



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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

 a__					: List[Any] =  None
 a__					: str =  None
 a__					: int =  None  # sigma(t_i)

 @classmethod
 def     snake_case_      (    cls					:					Optional[int]      )   ->	Union[str, Any]:
    return cls()


@dataclass
class 	lowerCamelCase__	(       SCREAMING_SNAKE_CASE__):



 """simple docstring"""

 a__					: str =  42
 a__					: Optional[int] =  42
 a__					: Optional[Any] =  42


class 	lowerCamelCase__	(       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__):



 """simple docstring"""

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[Any]:
    return True

 @register_to_config
 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					float = 0.02			,						__lowerCAmelCase					:					float = 1_00			,						__lowerCAmelCase					:					float = 1.007			,						__lowerCAmelCase					:					float = 80			,						__lowerCAmelCase					:					float = 0.05			,						__lowerCAmelCase					:					float = 50			,						)   ->	Dict:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[Any]:
    return KarrasVeSchedulerState.create()

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					KarrasVeSchedulerState			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Tuple = ()      )   ->	Optional[int]:
    _A      =       jnp.arange(0			,						snake_case__      )[::-1].copy()
    _A      =       [
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in timesteps
    ]

    return state.replace(
        num_inference_steps=snake_case__			,						schedule=jnp.array(snake_case__			,						dtype=jnp.floataa      )			,						timesteps=snake_case__			,						)

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					KarrasVeSchedulerState			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					random.KeyArray			,						)   ->	Optional[Any]:

    if self.config.s_min <= sigma <= self.config.s_max:
       _A      =       min(self.config.s_churn / state.num_inference_steps			,						2**0.5 - 1      )
    else:
       _A      =       0

    # sample eps ~ N(0, S_noise^2 * I)
    _A      =       random.split(snake_case__			,						num=1      )
    _A      =       self.config.s_noise * random.normal(key=snake_case__			,						shape=sample.shape      )
    _A      =       sigma + gamma * sigma
    _A      =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					KarrasVeSchedulerState			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					bool = True			,						)   ->	str:
    _A      =       sample_hat + sigma_hat * model_output
    _A      =       (sample_hat - pred_original_sample) / sigma_hat
    _A      =       sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
       return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=snake_case__			,						derivative=snake_case__			,						state=snake_case__      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					KarrasVeSchedulerState			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					bool = True			,						)   ->	Any:
    _A      =       sample_prev + sigma_prev * model_output
    _A      =       (sample_prev - pred_original_sample) / sigma_prev
    _A      =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
       return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=snake_case__			,						derivative=snake_case__			,						state=snake_case__      )

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					KarrasVeSchedulerState			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any      )   ->	str:
    raise NotImplementedError()









from collections import defaultdict


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	bool:
		SCREAMING_SNAKE_CASE_					      =							first_str.lower().strip()
		SCREAMING_SNAKE_CASE_					      =							second_str.lower().strip()

		# Remove whitespace
		SCREAMING_SNAKE_CASE_					      =							first_str.replace(' '    ,						'' )
		SCREAMING_SNAKE_CASE_					      =							second_str.replace(' '    ,						'' )

		# Strings of different lengths are not anagrams
		if len(lowerCAmelCase ) != len(lowerCAmelCase ):
				return False

		# Default values for count should be 0
		SCREAMING_SNAKE_CASE_					      =							defaultdict(lowerCAmelCase )

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(lowerCAmelCase ) ):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						SCREAMING_SNAKE_CASE:     Dict									= input('''Enter the first string ''').strip()
						SCREAMING_SNAKE_CASE:     str									= input('''Enter the second string ''').strip()

						SCREAMING_SNAKE_CASE:     Optional[Any]									= check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {'' if status else 'not '}anagrams.""")