


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 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				Union[str, Any] 						=						model.config

    _lowercase			:				List[str] 						=						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			:				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 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    if "encoder.model" in name:
        _lowercase			:				Tuple 						=						name.replace('encoder.model'				,					'encoder'					)
    if "decoder.model" in name:
        _lowercase			:				int 						=						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			:				Optional[int] 						=						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			:				Tuple 						=						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			:				Tuple 						=						name.replace('norm1'				,					'layernorm_before'					)
        if "norm2" in name:
            _lowercase			:				List[str] 						=						name.replace('norm2'				,					'layernorm_after'					)
        if "mlp.fc1" in name:
            _lowercase			:				str 						=						name.replace('mlp.fc1'				,					'intermediate.dense'					)
        if "mlp.fc2" in name:
            _lowercase			:				List[Any] 						=						name.replace('mlp.fc2'				,					'output.dense'					)

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

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		List[Any]:
    for key in orig_state_dict.copy().keys():
        _lowercase			:				List[Any] 						=						orig_state_dict.pop(lowerCamelCase_					)

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

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

    return orig_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=False					)       ->		int:
    # load original model
    _lowercase			:				Dict 						=						DonutModel.from_pretrained(lowerCamelCase_					).eval()

    # load HuggingFace model
    _lowercase						,							_lowercase			:				Union[str, Any] 						=						get_configs(lowerCamelCase_					)
    _lowercase			:				Tuple 						=						DonutSwinModel(lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						MBartForCausalLM(lowerCamelCase_					)
    _lowercase			:				int 						=						VisionEncoderDecoderModel(encoder=lowerCamelCase_				,					decoder=lowerCamelCase_					)
    model.eval()

    _lowercase			:				str 						=						original_model.state_dict()
    _lowercase			:				int 						=						convert_state_dict(lowerCamelCase_				,					lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)

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

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

    if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
        _lowercase			:				Tuple 						=						'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        _lowercase			:				Union[str, Any] 						=						'When is the coffee break?'
        _lowercase			:				Tuple 						=						task_prompt.replace('{user_input}'				,					lowerCamelCase_					)
    elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
        _lowercase			:				Dict 						=						'<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			:				Any 						=						'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			:				Optional[Any] 						=						'hello world'
    else:
        raise ValueError('Model name not supported'					)
    _lowercase			:				Optional[int] 						=						original_model.decoder.tokenizer(lowerCamelCase_				,					add_special_tokens=lowerCamelCase_				,					return_tensors='pt'					)[
        'input_ids'
    ]

    _lowercase			:				Optional[Any] 						=						original_model.encoder.model.patch_embed(lowerCamelCase_					)
    _lowercase						,							_lowercase			:				List[str] 						=						model.encoder.embeddings(lowerCamelCase_					)
    assert torch.allclose(lowerCamelCase_				,					lowerCamelCase_				,					atol=1e-3					)

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

    # verify decoder hidden states
    _lowercase			:				int 						=						original_model(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					).logits
    _lowercase			:				str 						=						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__":
 SCREAMING_SNAKE_CASE			: List[str]				   =					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.",
 )

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










"""simple docstring"""
import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


_lowerCAmelCase       =				"\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


_lowerCAmelCase       =				"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


_lowerCAmelCase       =				r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION    )
class   SCREAMING_SNAKE_CASE	(					datasets.Metric    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		return datasets.MetricInfo(
		    description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string"""				),
		            """references""": datasets.Value("""string"""				),
		        }				)  ,  homepage="""https://github.com/hendrycks/math"""  ,  codebase_urls=["""https://github.com/hendrycks/math"""]  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = 0.0
		for i, j in zip(lowerCAmelCase_  ,  lowerCAmelCase_				):
			n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_  ,  lowerCAmelCase_				) else 0.0
		A_		:    List[str]						     = n_correct / len(lowerCAmelCase_				)
		return {
		    "accuracy": accuracy,
		}

