

'''simple docstring'''






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class      lowercase_      :




  """simple docstring"""




  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return self.get_dummy_input()






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   if self.block_type == "down":
    return (4, 3_2, 1_6, 1_6)
   elif self.block_type == "mid":
    return (4, 3_2, 3_2, 3_2)
   elif self.block_type == "up":
    return (4, 3_2, 6_4, 6_4)

   raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Any=True   ,lowercase__  :	Dict=False   ,lowercase__  :	Dict=False   ,lowercase__  :	Tuple=False   ,):
   __lowercase				    =					4
   __lowercase				    =					3_2
   __lowercase				    =					(3_2, 3_2)

   __lowercase				    =					torch.manual_seed(0     )
   __lowercase				    =					torch.device(lowercase__     )
   __lowercase				    =					(batch_size, num_channels) + sizes
   __lowercase				    =					randn_tensor(lowercase__   ,generator=lowercase__   ,device=lowercase__     )
   __lowercase				    =					{'''hidden_states''': hidden_states}

   if include_temb:
    __lowercase				    =					1_2_8
    __lowercase				    =					randn_tensor((batch_size, temb_channels)   ,generator=lowercase__   ,device=lowercase__     )

   if include_res_hidden_states_tuple:
    __lowercase				    =					torch.manual_seed(1     )
    __lowercase				    =					(randn_tensor(lowercase__   ,generator=lowercase__   ,device=lowercase__     ),)

   if include_encoder_hidden_states:
    __lowercase				    =					floats_tensor((batch_size, 3_2, 3_2)     ).to(lowercase__     )

   if include_skip_sample:
    __lowercase				    =					randn_tensor(((batch_size, 3) + sizes)   ,generator=lowercase__   ,device=lowercase__     )

   return dummy_input






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					{
       '''in_channels''': 3_2,
       '''out_channels''': 3_2,
       '''temb_channels''': 1_2_8,
   }
   if self.block_type == "up":
    __lowercase				    =					3_2

   if self.block_type == "mid":
    init_dict.pop('''out_channels'''     )

   __lowercase				    =					self.dummy_input
   return init_dict, inputs_dict






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	Union[str, Any]     ):
   __lowercase    ,      __lowercase				    =					self.prepare_init_args_and_inputs_for_common()
   __lowercase				    =					self.block_class(**lowercase__     )
   unet_block.to(lowercase__     )
   unet_block.eval()

   with torch.no_grad():
    __lowercase				    =					unet_block(**lowercase__     )

   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					output[0]

   self.assertEqual(output.shape   ,self.output_shape     )

   __lowercase				    =					output[0, -1, -3:, -3:]
   __lowercase				    =					torch.tensor(lowercase__     ).to(lowercase__     )
   assert torch_all_close(output_slice.flatten()   ,lowercase__   ,atol=5e-3     )






  @unittest.skipIf(torch_device == '''mps'''   ,'''Training is not supported in mps'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase    ,      __lowercase				    =					self.prepare_init_args_and_inputs_for_common()
   __lowercase				    =					self.block_class(**lowercase__     )
   model.to(lowercase__     )
   model.train()
   __lowercase				    =					model(**lowercase__     )

   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					output[0]

   __lowercase				    =					torch.device(lowercase__     )
   __lowercase				    =					randn_tensor(output.shape   ,device=lowercase__     )
   __lowercase				    =					torch.nn.functional.mse_loss(lowercase__   ,lowercase__     )
   loss.backward()



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							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.")

						_snake_case      :							Tuple     	=    parser.parse_args()

						_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",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							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:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    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      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

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

						_snake_case      :							Any     	=    dict(model.config)

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

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

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

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

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