


# 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 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			42

 # setable values
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			None




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return cls(common=_lowerCamelCase   ,    init_noise_sigma=_lowerCamelCase   ,    timesteps=_lowerCamelCase       )







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			42







class 						_snake_case    ( _snake_case		,       _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[e.name for e in FlaxKarrasDiffusionSchedulers]

 SCREAMING_SNAKE_CASE__ =			42




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return True




 @register_to_config
 def __init__(						self   ,    _lowerCamelCase = 1000   ,    _lowerCamelCase = 0.0001   ,    _lowerCamelCase = 0.02   ,    _lowerCamelCase = "linear"   ,    _lowerCamelCase = None   ,    _lowerCamelCase = "fixed_small"   ,    _lowerCamelCase = True   ,    _lowerCamelCase = "epsilon"   ,    _lowerCamelCase = jnp.floataa   ,    ):
       a		:List[Any]									=			dtype




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None       ):
       if common is None:
             a		:Tuple									=			CommonSchedulerState.create(self       )

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

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

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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       return sample




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = ()       ):
       a		:Optional[int]									=			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
       a		:List[str]									=			(jnp.arange(0   ,    _lowerCamelCase       ) * step_ratio).round()[::-1]

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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None       ):
       a		:List[str]									=			state.common.alphas_cumprod[t]
       a		:Tuple									=			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
       a		:List[Any]									=			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
             a		:int									=			self.config.variance_type

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

       return variance




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase = True   ,    ):
       a		:Tuple									=			timestep

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

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

       # 1. compute alphas, betas
       a		:Union[str, Any]									=			state.common.alphas_cumprod[t]
       a		:List[Any]									=			jnp.where(t > 0   ,    state.common.alphas_cumprod[t - 1]   ,    jnp.array(1.0   ,    dtype=self.dtype       )       )
       a		:Union[str, Any]									=			1 - alpha_prod_t
       a		:Dict									=			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":
             a		:Any									=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
             a		:List[str]									=			model_output
       elif self.config.prediction_type == "v_prediction":
             a		:List[Any]									=			(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:
             a		:str									=			jnp.clip(_lowerCamelCase   ,    -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
       a		:Optional[Any]									=			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       a		:Tuple									=			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
       a		:Tuple									=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
             a		:str									=			jax.random.split(_lowerCamelCase   ,    num=1       )
             a		:List[str]									=			jax.random.normal(_lowerCamelCase   ,    shape=model_output.shape   ,    dtype=self.dtype       )
             return (self._get_variance(_lowerCamelCase   ,    _lowerCamelCase   ,    predicted_variance=_lowerCamelCase       ) ** 0.5) * noise

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

       a		:Tuple									=			pred_prev_sample + variance

       if not return_dict:
             return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=_lowerCamelCase   ,    state=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    ):
       return add_noise_common(state.common   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    ):
       return get_velocity_common(state.common   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 def __len__(						self       ):
       return self.config.num_train_timesteps









import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      a		:Optional[int]									=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      i -= len(UpperCAmelCase_		)
      a		:Tuple									=			i // 3
      a		:int									=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      a		:Union[str, Any]									=			(
          chars_incl
          + random(UpperCAmelCase_					,    quotient + remainder		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
          + random(UpperCAmelCase_					,    UpperCAmelCase_		)
      )
      a		:Dict									=			list(UpperCAmelCase_		)
      shuffle(UpperCAmelCase_		)
      return "".join(UpperCAmelCase_		)

      # random is a generalised function for letters, characters and numbers
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return "".join(secrets.choice(UpperCAmelCase_		) for _ in range(UpperCAmelCase_		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      pass  # Put your code here...
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int = 8		):




      """simple docstring"""





      if len(UpperCAmelCase_		) < min_length:
            # Your Password must be at least 8 characters long
            return False

      a		:Dict									=			any(char in ascii_uppercase for char in password		)
      a		:Optional[int]									=			any(char in ascii_lowercase for char in password		)
      a		:Tuple									=			any(char in digits for char in password		)
      a		:Any									=			any(char in punctuation for char in password		)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:int									=			int(input('''Please indicate the max length of your password: '''		).strip()		)
      a		:Union[str, Any]									=			input(
          '''Please indicate the characters that must be in your password: '''		).strip()
      print('''Password generated:'''					,    password_generator(UpperCAmelCase_		)		)
      print(
          '''Alternative Password generated:'''					,    alternative_password_generator(UpperCAmelCase_					,    UpperCAmelCase_		)					,    )
      print('''[If you are thinking of using this passsword, You better save it.]'''		)


if __name__ == "__main__":
     main()





