


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class lowerCAmelCase_						(			A__	):

	'''simple docstring'''

	__snake_case												=  4_2
	__snake_case												=  4_2
	__snake_case												=  None







class lowerCAmelCase_						(			A__ ,						A__	):

	'''simple docstring'''

	__snake_case												=  2



	@register_to_config
	def __init__(     self		,				_UpperCAmelCase = 0.02		,				_UpperCAmelCase = 1_00		,				_UpperCAmelCase = 1.007		,				_UpperCAmelCase = 80		,				_UpperCAmelCase = 0.05		,				_UpperCAmelCase = 50		,				):
							# standard deviation of the initial noise distribution
							snake_case_	=				sigma_max

							# setable values
							snake_case_	=				None
							snake_case_	=				None
							snake_case_	=				None  # sigma(t_i)



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							return sample



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							snake_case_	=				num_inference_steps
							snake_case_	=				np.arange(0		,				self.num_inference_steps )[::-1].copy()
							snake_case_	=				torch.from_numpy(__snake_case ).to(__snake_case )
							snake_case_	=				[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							snake_case_	=				torch.tensor(__snake_case		,				dtype=torch.floataa		,				device=__snake_case )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							if self.config.s_min <= sigma <= self.config.s_max:
													snake_case_	=				min(self.config.s_churn / self.num_inference_steps		,				2**0.5 - 1 )
							else:
													snake_case_	=				0

							# sample eps ~ N(0, S_noise^2 * I)
							snake_case_	=				self.config.s_noise * randn_tensor(sample.shape		,				generator=__snake_case ).to(sample.device )
							snake_case_	=				sigma + gamma * sigma
							snake_case_	=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = True		,				):
							snake_case_	=				sample_hat + sigma_hat * model_output
							snake_case_	=				(sample_hat - pred_original_sample) / sigma_hat
							snake_case_	=				sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=__snake_case		,				derivative=__snake_case		,				pred_original_sample=__snake_case )



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = True		,				):
							snake_case_	=				sample_prev + sigma_prev * model_output
							snake_case_	=				(sample_prev - pred_original_sample) / sigma_prev
							snake_case_	=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=__snake_case		,				derivative=__snake_case		,				pred_original_sample=__snake_case )





	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
							raise NotImplementedError()



# Function to print upper half of diamond (pyramid)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(0 , SCREAMING_SNAKE_CASE ):
            for _ in range(0 , n - i - 1 ):  # printing spaces
                  print(''' ''' , end='''''' )
            for _ in range(0 , i + 1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
            for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()
            for _ in range(n - i + 1 , 0 , -1 ):  # printing spaces
                  print(''' ''' , end='''''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      if n <= 0:
            print('''       ...       ....        nothing printing :(''' )
            return
      floyd(SCREAMING_SNAKE_CASE )  # upper half
      reverse_floyd(SCREAMING_SNAKE_CASE )  # lower half


if __name__ == "__main__":
   print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
   print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
   UpperCAmelCase      =   1
   while K:
      UpperCAmelCase      =   int(input("""enter the number and , and see the magic : """))
      print()
      pretty_print(user_number)
      UpperCAmelCase      =   int(input("""press 0 to exit... and 1 to continue..."""))

   print("""Good Bye...""")