
from typing import List

import numpy as np


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  {key: len(lowercase				) for key, value in gen_kwargs.items() if isinstance(lowercase					, lowercase				)}
		if len(set(lists_lengths.values()				)				) > 1:
				raise RuntimeError(
				    (
				        'Sharding is ambiguous for this dataset: '
				        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
				        + '\n'.join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()				)
				        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
				        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
				    )				)
		UpperCamelCase						=  max(lists_lengths.values()					, default=0				)
		return max(1					, lowercase				)


def    A (      lowercase					, lowercase				)     ->						List[range]:
		'''simple docstring'''

		UpperCamelCase						=  []
		for group_idx in range(lowercase				):
				UpperCamelCase						=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				UpperCamelCase						=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				UpperCamelCase						=  range(lowercase					, start + num_shards_to_add				)
				shards_indices_per_group.append(lowercase				)
		return shards_indices_per_group


def    A (      lowercase					, lowercase				)     ->						List[dict]:
		'''simple docstring'''

		UpperCamelCase						=  _number_of_shards_in_gen_kwargs(lowercase				)
		if num_shards == 1:
				return [dict(lowercase				)]
		else:
				UpperCamelCase						=  _distribute_shards(num_shards=lowercase					, max_num_jobs=lowercase				)
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(lowercase					, lowercase				)
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(lowercase				)				)
				]


def    A (      lowercase				)     ->						dict:
		'''simple docstring'''

		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key]					, lowercase				)
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}


def    A (      lowercase					, lowercase				)     ->						dict:
		'''simple docstring'''

		UpperCamelCase						=  {len(lowercase				) for value in gen_kwargs.values() if isinstance(lowercase					, lowercase				)}
		UpperCamelCase						=  {}
		for size in list_sizes:
				UpperCamelCase						=  list(range(lowercase				)				)
				rng.shuffle(indices_per_size[size]				)
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		UpperCamelCase						=  dict(lowercase				)
		for key, value in shuffled_kwargs.items():
				if isinstance(lowercase					, lowercase				):
						UpperCamelCase						=  [value[i] for i in indices_per_size[len(lowercase				)]]
		return shuffled_kwargs


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


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  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.
						UpperCamelCase						=  (
						    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
						UpperCamelCase						=  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__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

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






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

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

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

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
