


"""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 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			CommonSchedulerState

		# setable values
		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			Optional[int]										=				None

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a							, _a							, _a      ):
				return cls(common=_a							, init_noise_sigma=_a							, timesteps=_a      )







@dataclass
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			DDPMSchedulerState







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				[e.name for e in FlaxKarrasDiffusionSchedulers]

		__UpperCAmelCase		:			jnp.dtype

		@property
		def 			__UpperCAmelCase       (  self      ):
				return True

		@register_to_config
		def __init__(  self							, _a = 1_000							, _a = 0.0001							, _a = 0.02							, _a = "linear"							, _a = None							, _a = "fixed_small"							, _a = True							, _a = "epsilon"							, _a = jnp.floataa							, ):
				__a           =			dtype

		def 			__UpperCAmelCase       (  self							, _a = None      ):
				if common is None:
						__a           =			CommonSchedulerState.create(self      )

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

				__a           =			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      ):
				return sample

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = ()      ):
				__a           =			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           =			(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      ):
				__a           =			state.common.alphas_cumprod[t]
				__a           =			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           =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

				return variance

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a = None							, _a = True							, ):
				__a           =			timestep

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

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

				# 1. compute alphas, betas
				__a           =			state.common.alphas_cumprod[t]
				__a           =			jnp.where(t > 0							, state.common.alphas_cumprod[t - 1]							, jnp.array(1.0							, dtype=self.dtype      )      )
				__a           =			1 - alpha_prod_t
				__a           =			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           =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
						__a           =			model_output
				elif self.config.prediction_type == "v_prediction":
						__a           =			(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           =			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
				__a           =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				__a           =			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           =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

				__a           =			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							, ):
				return add_noise_common(state.common							, _a							, _a							, _a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, ):
				return get_velocity_common(state.common							, _a							, _a							, _a      )





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










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			set()

		# Replace all the whitespace in our sentence
		__a           =			input_str.replace(''' '''    ,					''''''			)
		for alpha in input_str:
				if "a" <= alpha.lower() <= "z":
						frequency.add(alpha.lower()			)
		return len(lowerCAmelCase__			) == 26

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		__a           =			[False] * 26
		for char in input_str:
				if char.islower():
						__a           =			True
				elif char.isupper():
						__a           =			True
		return all(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     str = "The quick brown fox jumps over the lazy dog"    ,					) ->      bool:
		return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def        lowercase							(		) ->      None:
		from timeit import timeit

		__a           =			'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
		print(timeit('''is_pangram()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_faster()'''    ,					setup=lowerCAmelCase__			)			)
		print(timeit('''is_pangram_fastest()'''    ,					setup=lowerCAmelCase__			)			)
		# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
		# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()






