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 						A__  (					__A	):







    '''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     :		Tuple 						=						MBartConfig(
        is_decoder=__A   , is_encoder_decoder=__A   , add_cross_attention=__A   , decoder_layers=original_config.decoder_layer   , max_position_embeddings=original_config.max_position_embeddings   , vocab_size=len(
            model.decoder.tokenizer	)   , scale_embedding=__A   , add_final_layer_norm=__A   , )

    return encoder_config, decoder_config







def 						A__  (					__A	):







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

        if name == "encoder.norm.weight":
            _lowerCamelCase     :		List[str] 						=						"""encoder.layernorm.weight"""
        if name == "encoder.norm.bias":
            _lowerCamelCase     :		Tuple 						=						"""encoder.layernorm.bias"""

    return name







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    for key in orig_state_dict.copy().keys():
        _lowerCamelCase     :		str 						=						orig_state_dict.pop(__A	)

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

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

    return orig_state_dict







def 						A__  (					__A   , __A=None   , __A=False	):







    '''simple docstring'''
    # load original model
    _lowerCamelCase     :		int 						=						DonutModel.from_pretrained(__A	).eval()

    # load HuggingFace model
    _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_configs(__A	)
    _lowerCamelCase     :		Tuple 						=						DonutSwinModel(__A	)
    _lowerCamelCase     :		str 						=						MBartForCausalLM(__A	)
    _lowerCamelCase     :		Dict 						=						VisionEncoderDecoderModel(encoder=__A   , decoder=__A	)
    model.eval()

    _lowerCamelCase     :		List[str] 						=						original_model.state_dict()
    _lowerCamelCase     :		str 						=						convert_state_dict(__A   , __A	)
    model.load_state_dict(__A	)

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

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

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

    _lowerCamelCase     :		Optional[int] 						=						original_model.encoder.model.patch_embed(__A	)
    _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						model.encoder.embeddings(__A	)
    assert torch.allclose(__A   , __A   , atol=1E-3	)

    # verify encoder hidden states
    _lowerCamelCase     :		Any 						=						original_model.encoder(__A	)
    _lowerCamelCase     :		Tuple 						=						model.encoder(__A	).last_hidden_state
    assert torch.allclose(__A   , __A   , atol=1E-2	)

    # verify decoder hidden states
    _lowerCamelCase     :		Any 						=						original_model(__A   , __A   , __A	).logits
    _lowerCamelCase     :		Optional[Any] 						=						model(__A   , decoder_input_ids=__A	).logits
    assert torch.allclose(__A   , __A   , 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(__A	)
        processor.save_pretrained(__A	)

    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			:							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)





def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						0
    for ch in input_str:
        _lowerCamelCase     :		Optional[Any] 						=						ord(__A	)
        _lowerCamelCase     :		List[str] 						=						pow(2   , __A	)

        # If we already turned on bit for current character's unicode
        if bitmap >> ch_unicode & 1 == 1:
            return False
        bitmap |= ch_bit_index_on
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()




