

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a):
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         lowercase__   :       str				=	DDIMScheduler.from_config(scheduler.config)

         self.register_modules(unet=a						,       scheduler=a)





    @torch.no_grad()
    def __call__(				self						,       a = 1						,       a = None						,       a = 0.0						,       a = 50						,       a = None						,       a = "pil"						,       a = True						,       ):

         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size						,       a):
              lowercase__   :       Dict				=	(
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size,
                  self.unet.config.sample_size,
              )
         else:
              lowercase__   :       List[str]				=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(a						,       a) and len(a) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(a)}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

         lowercase__   :       Dict				=	randn_tensor(a						,       generator=a						,       device=self.device						,       dtype=self.unet.dtype)

         # set step values
         self.scheduler.set_timesteps(a)

         for t in self.progress_bar(self.scheduler.timesteps):
              # 1. predict noise model_output
              lowercase__   :       Optional[Any]				=	self.unet(a						,       a).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowercase__   :       Dict				=	self.scheduler.step(
                  a						,       a						,       a						,       eta=a						,       use_clipped_model_output=a						,       generator=a).prev_sample

         lowercase__   :       Any				=	(image / 2 + 0.5).clamp(0						,       1)
         lowercase__   :       Dict				=	image.cpu().permute(0						,       2						,       3						,       1).numpy()
         if output_type == "pil":
              lowercase__   :       Optional[Any]				=	self.numpy_to_pil(a)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a)




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			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.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''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''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' 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:
              snake_case_        =			reader.read()
              snake_case_        =			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'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

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

       snake_case_        =			dict(model.config)

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

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

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

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

