

"""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 	lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    model.config

    _lowerCAmelCase       :   List[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 ,   )
    _lowerCAmelCase       :   List[Any]  =    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 	lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

        if name == "encoder.norm.weight":
            _lowerCAmelCase       :   str  =    """encoder.layernorm.weight"""
        if name == "encoder.norm.bias":
            _lowerCAmelCase       :   Dict  =    """encoder.layernorm.bias"""

    return name

def 	lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



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

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

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

    return orig_state_dict

def 	lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    DonutModel.from_pretrained(_lowerCamelCase   ).eval()

    # load HuggingFace model
    _lowerCAmelCase       :   List[str]  =    get_configs(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    DonutSwinModel(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    MBartForCausalLM(_lowerCamelCase   )
    _lowerCAmelCase       :   Any  =    VisionEncoderDecoderModel(encoder=_lowerCamelCase ,   decoder=_lowerCamelCase   )
    model.eval()

    _lowerCAmelCase       :   List[Any]  =    original_model.state_dict()
    _lowerCAmelCase       :   List[Any]  =    convert_state_dict(_lowerCamelCase ,   _lowerCamelCase   )
    model.load_state_dict(_lowerCamelCase   )

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

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

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

    _lowerCAmelCase       :   Tuple  =    original_model.encoder.model.patch_embed(_lowerCamelCase   )
    _lowerCAmelCase       :   str  =    model.encoder.embeddings(_lowerCamelCase   )
    assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1e-3   )

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

    # verify decoder hidden states
    _lowerCAmelCase       :   int  =    original_model(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ).logits
    _lowerCAmelCase       :   Dict  =    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						=		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						=		parser.parse_args()
 convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""






import baseaa

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaaencode(string.encode('utf-8'   )   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaadecode(_lowerCamelCase   ).decode('utf-8'   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





