




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowerCamelCase      :  List[str]      						=						False
_lowerCamelCase      :  int      						=						True
_lowerCamelCase      :  List[str]      						=						False


if __name__ == "__main__":
	_lowerCamelCase      :  Tuple      						=						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.")

	_lowerCamelCase      :  str      						=						parser.parse_args()

	_lowerCamelCase      :  int      						=						{
	    "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",
	}

	_lowerCamelCase      :  Optional[Any]      						=						{
	    "time_steps": "time_proj",
	    "mid": "mid_block",
	    "downsample_blocks": "down_blocks",
	    "upsample_blocks": "up_blocks",
	}

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

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

	_lowerCamelCase      :  Dict      						=						dict(model.config)

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

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

	if do_only_weights:
		_lowerCamelCase      :  List[Any]      						=						torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

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







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

