


"""simple docstring"""




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase_			     =  "scheduler_config.json"
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				1
		__UpperCAmelCase		:			Dict										=				2
		__UpperCAmelCase		:			int										=				3
		__UpperCAmelCase		:			Tuple										=				4
		__UpperCAmelCase		:			Union[str, Any]										=				5







@dataclass
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			jnp.ndarray







class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				SCHEDULER_CONFIG_NAME
		__UpperCAmelCase		:			str										=				['dtype']
		__UpperCAmelCase		:			List[str]										=				[]
		__UpperCAmelCase		:			Any										=				True

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a = None							, _a = None							, _a=False							, **_a							, ):
				__a      ,     __a           =			cls.load_config(
				    pretrained_model_name_or_path=_a							, subfolder=_a							, return_unused_kwargs=_a							, **_a							, )
				__a      ,     __a           =			cls.from_config(_a							, return_unused_kwargs=_a							, **_a      )

				if hasattr(_a							, '''create_state'''      ) and getattr(_a							, '''has_state'''							, _a      ):
						__a           =			scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state

		def 			__UpperCAmelCase       (  self							, _a							, _a = False							, **_a      ):
				self.save_config(save_directory=_a							, push_to_hub=_a							, **_a      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self._get_compatibles()





		@classmethod
		def 			__UpperCAmelCase       (  cls      ):
				__a           =			list(set([cls.__name__] + cls._compatibles      )      )
				__a           =			importlib.import_module(__name__.split('''.'''      )[0]      )
				__a           =			[
				    getattr(_a							, _a      ) for c in compatible_classes_str if hasattr(_a							, _a      )
				]
				return compatible_classes







def        lowercase							(		lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     Tuple[int]			) ->      jnp.ndarray:
		assert len(lowerCAmelCase__			) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__			) - x.ndim)			)    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str=0.9_99    ,					lowerCAmelCase__ :     List[str]=jnp.floataa			) ->      jnp.ndarray:

		def alpha_bar(lowerCAmelCase__ :     str			):
				return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

		__a           =			[]
		for i in range(lowerCAmelCase__			):
				__a           =			i / num_diffusion_timesteps
				__a           =			(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowerCAmelCase__			) / alpha_bar(lowerCAmelCase__			)    ,					lowerCAmelCase__			)			)
		return jnp.array(lowerCAmelCase__    ,					dtype=lowerCAmelCase__			)
@flax.struct.dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			jnp.ndarray
		__UpperCAmelCase		:			jnp.ndarray

		@classmethod
		def 			__UpperCAmelCase       (  cls							, _a      ):
				__a           =			scheduler.config

				if config.trained_betas is not None:
						__a           =			jnp.asarray(config.trained_betas							, dtype=scheduler.dtype      )
				elif config.beta_schedule == "linear":
						__a           =			jnp.linspace(config.beta_start							, config.beta_end							, config.num_train_timesteps							, dtype=scheduler.dtype      )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						__a           =			(
						    jnp.linspace(
						        config.beta_start**0.5							, config.beta_end**0.5							, config.num_train_timesteps							, dtype=scheduler.dtype      )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						__a           =			betas_for_alpha_bar(config.num_train_timesteps							, dtype=scheduler.dtype      )
				else:
						raise NotImplementedError(
						    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''      )

				__a           =			1.0 - betas

				__a           =			jnp.cumprod(_a							, axis=0      )

				return cls(
				    alphas=_a							, betas=_a							, alphas_cumprod=_a							, )







def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      Optional[int]:
		__a           =			state.alphas_cumprod

		__a           =			alphas_cumprod[timesteps] ** 0.5
		__a           =			sqrt_alpha_prod.flatten()
		__a           =			broadcast_to_shape_from_left(lowerCAmelCase__    ,					original_samples.shape			)

		__a           =			(1 - alphas_cumprod[timesteps]) ** 0.5
		__a           =			sqrt_one_minus_alpha_prod.flatten()
		__a           =			broadcast_to_shape_from_left(lowerCAmelCase__    ,					original_samples.shape			)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      Dict:
		__a      ,     __a           =			get_sqrt_alpha_prod(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples




def        lowercase							(		lowerCAmelCase__ :     CommonSchedulerState    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray    ,					lowerCAmelCase__ :     jnp.ndarray			) ->      List[Any]:
		__a      ,     __a           =			get_sqrt_alpha_prod(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[Any]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		if n == 0:
				return 0
		__a           =			float('''-inf'''			)
		for i in range(1    ,					n + 1			):
				__a           =			max(
				    lowerCAmelCase__    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					lowerCAmelCase__			)			)

		return max_revue

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[str]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		return _top_down_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     list			) ->      Union[str, Any]:
		if max_rev[n] >= 0:
				return max_rev[n]
		elif n == 0:
				return 0
		else:
				__a           =			float('''-inf'''			)
				for i in range(1    ,					n + 1			):
						__a           =			max(
						    lowerCAmelCase__    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)    ,					)

				__a           =			max_revenue

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      Dict:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)

		# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
		# length 0.
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		__a           =			0

		for i in range(1    ,					n + 1			):
				__a           =			max_rev[i]
				for j in range(1    ,					i + 1			):
						__a           =			max(lowerCAmelCase__    ,					prices[j - 1] + max_rev[i - j]			)

				__a           =			max_revenue_i

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      str:
		if n < 0:
				__a           =			f'''n must be greater than or equal to 0. Got n = {n}'''
				raise ValueError(lowerCAmelCase__			)

		if n > len(lowerCAmelCase__			):
				__a           =			(
				    '''Each integral piece of rod must have a corresponding price. '''
				    f'''Got n = {n} but length of prices = {len(lowerCAmelCase__			)}'''
				)
				raise ValueError(lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			[6, 10, 12, 15, 20, 23]
		__a           =			len(lowerCAmelCase__			)

		# the best revenue comes from cutting the rod into 6 pieces, each
		# of length 1 resulting in a revenue of 6 * 6 = 36.
		__a           =			36

		__a           =			top_down_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			bottom_up_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			naive_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__			)

		assert expected_max_revenue == max_rev_top_down
		assert max_rev_top_down == max_rev_bottom_up
		assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
				main()






