



'''simple docstring'''

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))








'''simple docstring'''






from collections.abc import Callable

import numpy as np


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       np.array:
							lowerCAmelCase__			  =      int(np.ceil((x_end - xa) / step_size			)			)
							lowerCAmelCase__			  =      np.zeros((n + 1,)			)
							lowerCAmelCase__			  =      ya
							lowerCAmelCase__			  =      xa

							for k in range(A			):
														lowerCAmelCase__			  =      y[k] + step_size * ode_func(A					,       y[k]			)
														lowerCAmelCase__			  =      y[k] + (
														    (step_size / 2) * (ode_func(A					,       y[k]			) + ode_func(x + step_size					,       A			))
														)
														x += step_size

							return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()