
"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class     _UpperCAmelCase:
		lowercase__						=      42

		# setable values
		lowercase__						=      42
		lowercase__						=      42
		lowercase__						=      None



		@classmethod
		def 						UpperCAmelCase							(					cls				,		__a				,		__a				,		__a)       ->       Tuple:






							'''simple docstring'''
							return cls(common=__a				,		init_noise_sigma=__a				,		timesteps=__a)






@dataclass
class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      42






class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase	):
		lowercase__						=      [e.name for e in FlaxKarrasDiffusionSchedulers]

		lowercase__						=      42



		@property
		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							return True



		@register_to_config
		def __init__(					self				,		__a = 10_00				,		__a = 0.0001				,		__a = 0.02				,		__a = "linear"				,		__a = None				,		__a = "fixed_small"				,		__a = True				,		__a = "epsilon"				,		__a = jnp.floataa				,		)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  dtype



		def 						UpperCAmelCase							(					self				,		__a = None)       ->       DDPMSchedulerState:






							'''simple docstring'''
							if common is None:
												_UpperCamelCase							 =  CommonSchedulerState.create(self)

							# standard deviation of the initial noise distribution
							_UpperCamelCase							 =  jnp.array(1.0				,		dtype=self.dtype)

							_UpperCamelCase							 =  jnp.arange(0				,		self.config.num_train_timesteps).round()[::-1]

							return DDPMSchedulerState.create(
							    common=__a				,		init_noise_sigma=__a				,		timesteps=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = None)       ->       jnp.ndarray:






							'''simple docstring'''
							return sample



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a = ())       ->       DDPMSchedulerState:






							'''simple docstring'''
							_UpperCamelCase							 =  self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							_UpperCamelCase							 =  (jnp.arange(0				,		__a) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=__a				,		timesteps=__a				,		)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None				,		__a=None)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  state.common.alphas_cumprod[t]
							_UpperCamelCase							 =  jnp.where(t > 0				,		state.common.alphas_cumprod[t - 1]				,		jnp.array(1.0				,		dtype=self.dtype))

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							_UpperCamelCase							 =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
												_UpperCamelCase							 =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
												_UpperCamelCase							 =  jnp.clip(__a				,		a_min=1e-20)
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
												_UpperCamelCase							 =  jnp.log(jnp.clip(__a				,		a_min=1e-20))
							elif variance_type == "fixed_large":
												_UpperCamelCase							 =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
												# Glide max_log
												_UpperCamelCase							 =  jnp.log(state.common.betas[t])
							elif variance_type == "learned":
												return predicted_variance
							elif variance_type == "learned_range":
												_UpperCamelCase							 =  variance
												_UpperCamelCase							 =  state.common.betas[t]
												_UpperCamelCase							 =  (predicted_variance + 1) / 2
												_UpperCamelCase							 =  frac * max_log + (1 - frac) * min_log

							return variance



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a = None				,		__a = True				,		)       ->       Union[FlaxDDPMSchedulerOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase							 =  timestep

							if key is None:
												_UpperCamelCase							 =  jax.random.PRNGKey(0)

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												_UpperCamelCase ,  _UpperCamelCase							 =  jnp.split(__a				,		sample.shape[1]				,		axis=1)
							else:
												_UpperCamelCase							 =  None

							# 1. compute alphas, betas
							_UpperCamelCase							 =  state.common.alphas_cumprod[t]
							_UpperCamelCase							 =  jnp.where(t > 0				,		state.common.alphas_cumprod[t - 1]				,		jnp.array(1.0				,		dtype=self.dtype))
							_UpperCamelCase							 =  1 - alpha_prod_t
							_UpperCamelCase							 =  1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
												_UpperCamelCase							 =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
												_UpperCamelCase							 =  model_output
							elif self.config.prediction_type == "v_prediction":
												_UpperCamelCase							 =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
												    ''' for the FlaxDDPMScheduler.''')

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
												_UpperCamelCase							 =  jnp.clip(__a				,		-1				,		1)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_UpperCamelCase							 =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							_UpperCamelCase							 =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_UpperCamelCase							 =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
												_UpperCamelCase							 =  jax.random.split(__a				,		num=1)
												_UpperCamelCase							 =  jax.random.normal(__a				,		shape=model_output.shape				,		dtype=self.dtype)
												return (self._get_variance(__a				,		__a				,		predicted_variance=__a) ** 0.5) * noise

							_UpperCamelCase							 =  jnp.where(t > 0				,		random_variance()				,		jnp.zeros(model_output.shape				,		dtype=self.dtype))

							_UpperCamelCase							 =  pred_prev_sample + variance

							if not return_dict:
												return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__a				,		state=__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       jnp.ndarray:






							'''simple docstring'''
							return add_noise_common(state.common				,		__a				,		__a				,		__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       jnp.ndarray:






							'''simple docstring'''
							return get_velocity_common(state.common				,		__a				,		__a				,		__a)




		def __len__(					self)       ->       Dict:






							'''simple docstring'''
							return self.config.num_train_timesteps








"""simple docstring"""





def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')	)



def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					_UpperCamelCase							 =  credit_card_number
					_UpperCamelCase							 =  0
					_UpperCamelCase							 =  len(__snake_case	) - 2
					for i in range(__snake_case,     -1,     -2	):
										#  double the value of every second digit
										_UpperCamelCase							 =  int(cc_number[i]	)
										digit *= 2
										# If doubling of a number results in a two digit number
										# i.e greater than 9(e.g., 6 × 2 = 12),
										# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
										# to get a single digit number.
										if digit > 9:
															digit %= 10
															digit += 1
										_UpperCamelCase							 =  cc_number[:i] + str(__snake_case	) + cc_number[i + 1 :]
										total += digit

					# Sum up the remaining digits
					for i in range(len(__snake_case	) - 1,     -1,     -2	):
										total += int(cc_number[i]	)

					return total % 10 == 0



def       lowerCamelCase__		(			__snake_case	)							->		bool:


					"""simple docstring"""





					_UpperCamelCase							 =  F'''{credit_card_number} is an invalid credit card number because'''
					if not credit_card_number.isdigit():
										print(F'''{error_message} it has nonnumerical characters.'''	)
										return False

					if not 13 <= len(__snake_case	) <= 16:
										print(F'''{error_message} of its length.'''	)
										return False

					if not validate_initial_digits(__snake_case	):
										print(F'''{error_message} of its first two digits.'''	)
										return False

					if not luhn_validation(__snake_case	):
										print(F'''{error_message} it fails the Luhn check.'''	)
										return False

					print(F'''{credit_card_number} is a valid credit card number.'''	)
					return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				validate_credit_card_number("""4111111111111111""")
				validate_credit_card_number("""32323""")






