code: 
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  _lowerCAmelCase     :			Tuple    =logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      4_2
  					__magic_name__     			=      None
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=0.9_99  ,SCREAMING_SNAKE_CASE="cosine"  ,):
  	if alpha_transform_type == "cosine":
  
  		def alpha_bar_fn(SCREAMING_SNAKE_CASE			):
  			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2
  
  	elif alpha_transform_type == "exp":
  
  		def alpha_bar_fn(SCREAMING_SNAKE_CASE			):
  			return math.exp(t * -12.0			)
  
  	else:
  		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)
  
  	UpperCAmelCase__:  Union[str, Any]		       =   []
  	for i in range(SCREAMING_SNAKE_CASE			):
  		UpperCAmelCase__:  List[str]		       =   i / num_diffusion_timesteps
  		UpperCAmelCase__:  List[str]		       =   (i + 1) / num_diffusion_timesteps
  		betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE			) / alpha_bar_fn(SCREAMING_SNAKE_CASE			)  ,SCREAMING_SNAKE_CASE			)			)
  	return torch.tensor(SCREAMING_SNAKE_CASE  ,dtype=torch.floataa			)
  
  class __UpperCamelCase    (							_a				,_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      [e.name for e in KarrasDiffusionSchedulers]
  					__magic_name__     			=      1
  
  
  
  
  
  					@register_to_config
  					def __init__(							self							,			lowerCamelCase__ = 1_0_0_0							,			lowerCamelCase__ = 0.0_001							,			lowerCamelCase__ = 0.02							,			lowerCamelCase__ = "linear"							,			lowerCamelCase__ = None							,			lowerCamelCase__ = "epsilon"							,			lowerCamelCase__ = "linspace"							,			lowerCamelCase__ = 0							,			):
  						if trained_betas is not None:
  							UpperCAmelCase__:  List[Any]		       =   torch.tensor(lowerCamelCase__							,			dtype=torch.floataa	)
  						elif beta_schedule == "linear":
  							UpperCAmelCase__:  List[str]		       =   torch.linspace(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			dtype=torch.floataa	)
  						elif beta_schedule == "scaled_linear":
  							# this schedule is very specific to the latent diffusion model.
  							UpperCAmelCase__:  Any		       =   (
  							    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			lowerCamelCase__							,			dtype=torch.floataa	) ** 2
  							)
  						elif beta_schedule == "squaredcos_cap_v2":
  							# Glide cosine schedule
  							UpperCAmelCase__:  int		       =   betas_for_alpha_bar(lowerCamelCase__	)
  						else:
  							raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   1.0 - self.betas
  						UpperCAmelCase__:  Optional[int]		       =   torch.cumprod(self.alphas							,			dim=0	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
  						UpperCAmelCase__:  int		       =   np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
  						UpperCAmelCase__:  Tuple		       =   torch.from_numpy(lowerCamelCase__	)
  
  						# setable values
  						UpperCAmelCase__:  Optional[int]		       =   None
  						UpperCAmelCase__:  Tuple		       =   np.linspace(0							,			num_train_timesteps - 1							,			lowerCamelCase__							,			dtype=lowerCamelCase__	)[::-1].copy()
  						UpperCAmelCase__:  Optional[int]		       =   torch.from_numpy(lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   False
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						# standard deviation of the initial noise distribution
  						if self.config.timestep_spacing in ["linspace", "trailing"]:
  							return self.sigmas.max()
  
  						return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						if isinstance(lowerCamelCase__							,			torch.Tensor	):
  							UpperCAmelCase__:  Dict		       =   timestep.to(self.timesteps.device	)
  						UpperCAmelCase__:  int		       =   (self.timesteps == timestep).nonzero().item()
  						UpperCAmelCase__:  Union[str, Any]		       =   self.sigmas[step_index]
  						UpperCAmelCase__:  str		       =   sample / ((sigma**2 + 1) ** 0.5)
  						UpperCAmelCase__:  Union[str, Any]		       =   True
  						return sample
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None	):
  						UpperCAmelCase__:  Union[str, Any]		       =   num_inference_steps
  
  						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  						if self.config.timestep_spacing == "linspace":
  							UpperCAmelCase__:  int		       =   np.linspace(0							,			self.config.num_train_timesteps - 1							,			lowerCamelCase__							,			dtype=lowerCamelCase__	)[
  							    ::-1
  							].copy()
  						elif self.config.timestep_spacing == "leading":
  							UpperCAmelCase__:  int		       =   self.config.num_train_timesteps // self.num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# casting to int to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase__:  List[str]		       =   (np.arange(0							,			lowerCamelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__	)
  							timesteps += self.config.steps_offset
  						elif self.config.timestep_spacing == "trailing":
  							UpperCAmelCase__:  Union[str, Any]		       =   self.config.num_train_timesteps / self.num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# casting to int to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase__:  Tuple		       =   (np.arange(self.config.num_train_timesteps							,			0							,			-step_ratio	)).round().copy().astype(lowerCamelCase__	)
  							timesteps -= 1
  						else:
  							raise ValueError(
  							    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)
  
  						UpperCAmelCase__:  Tuple		       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
  						UpperCAmelCase__:  Any		       =   np.interp(lowerCamelCase__							,			np.arange(0							,			len(lowerCamelCase__	)	)							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Any		       =   np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
  						UpperCAmelCase__:  Union[str, Any]		       =   torch.from_numpy(lowerCamelCase__	).to(device=lowerCamelCase__	)
  						if str(lowerCamelCase__	).startswith("mps"	):
  							# mps does not support float64
  							UpperCAmelCase__:  List[Any]		       =   torch.from_numpy(lowerCamelCase__	).to(lowerCamelCase__							,			dtype=torch.floataa	)
  						else:
  							UpperCAmelCase__:  Union[str, Any]		       =   torch.from_numpy(lowerCamelCase__	).to(device=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = None							,			lowerCamelCase__ = True							,			):
  
  						if (
  						    isinstance(lowerCamelCase__							,			lowerCamelCase__	)
  						    or isinstance(lowerCamelCase__							,			torch.IntTensor	)
  						    or isinstance(lowerCamelCase__							,			torch.LongTensor	)
  						):
  							raise ValueError(
  							    (
  							        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
  							        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
  							        " one of the `scheduler.timesteps` as a timestep."
  							    )							,			)
  
  						if not self.is_scale_input_called:
  							logger.warning(
  							    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
  							    "See `StableDiffusionPipeline` for a usage example."	)
  
  						if isinstance(lowerCamelCase__							,			torch.Tensor	):
  							UpperCAmelCase__:  Union[str, Any]		       =   timestep.to(self.timesteps.device	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   (self.timesteps == timestep).nonzero().item()
  						UpperCAmelCase__:  Tuple		       =   self.sigmas[step_index]
  
  						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  						if self.config.prediction_type == "epsilon":
  							UpperCAmelCase__:  Tuple		       =   sample - sigma * model_output
  						elif self.config.prediction_type == "v_prediction":
  							# * c_out + input * c_skip
  							UpperCAmelCase__:  Any		       =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  						elif self.config.prediction_type == "sample":
  							raise NotImplementedError("prediction_type not implemented yet: sample"	)
  						else:
  							raise ValueError(
  							    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)
  
  						UpperCAmelCase__:  Any		       =   self.sigmas[step_index]
  						UpperCAmelCase__:  Optional[int]		       =   self.sigmas[step_index + 1]
  						UpperCAmelCase__:  Union[str, Any]		       =   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
  						UpperCAmelCase__:  List[str]		       =   (sigma_to**2 - sigma_up**2) ** 0.5
  
  						# 2. Convert to an ODE derivative
  						UpperCAmelCase__:  Union[str, Any]		       =   (sample - pred_original_sample) / sigma
  
  						UpperCAmelCase__:  Union[str, Any]		       =   sigma_down - sigma
  
  						UpperCAmelCase__:  List[Any]		       =   sample + derivative * dt
  
  						UpperCAmelCase__:  Tuple		       =   model_output.device
  						UpperCAmelCase__:  List[Any]		       =   randn_tensor(model_output.shape							,			dtype=model_output.dtype							,			device=lowerCamelCase__							,			generator=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Tuple		       =   prev_sample + noise * sigma_up
  
  						if not return_dict:
  							return (prev_sample,)
  
  						return EulerAncestralDiscreteSchedulerOutput(
  						    prev_sample=lowerCamelCase__							,			pred_original_sample=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			):
  						# Make sure sigmas and timesteps have the same device and dtype as original_samples
  						UpperCAmelCase__:  Optional[Any]		       =   self.sigmas.to(device=original_samples.device							,			dtype=original_samples.dtype	)
  						if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__	):
  							# mps does not support float64
  							UpperCAmelCase__:  Any		       =   self.timesteps.to(original_samples.device							,			dtype=torch.floataa	)
  							UpperCAmelCase__:  int		       =   timesteps.to(original_samples.device							,			dtype=torch.floataa	)
  						else:
  							UpperCAmelCase__:  Union[str, Any]		       =   self.timesteps.to(original_samples.device	)
  							UpperCAmelCase__:  Union[str, Any]		       =   timesteps.to(original_samples.device	)
  
  						UpperCAmelCase__:  List[Any]		       =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  						UpperCAmelCase__:  List[str]		       =   sigmas[step_indices].flatten()
  						while len(sigma.shape	) < len(original_samples.shape	):
  							UpperCAmelCase__:  Union[str, Any]		       =   sigma.unsqueeze(-1	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   original_samples + noise * sigma
  						return noisy_samples
  
  
  
  
  
  					def __len__(							self	):
  						return self.config.num_train_timesteps
style_context: 
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  _lowerCAmelCase     :			int    ="""1.5.0"""
  
  _lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	if is_optimum_available():
  		from optimum.version import __version__ as optimum_version
  
  		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
  		if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE			):
  			raise RuntimeError(
  			    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
  			    "can upgrade optimum by running: pip install -U optimum[exporters]"			)
  	else:
  		raise RuntimeError(
  		    "transformers.onnx requires optimum to run, you can install the library by running: pip install "
  		    "optimum[exporters]"			)
  	UpperCAmelCase__:  Any		       =   [
  	    sys.executable,
  	    "-m",
  	    "optimum.exporters.onnx",
  	    f"--model {args.model}",
  	    f"--task {args.feature}",
  	    f"--framework {args.framework}" if args.framework is not None else "",
  	    f"{args.output}",
  	]
  	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_SNAKE_CASE			)
  	proc.wait()
  
  	logger.info(
  	    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
  	    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
  	    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   args.output if args.output.is_file() else args.output.joinpath("model.onnx"			)
  	if not args.output.parent.exists():
  		args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE			)
  
  	# Allocate the model
  	UpperCAmelCase__:  int		       =   FeaturesManager.get_model_from_feature(
  	    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir			)
  
  	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
  	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)
  
  	if model_kind in ENCODER_DECODER_MODELS:
  		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
  		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()
  
  		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
  		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
  		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)
  
  		if args.opset is None:
  			UpperCAmelCase__:  Tuple		       =   max(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)
  
  		if args.opset < min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			):
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
  			    f" {min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)} is required."			)
  
  		UpperCAmelCase__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)
  
  		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else decoder_onnx_config.atol_for_validation  ,)
  		logger.info(
  		    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"
  		    f" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"			)
  
  	else:
  		# Instantiate the appropriate preprocessor
  		if args.preprocessor == "auto":
  			UpperCAmelCase__:  str		       =   get_preprocessor(args.model			)
  		elif args.preprocessor == "tokenizer":
  			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
  		elif args.preprocessor == "image_processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
  		elif args.preprocessor == "feature_extractor":
  			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  		elif args.preprocessor == "processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoProcessor.from_pretrained(args.model			)
  		else:
  			raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"			)
  
  		# Ensure the requested opset is sufficient
  		if args.opset is None:
  			UpperCAmelCase__:  List[str]		       =   onnx_config.default_onnx_opset
  
  		if args.opset < onnx_config.default_onnx_opset:
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. "
  			    f"At least  {onnx_config.default_onnx_opset} is required."			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)
  
  		if args.atol is None:
  			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation
  
  		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_SNAKE_CASE  ,args.atol			)
  		logger.info(f"All good, model saved at: {args.output.as_posix()}"			)
  		warnings.warn(
  		    "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
  		    " using optimum.exporters.onnx in future. You can find more information here:"
  		    " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."  ,SCREAMING_SNAKE_CASE  ,)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  str		       =   ArgumentParser("Hugging Face Transformers ONNX exporter"			)
  	parser.add_argument(
  	    "-m"  ,"--model"  ,type=SCREAMING_SNAKE_CASE  ,required=SCREAMING_SNAKE_CASE  ,help="Model ID on huggingface.co or path on disk to load model from."			)
  	parser.add_argument(
  	    "--feature"  ,default="default"  ,help="The type of features to export the model with."  ,)
  	parser.add_argument("--opset"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="ONNX opset version to export the model with."			)
  	parser.add_argument(
  	    "--atol"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Absolute difference tolerance when validating the model."			)
  	parser.add_argument(
  	    "--framework"  ,type=SCREAMING_SNAKE_CASE  ,choices=["pt", "tf"]  ,default=SCREAMING_SNAKE_CASE  ,help=(
  	        "The framework to use for the ONNX export."
  	        " If not provided, will attempt to use the local checkpoint's original framework"
  	        " or what is available in the environment."
  	    )  ,)
  	parser.add_argument("output"  ,type=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store generated ONNX model."			)
  	parser.add_argument("--cache_dir"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store cache."			)
  	parser.add_argument(
  	    "--preprocessor"  ,type=SCREAMING_SNAKE_CASE  ,choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]  ,default="auto"  ,help="Which type of preprocessor to use. 'auto' tries to automatically detect it."  ,)
  	parser.add_argument(
  	    "--export_with_transformers"  ,action="store_true"  ,help=(
  	        "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
  	        "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
  	        "recommended."
  	    )  ,)
  
  	UpperCAmelCase__:  Optional[int]		       =   parser.parse_args()
  	if args.export_with_transformers or not is_optimum_available():
  		export_with_transformers(SCREAMING_SNAKE_CASE			)
  	else:
  		export_with_optimum(SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			int    =logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  					logger.setLevel(logging.INFO)
  					main()