

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     UNetaDModel(**SCREAMING_SNAKE_CASE )
    __UpperCAmelCase			   =     checkpoint['''all_modules.0.W'''].data
    __UpperCAmelCase			   =     checkpoint['''all_modules.0.W'''].data
    __UpperCAmelCase			   =     checkpoint['''all_modules.1.weight'''].data
    __UpperCAmelCase			   =     checkpoint['''all_modules.1.bias'''].data

    __UpperCAmelCase			   =     checkpoint['''all_modules.2.weight'''].data
    __UpperCAmelCase			   =     checkpoint['''all_modules.2.bias'''].data

    __UpperCAmelCase			   =     checkpoint['''all_modules.3.weight'''].data
    __UpperCAmelCase			   =     checkpoint['''all_modules.3.bias'''].data

    __UpperCAmelCase			   =     checkpoint[list(checkpoint.keys() )[-4]].data
    __UpperCAmelCase			   =     checkpoint[list(checkpoint.keys() )[-3]].data
    __UpperCAmelCase			   =     checkpoint[list(checkpoint.keys() )[-2]].data
    __UpperCAmelCase			   =     checkpoint[list(checkpoint.keys() )[-1]].data

    __UpperCAmelCase			   =     4

    def set_attention_weights(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ):
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

    def set_resnet_weights(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ):
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
        __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

        if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
            __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
            __UpperCAmelCase			   =     old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

    for i, block in enumerate(new_model_architecture.downsample_blocks ):
        __UpperCAmelCase			   =     hasattr(SCREAMING_SNAKE_CASE    ,				'''attentions''' )
        for j in range(len(block.resnets ) ):
            set_resnet_weights(block.resnets[j]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
            module_index += 1
            if has_attentions:
                set_attention_weights(block.attentions[j]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
                module_index += 1

        if hasattr(SCREAMING_SNAKE_CASE    ,				'''downsamplers''' ) and block.downsamplers is not None:
            set_resnet_weights(block.resnet_down    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
            module_index += 1
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
            module_index += 1

    set_resnet_weights(new_model_architecture.mid_block.resnets[0]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
    module_index += 1

    for i, block in enumerate(new_model_architecture.up_blocks ):
        __UpperCAmelCase			   =     hasattr(SCREAMING_SNAKE_CASE    ,				'''attentions''' )
        for j in range(len(block.resnets ) ):
            set_resnet_weights(block.resnets[j]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
            module_index += 1
        if has_attentions:
            set_attention_weights(
                block.attentions[0]    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )  # why can there only be a single attention layer for up?
            module_index += 1

        if hasattr(SCREAMING_SNAKE_CASE    ,				'''resnet_up''' ) and block.resnet_up is not None:
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
            module_index += 1
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
            __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
            module_index += 1
            set_resnet_weights(block.resnet_up    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
            module_index += 1

    __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
    __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.bias'''].data
    module_index += 1
    __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.weight'''].data
    __UpperCAmelCase			   =     checkpoint[f'''all_modules.{module_index}.bias'''].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
       A_						:     str			  =				argparse.ArgumentParser()

       parser.add_argument(
           '--checkpoint_path',
           default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
           type=str,
           required=False,
           help='Path to the checkpoint to convert.',
       )

       parser.add_argument(
           '--config_file',
           default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
           type=str,
           required=False,
           help='The config json file corresponding to the architecture.',
       )

       parser.add_argument(
           '--dump_path',
           default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
           type=str,
           required=False,
           help='Path to the output model.',
       )

       A_						:     Optional[Any]			  =				parser.parse_args()

       A_						:     List[Any]			  =				torch.load(args.checkpoint_path, map_location='cpu')

       with open(args.config_file) as f:
              A_						:     List[str]			  =				json.loads(f.read())

       A_						:     str			  =				convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )

       if "sde" in config:
              del config["sde"]

       A_						:     Any			  =				UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              A_						:     List[str]			  =				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

              A_						:     Dict			  =				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/




