_config.max_position_embeddings     ,					vocab_size=len(
             model.decoder.tokenizer      )     ,					scale_embedding=SCREAMING_SNAKE_CASE_     ,					add_final_layer_norm=SCREAMING_SNAKE_CASE_     ,					)

     return encoder_config, decoder_config

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





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

          if name == "encoder.norm.weight":
               lowercase__   :       Any				=	'encoder.layernorm.weight'
          if name == "encoder.norm.bias":
               lowercase__   :       Tuple				=	'encoder.layernorm.bias'

     return name

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





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

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

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

     return orig_state_dict

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Any=None     ,					SCREAMING_SNAKE_CASE_		:   Any=False      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	DonutModel.from_pretrained(SCREAMING_SNAKE_CASE_      ).eval()

     # load HuggingFace model
     lowercase__				,			lowercase__   :       Optional[int]				=	get_configs(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Dict				=	DonutSwinModel(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[int]				=	MBartForCausalLM(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       str				=	VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE_     ,					decoder=SCREAMING_SNAKE_CASE_      )
     model.eval()

     lowercase__   :       List[str]				=	original_model.state_dict()
     lowercase__   :       List[Any]				=	convert_state_dict(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

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

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

     lowercase__   :       Optional[Any]				=	original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE_      )
     lowercase__				,			lowercase__   :       Optional[Any]				=	model.encoder.embeddings(SCREAMING_SNAKE_CASE_      )
     assert torch.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					atol=1E-3      )

     # verify encoder hidden states
     lowercase__   :       Tuple				=	original_model.encoder(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[Any]				=	model.encoder(SCREAMING_SNAKE_CASE_      ).last_hidden_state
     assert torch.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					atol=1E-2      )

     # verify decoder hidden states
     lowercase__   :       List[str]				=	original_model(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ).logits
     lowercase__   :       Dict				=	model(SCREAMING_SNAKE_CASE_     ,					decoder_input_ids=SCREAMING_SNAKE_CASE_      ).logits
     assert torch.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_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(SCREAMING_SNAKE_CASE_      )
          processor.save_pretrained(SCREAMING_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__":
       snake_case_        =			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.''',
       )

       snake_case_        =			parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

