'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE_:					Dict  =False
SCREAMING_SNAKE_CASE_:					int  =True
SCREAMING_SNAKE_CASE_:					List[str]  =False


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					List[Any]  =argparse.ArgumentParser()

    parser.add_argument(
        '--repo_path',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

    SCREAMING_SNAKE_CASE_:					Any  =parser.parse_args()

    SCREAMING_SNAKE_CASE_:					Optional[Any]  ={
        'image_size': 'sample_size',
        'num_res_blocks': 'layers_per_block',
        'block_channels': 'block_out_channels',
        'down_blocks': 'down_block_types',
        'up_blocks': 'up_block_types',
        'downscale_freq_shift': 'freq_shift',
        'resnet_num_groups': 'norm_num_groups',
        'resnet_act_fn': 'act_fn',
        'resnet_eps': 'norm_eps',
        'num_head_channels': 'attention_head_dim',
    }

    SCREAMING_SNAKE_CASE_:					List[Any]  ={
        'time_steps': 'time_proj',
        'mid': 'mid_block',
        'downsample_blocks': 'down_blocks',
        'upsample_blocks': 'up_blocks',
    }

    SCREAMING_SNAKE_CASE_:					Optional[Any]  ='' if has_file(args.repo_path, 'config.json') else 'unet'

    with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
        SCREAMING_SNAKE_CASE_:					int  =reader.read()
        SCREAMING_SNAKE_CASE_:					List[str]  =json.loads(text)

    if do_only_config:
        for key in config_parameters_to_change.keys():
            config.pop(key, None)

    if has_file(args.repo_path, 'config.json'):
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =UNetaDModel(**config)
    else:
        SCREAMING_SNAKE_CASE_:					Tuple  =UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
        SCREAMING_SNAKE_CASE_:					Any  =class_name(**config)

    if do_only_config:
        model.save_config(os.path.join(args.repo_path, subfolder))

    SCREAMING_SNAKE_CASE_:					Optional[Any]  =dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                SCREAMING_SNAKE_CASE_:					int  =config[key]
                del config[key]

        SCREAMING_SNAKE_CASE_:					Tuple  =[k.replace('UNetRes', '') for k in config['down_block_types']]
        SCREAMING_SNAKE_CASE_:					Any  =[k.replace('UNetRes', '') for k in config['up_block_types']]

    if do_only_weights:
        SCREAMING_SNAKE_CASE_:					Optional[int]  =torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

        SCREAMING_SNAKE_CASE_:					Optional[int]  ={}
        for param_key, param_value in state_dict.items():
            if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
                continue
            SCREAMING_SNAKE_CASE_:					Optional[int]  =False
            for key, new_key in key_parameters_to_change.items():
                if not has_changed and param_key.split('.')[0] == key:
                    SCREAMING_SNAKE_CASE_:					Dict  =param_value
                    SCREAMING_SNAKE_CASE_:					List[str]  =True
            if not has_changed:
                SCREAMING_SNAKE_CASE_:					List[str]  =param_value

        model.load_state_dict(new_state_dict)
        model.save_pretrained(os.path.join(args.repo_path, subfolder))





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


if __name__ == "__main__":
    print(f"{solution() = }")




