



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 __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			model.config

    snake_case_				:  Optional[int]       =			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	,    )
    snake_case_				:  List[Any]       =			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 __lowercase    (							_a					):



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

        if name == "encoder.norm.weight":
            snake_case_				:  str       =			'''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            snake_case_				:  str       =			'''encoder.layernorm.bias'''

    return name




def __lowercase    (							_a	,    _a					):



    for key in orig_state_dict.copy().keys():
        snake_case_				:  int       =			orig_state_dict.pop(_a					)

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

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

    return orig_state_dict




def __lowercase    (							_a	,    _a=None	,    _a=False					):
    snake_case_				:  Tuple       =			DonutModel.from_pretrained(_a					).eval()

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

    snake_case_				:  Union[str, Any]       =			original_model.state_dict()
    snake_case_				:  Optional[int]       =			convert_state_dict(_a	,    _a					)
    model.load_state_dict(_a					)

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

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

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

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

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

    # verify decoder hidden states
    snake_case_				:  Optional[int]       =			original_model(_a	,    _a	,    _a					).logits
    snake_case_				:  Optional[int]       =			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__":
     lowercase__					:						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.''',
     )

     lowercase__					:						str												=			parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

     snake_case_				:  str       =			vocab_size
     snake_case_				:  Optional[Any]       =			hidden_size
     snake_case_				:  Any       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  Dict       =			hidden_act
     snake_case_				:  List[Any]       =			intermediate_size
     snake_case_				:  Optional[int]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			type_vocab_size
     snake_case_				:  Optional[int]       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  Optional[int]       =			position_embedding_type
     snake_case_				:  str       =			use_cache
     snake_case_				:  Tuple       =			classifier_dropout





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





