





'''simple docstring'''





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger(__name__)


UpperCAmelCase_  							=				'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_  							=				{
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Union[str, Any]:
    if key.endswith('''.model.1.bias'''   ) and len(key.split('''.'''   )   ) > 10:
        UpperCamelCase__							:       Optional[Any]      =   key.replace('''.model.1.bias'''	,     '''.conv1d_1.bias'''   )
    elif key.endswith('''.model.1.weight'''   ) and len(key.split('''.'''   )   ) > 10:
        UpperCamelCase__							:       str      =   key.replace('''.model.1.weight'''	,     '''.conv1d_1.weight'''   )
    elif key.endswith('''.model.3.bias'''   ) and len(key.split('''.'''   )   ) > 10:
        UpperCamelCase__							:       List[str]      =   key.replace('''.model.3.bias'''	,     '''.conv1d_2.bias'''   )
    elif key.endswith('''.model.3.weight'''   ) and len(key.split('''.'''   )   ) > 10:
        UpperCamelCase__							:       Optional[Any]      =   key.replace('''.model.3.weight'''	,     '''.conv1d_2.weight'''   )

    if "conditioner_blocks.0." in key:
        UpperCamelCase__							:       int      =   key.replace('''conditioner_blocks.0'''	,     '''conditioner_blocks'''   )

    if "prime_prior" in key:
        UpperCamelCase__							:       Tuple      =   key.replace('''prime_prior'''	,     '''encoder'''   )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        UpperCamelCase__							:       Optional[Any]      =   key.replace('''.emb.'''	,     '''.'''   )

    if key.endswith('''k'''   ):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace('''.k'''	,     '''.codebook'''   )
    if "y_emb." in key:
        return key.replace('''y_emb.'''	,     '''metadata_embedding.'''   )

    if "x_emb.emb." in key:
        UpperCamelCase__							:       List[Any]      =   key.replace('''0.x_emb.emb'''	,     '''embed_tokens'''   )

    if "prime_state_ln" in key:
        return key.replace('''prime_state_ln'''	,     '''encoder.final_layer_norm'''   )
    if ".ln" in key:
        return key.replace('''.ln'''	,     '''.layer_norm'''   )
    if "_ln" in key:
        return key.replace('''_ln'''	,     '''_layer_norm'''   )

    if "prime_state_proj" in key:
        return key.replace('''prime_state_proj'''	,     '''encoder.proj_in'''   )
    if "prime_x_out" in key:
        return key.replace('''prime_x_out'''	,     '''encoder.lm_head'''   )
    if "prior.x_out" in key:
        return key.replace('''x_out'''	,     '''fc_proj_out'''   )
    if "x_emb" in key:
        return key.replace('''x_emb'''	,     '''embed_tokens'''   )

    return key




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Optional[int]   )   ->			List[str]:
    UpperCamelCase__							:       Dict      =   {}
    import re

    UpperCamelCase__							:       List[str]      =   re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
    UpperCamelCase__							:       Union[str, Any]      =   re.compile(
        r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
    UpperCamelCase__							:       Any      =   re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

    UpperCamelCase__							:       List[str]      =   re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
    UpperCamelCase__							:       str      =   re.compile(
        r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
    UpperCamelCase__							:       Optional[int]      =   re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

    UpperCamelCase__							:       Tuple      =   re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''   )
    UpperCamelCase__							:       List[Any]      =   re.compile(
        r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
    UpperCamelCase__							:       Optional[Any]      =   re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''   )

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       List[Any]      =   re_encoder_block_conv_in.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Tuple      =   regex_match.groups()
            UpperCamelCase__							:       List[Any]      =   int(groups[2]   ) * 2 + int(groups[3]   )
            UpperCamelCase__							:       List[str]      =   f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
            UpperCamelCase__							:       Dict      =   re_encoder_block_conv_in.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_encoder_block_resnet.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       List[Any]      =   re_encoder_block_resnet.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Tuple      =   regex_match.groups()
            UpperCamelCase__							:       str      =   int(groups[2]   ) * 2 + int(groups[3]   )
            UpperCamelCase__							:       Optional[int]      =   {'''1''': 1, '''3''': 2}[groups[-2]]
            UpperCamelCase__							:       int      =   f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
            UpperCamelCase__							:       str      =   f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            UpperCamelCase__							:       Optional[Any]      =   prefix + resnet_block
            UpperCamelCase__							:       Optional[int]      =   re_encoder_block_resnet.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_encoder_block_proj_out.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Any      =   re_encoder_block_proj_out.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Any      =   regex_match.groups()
            UpperCamelCase__							:       Optional[int]      =   f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
            UpperCamelCase__							:       int      =   re_encoder_block_proj_out.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Tuple      =   re_decoder_block_conv_out.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Dict      =   regex_match.groups()
            UpperCamelCase__							:       List[str]      =   int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            UpperCamelCase__							:       Union[str, Any]      =   f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
            UpperCamelCase__							:       Optional[Any]      =   re_decoder_block_conv_out.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_decoder_block_resnet.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Tuple      =   re_decoder_block_resnet.match(__UpperCAmelCase   )
            UpperCamelCase__							:       str      =   regex_match.groups()
            UpperCamelCase__							:       Optional[Any]      =   int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            UpperCamelCase__							:       Any      =   {'''1''': 1, '''3''': 2}[groups[-2]]
            UpperCamelCase__							:       Union[str, Any]      =   f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
            UpperCamelCase__							:       Dict      =   f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            UpperCamelCase__							:       List[Any]      =   prefix + resnet_block
            UpperCamelCase__							:       Dict      =   re_decoder_block_resnet.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_decoder_block_proj_in.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Dict      =   re_decoder_block_proj_in.match(__UpperCAmelCase   )
            UpperCamelCase__							:       int      =   regex_match.groups()
            UpperCamelCase__							:       str      =   f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
            UpperCamelCase__							:       List[str]      =   re_decoder_block_proj_in.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Any      =   re_prior_cond_conv_out.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   regex_match.groups()
            UpperCamelCase__							:       Any      =   int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            UpperCamelCase__							:       Any      =   f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
            UpperCamelCase__							:       Optional[int]      =   re_prior_cond_conv_out.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_prior_cond_resnet.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Any      =   re_prior_cond_resnet.match(__UpperCAmelCase   )
            UpperCamelCase__							:       List[str]      =   regex_match.groups()
            UpperCamelCase__							:       str      =   int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            UpperCamelCase__							:       Any      =   {'''1''': 1, '''3''': 2}[groups[-2]]
            UpperCamelCase__							:       Tuple      =   f"conditioner_blocks.upsampler.upsample_block.{block_index}."
            UpperCamelCase__							:       List[Any]      =   f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
            UpperCamelCase__							:       Dict      =   prefix + resnet_block
            UpperCamelCase__							:       List[str]      =   re_prior_cond_resnet.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        elif re_prior_cond_proj_in.fullmatch(__UpperCAmelCase   ):
            UpperCamelCase__							:       Union[str, Any]      =   re_prior_cond_proj_in.match(__UpperCAmelCase   )
            UpperCamelCase__							:       Dict      =   regex_match.groups()
            UpperCamelCase__							:       List[str]      =   f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
            UpperCamelCase__							:       Optional[int]      =   re_prior_cond_proj_in.sub(__UpperCAmelCase	,     __UpperCAmelCase   )

        # keep original key
        else:
            UpperCamelCase__							:       List[str]      =   original_key

        UpperCamelCase__							:       Tuple      =   replace_key(__UpperCAmelCase   )

        if f"{key_prefix}.{key}" not in model_state_dict or key is None:
            print(f"failed converting {original_key} to {key}, does not match"   )

        # handle missmatched shape
        elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
            UpperCamelCase__							:       Any      =   model_state_dict[f"{key_prefix}.{key}"]
            print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"   )
            UpperCamelCase__							:       Optional[int]      =   original_key

        UpperCamelCase__							:       Any      =   original_key
        UpperCamelCase__							:       Union[str, Any]      =   value

    return new_dict




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str=None	,     __UpperCAmelCase:    List[Any]=None   )   ->			int:
    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"   ):
            UpperCamelCase__							:       Tuple      =   requests.get(f"{PREFIX}{file}"	,     allow_redirects=__UpperCAmelCase   )
            os.makedirs(f"{pytorch_dump_folder_path}/"	,     exist_ok=__UpperCAmelCase   )
            open(f"{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"	,     '''wb'''   ).write(r.content   )

    UpperCamelCase__							:       Union[str, Any]      =   MODEL_MAPPING[model_name.split('''/'''   )[-1]]

    UpperCamelCase__							:       List[str]      =   JukeboxConfig.from_pretrained(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   JukeboxModel(__UpperCAmelCase   )

    UpperCamelCase__							:       List[Any]      =   []
    UpperCamelCase__							:       List[Any]      =   {}
    for i, dict_name in enumerate(__UpperCAmelCase   ):
        UpperCamelCase__							:       Dict      =   torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"   )['''model''']

        UpperCamelCase__							:       Optional[Any]      =   {}
        for k in old_dic.keys():
            if k.endswith('''.b'''   ):
                UpperCamelCase__							:       List[str]      =   old_dic[k]
            elif k.endswith('''.w'''   ):
                UpperCamelCase__							:       List[Any]      =   old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                UpperCamelCase__							:       int      =   old_dic[k]
            else:
                UpperCamelCase__							:       Tuple      =   old_dic[k]

        UpperCamelCase__							:       List[Any]      =   '''vqvae''' if i == 0 else f"priors.{3 - i}"
        UpperCamelCase__							:       Optional[int]      =   fix_jukebox_keys(__UpperCAmelCase	,     model.state_dict()	,     __UpperCAmelCase	,     __UpperCAmelCase   )
        weight_dict.append(__UpperCAmelCase   )

    UpperCamelCase__							:       List[str]      =   weight_dict.pop(0   )
    model.vqvae.load_state_dict(__UpperCAmelCase   )
    for i in range(len(__UpperCAmelCase   )   ):
        model.priors[i].load_state_dict(weight_dict[2 - i]   )

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    with open(f"{pytorch_dump_folder_path}/mapping.json"	,     '''w'''   ) as txtfile:
        json.dump(__UpperCAmelCase	,     __UpperCAmelCase   )

    print(f"Saving model {model_name} to {pytorch_dump_folder_path}"   )
    model.save_pretrained(__UpperCAmelCase   )

    return weight_dict


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='jukebox-5b-lyrics',
      type=str,
      help='Name of the model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='jukebox-5b-lyrics-converted',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








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)

