

'''simple docstring'''


import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)




def 	_a       (				__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  checkpoint

		snake_case__						:       Optional[int]       						=  {}

		snake_case__						:       Any       						=  vae_state_dict['''encoder.conv_in.weight''']
		snake_case__						:       List[str]       						=  vae_state_dict['''encoder.conv_in.bias''']
		snake_case__						:       Optional[int]       						=  vae_state_dict['''encoder.conv_out.weight''']
		snake_case__						:       Any       						=  vae_state_dict['''encoder.conv_out.bias''']
		snake_case__						:       Union[str, Any]       						=  vae_state_dict['''encoder.norm_out.weight''']
		snake_case__						:       Tuple       						=  vae_state_dict['''encoder.norm_out.bias''']

		snake_case__						:       Union[str, Any]       						=  vae_state_dict['''decoder.conv_in.weight''']
		snake_case__						:       Optional[int]       						=  vae_state_dict['''decoder.conv_in.bias''']
		snake_case__						:       List[str]       						=  vae_state_dict['''decoder.conv_out.weight''']
		snake_case__						:       List[Any]       						=  vae_state_dict['''decoder.conv_out.bias''']
		snake_case__						:       Dict       						=  vae_state_dict['''decoder.norm_out.weight''']
		snake_case__						:       Tuple       						=  vae_state_dict['''decoder.norm_out.bias''']

		snake_case__						:       Optional[int]       						=  vae_state_dict['''quant_conv.weight''']
		snake_case__						:       int       						=  vae_state_dict['''quant_conv.bias''']
		snake_case__						:       List[str]       						=  vae_state_dict['''post_quant_conv.weight''']
		snake_case__						:       List[Any]       						=  vae_state_dict['''post_quant_conv.bias''']

		# Retrieves the keys for the encoder down blocks only
		snake_case__						:       List[Any]       						=  len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in vae_state_dict if '''encoder.down''' in layer}     )
		snake_case__						:       str       						=  {
		    layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(__lowerCAmelCase     )
		}

		# Retrieves the keys for the decoder up blocks only
		snake_case__						:       Dict       						=  len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in vae_state_dict if '''decoder.up''' in layer}     )
		snake_case__						:       Optional[Any]       						=  {
		    layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(__lowerCAmelCase     )
		}

		for i in range(__lowerCAmelCase     ):
				snake_case__						:       str       						=  [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]

				if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
						snake_case__						:       Tuple       						=  vae_state_dict.pop(
						    F"""encoder.down.{i}.downsample.conv.weight"""     )
						snake_case__						:       Union[str, Any]       						=  vae_state_dict.pop(
						    F"""encoder.down.{i}.downsample.conv.bias"""     )

				snake_case__						:       Any       						=  renew_vae_resnet_paths(__lowerCAmelCase     )
				snake_case__						:       Union[str, Any]       						=  {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
				assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )

		snake_case__						:       str       						=  [key for key in vae_state_dict if '''encoder.mid.block''' in key]
		snake_case__						:       str       						=  2
		for i in range(1   ,							num_mid_res_blocks + 1     ):
				snake_case__						:       List[str]       						=  [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]

				snake_case__						:       Union[str, Any]       						=  renew_vae_resnet_paths(__lowerCAmelCase     )
				snake_case__						:       Any       						=  {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
				assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )

		snake_case__						:       Dict       						=  [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
		snake_case__						:       Dict       						=  renew_vae_attention_paths(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
		assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )
		conv_attn_to_linear(__lowerCAmelCase     )

		for i in range(__lowerCAmelCase     ):
				snake_case__						:       Any       						=  num_up_blocks - 1 - i
				snake_case__						:       Union[str, Any]       						=  [
				    key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
				]

				if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
						snake_case__						:       Optional[int]       						=  vae_state_dict[
						    F"""decoder.up.{block_id}.upsample.conv.weight"""
						]
						snake_case__						:       List[Any]       						=  vae_state_dict[
						    F"""decoder.up.{block_id}.upsample.conv.bias"""
						]

				snake_case__						:       Dict       						=  renew_vae_resnet_paths(__lowerCAmelCase     )
				snake_case__						:       int       						=  {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
				assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )

		snake_case__						:       Optional[Any]       						=  [key for key in vae_state_dict if '''decoder.mid.block''' in key]
		snake_case__						:       List[str]       						=  2
		for i in range(1   ,							num_mid_res_blocks + 1     ):
				snake_case__						:       str       						=  [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]

				snake_case__						:       Any       						=  renew_vae_resnet_paths(__lowerCAmelCase     )
				snake_case__						:       List[Any]       						=  {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
				assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )

		snake_case__						:       str       						=  [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
		snake_case__						:       int       						=  renew_vae_attention_paths(__lowerCAmelCase     )
		snake_case__						:       int       						=  {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
		assign_to_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							additional_replacements=[meta_path]   ,							config=__lowerCAmelCase     )
		conv_attn_to_linear(__lowerCAmelCase     )
		return new_checkpoint




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      str   ,							):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  requests.get(
		    ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''     )
		snake_case__						:       str       						=  io.BytesIO(r.content     )

		snake_case__						:       Union[str, Any]       						=  OmegaConf.load(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  5_12
		snake_case__						:       Tuple       						=  '''cuda''' if torch.cuda.is_available() else '''cpu'''
		if checkpoint_path.endswith('''safetensors'''     ):
				from safetensors import safe_open

				snake_case__						:       List[str]       						=  {}
				with safe_open(__lowerCAmelCase   ,							framework='''pt'''   ,							device='''cpu'''     ) as f:
						for key in f.keys():
								snake_case__						:       Optional[int]       						=  f.get_tensor(__lowerCAmelCase     )
		else:
				snake_case__						:       Optional[Any]       						=  torch.load(__lowerCAmelCase   ,							map_location=__lowerCAmelCase     )['''state_dict''']

		# Convert the VAE model.
		snake_case__						:       Dict       						=  create_vae_diffusers_config(__lowerCAmelCase   ,							image_size=__lowerCAmelCase     )
		snake_case__						:       Dict       						=  custom_convert_ldm_vae_checkpoint(__lowerCAmelCase   ,							__lowerCAmelCase     )

		snake_case__						:       str       						=  AutoencoderKL(**__lowerCAmelCase     )
		vae.load_state_dict(__lowerCAmelCase     )
		vae.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Tuple										=						argparse.ArgumentParser()

					parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






'''simple docstring'''


import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 0.1:
								safe += 1
						else:
								not_safe += 1
		return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

					# voting system with forecasting
					lowerCAmelCase__						:       Optional[Any]										=						[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					lowerCAmelCase__						:       Optional[int]										=						"""""" if data_safety_checker(res_vote, tst_user) else """not """
					print("""Today's data is {not_str}safe.""")



