





'''simple docstring'''





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_  							=				logging.get_logger(__name__)






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							str     	=	field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()       )}       )
   a		:							str     	=	field(
       metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}       )
   a		:							int     	=	field(
       default=128    ,       metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }    ,       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Overwrite the cached training and evaluation sets"}       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.task_name.lower()






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Tuple     	=	"train"
   a		:							Optional[Any]     	=	"dev"
   a		:							Dict     	=	"test"






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							GlueDataTrainingArguments
   a		:							str
   a		:							List[InputFeatures]




   def __init__(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = Split.train,	__magic_name__ = None,	)				->   List[str]:






       """simple docstring"""
       warnings.warn(
           '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
           '''library. You can have a look at this example script for pointers: '''
           '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''',	__magic_name__,	)
       UpperCamelCase__							:       List[Any]      =   args
       UpperCamelCase__							:       List[Any]      =   glue_processors[args.task_name]()
       UpperCamelCase__							:       Optional[int]      =   glue_output_modes[args.task_name]
       if isinstance(__magic_name__,	__magic_name__							):
           try:
               UpperCamelCase__							:       List[str]      =   Split[mode]
           except KeyError:
               raise KeyError('''mode is not a valid split name'''							)
        # Load data features from cache or dataset file
       UpperCamelCase__							:       Union[str, Any]      =   os.path.join(
           cache_dir if cache_dir is not None else args.data_dir,	f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",	)
       UpperCamelCase__							:       Tuple      =   self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
           # HACK(label indices are swapped in RoBERTa pretrained model)
           UpperCamelCase__							:       int      =   label_list[2], label_list[1]
       UpperCamelCase__							:       Dict      =   label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       UpperCamelCase__							:       List[Any]      =   cached_features_file + '''.lock'''
       with FileLock(__magic_name__							):
           if os.path.exists(__magic_name__							) and not args.overwrite_cache:
               UpperCamelCase__							:       Union[str, Any]      =   time.time()
               UpperCamelCase__							:       List[str]      =   torch.load(__magic_name__							)
               logger.info(
                   f"Loading features from cached file {cached_features_file} [took %.3f s]",	time.time() - start							)
           else:
               logger.info(f"Creating features from dataset file at {args.data_dir}"							)

               if mode == Split.dev:
                   UpperCamelCase__							:       Tuple      =   self.processor.get_dev_examples(args.data_dir							)
               elif mode == Split.test:
                   UpperCamelCase__							:       List[Any]      =   self.processor.get_test_examples(args.data_dir							)
               else:
                   UpperCamelCase__							:       str      =   self.processor.get_train_examples(args.data_dir							)
               if limit_length is not None:
                   UpperCamelCase__							:       Union[str, Any]      =   examples[:limit_length]
               UpperCamelCase__							:       List[Any]      =   glue_convert_examples_to_features(
                   __magic_name__,	__magic_name__,	max_length=args.max_seq_length,	label_list=__magic_name__,	output_mode=self.output_mode,	)
               UpperCamelCase__							:       Tuple      =   time.time()
               torch.save(self.features,	__magic_name__							)
               # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
               logger.info(
                   f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"							)




   def __len__(					self							)				->   Tuple:






       """simple docstring"""
       return len(self.features							)




   def __getitem__(					self,	__magic_name__							)				->   InputFeatures:






       """simple docstring"""
       return self.features[i]




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       return self.label_list








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 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Union[str, Any]:
    UpperCamelCase__							:       List[str]      =   model.config

    UpperCamelCase__							:       Optional[Any]      =   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	,     )
    UpperCamelCase__							:       Union[str, Any]      =   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 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			Tuple:
    if "encoder.model" in name:
        UpperCamelCase__							:       str      =   name.replace('''encoder.model'''	,     '''encoder'''   )
    if "decoder.model" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''decoder.model'''	,     '''decoder'''   )
    if "patch_embed.proj" in name:
        UpperCamelCase__							:       Dict      =   name.replace('''patch_embed.proj'''	,     '''embeddings.patch_embeddings.projection'''   )
    if "patch_embed.norm" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''patch_embed.norm'''	,     '''embeddings.norm'''   )
    if name.startswith('''encoder'''   ):
        if "layers" in name:
            UpperCamelCase__							:       Optional[Any]      =   '''encoder.''' + name
        if "attn.proj" in name:
            UpperCamelCase__							:       Optional[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__							:       List[str]      =   name.replace('''norm1'''	,     '''layernorm_before'''   )
        if "norm2" in name:
            UpperCamelCase__							:       Union[str, Any]      =   name.replace('''norm2'''	,     '''layernorm_after'''   )
        if "mlp.fc1" in name:
            UpperCamelCase__							:       Any      =   name.replace('''mlp.fc1'''	,     '''intermediate.dense'''   )
        if "mlp.fc2" in name:
            UpperCamelCase__							:       List[str]      =   name.replace('''mlp.fc2'''	,     '''output.dense'''   )

        if name == "encoder.norm.weight":
            UpperCamelCase__							:       Optional[Any]      =   '''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            UpperCamelCase__							:       Any      =   '''encoder.layernorm.bias'''

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			Dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Dict      =   orig_state_dict.pop(__UpperCAmelCase   )

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

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

    return orig_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[Any]=None	,     __UpperCAmelCase:    Tuple=False   )   ->			Optional[int]:
    # load original model
    UpperCamelCase__							:       List[Any]      =   DonutModel.from_pretrained(__UpperCAmelCase   ).eval()

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

    UpperCamelCase__							:       List[Any]      =   original_model.state_dict()
    UpperCamelCase__							:       List[Any]      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

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

    UpperCamelCase__							:       Optional[Any]      =   XLMRobertaTokenizerFast.from_pretrained(__UpperCAmelCase	,     from_slow=__UpperCAmelCase   )
    UpperCamelCase__							:       Dict      =   DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis	,     size=original_model.config.input_size[::-1]   )
    UpperCamelCase__							:       Any      =   DonutProcessor(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       str      =   processor(__UpperCAmelCase	,     return_tensors='''pt'''   ).pixel_values

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

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

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

    # verify decoder hidden states
    UpperCamelCase__							:       Optional[Any]      =   original_model(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   ).logits
    UpperCamelCase__							:       str      =   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__":
  UpperCAmelCase_  							=				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.',
  )

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

