
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def  __snake_case							(	__magic_name__		,    __magic_name__=0.9_9_9		,    __magic_name__="cosine"		,    ):







				'''simple docstring'''


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__magic_name__						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__magic_name__						):
												return math.exp(t * -1_2.0						)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

				lowercase         =      []
				for i in range(__magic_name__						):
								lowercase         =      i / num_diffusion_timesteps
								lowercase         =      (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__magic_name__						) / alpha_bar_fn(__magic_name__						)		,    __magic_name__						)						)
				return torch.tensor(__magic_name__		,    dtype=torch.floataa						)





class   UpperCamelCase_      (      __a		,    __a		):







						'''simple docstring'''



						UpperCamelCase      :  Tuple				   =					[e.name for e in KarrasDiffusionSchedulers]
						UpperCamelCase      :  str				   =					2





						@register_to_config
						def __init__(			self							:Union[str, Any]		,    lowerCAmelCase__							:int = 1000		,    lowerCAmelCase__							:float = 0.0_00_85		,    lowerCAmelCase__							:float = 0.0_12		,    lowerCAmelCase__							:str = "linear"		,    lowerCAmelCase__							:Optional[Union[np.ndarray, List[float]]] = None		,    lowerCAmelCase__							:str = "epsilon"		,    lowerCAmelCase__							:Optional[bool] = False		,    lowerCAmelCase__							:Optional[bool] = False		,    lowerCAmelCase__							:float = 1.0		,    lowerCAmelCase__							:str = "linspace"		,    lowerCAmelCase__							:int = 0		,    )  ->Dict:
										if trained_betas is not None:
														lowercase         =      torch.tensor(lowerCAmelCase__		,    dtype=torch.floataa     )
										elif beta_schedule == "linear":
														lowercase         =      torch.linspace(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    dtype=torch.floataa     )
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowercase         =      (
														    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    lowerCAmelCase__		,    dtype=torch.floataa     ) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowercase         =      betas_for_alpha_bar(lowerCAmelCase__		,    alpha_transform_type="cosine"     )
										elif beta_schedule == "exp":
														lowercase         =      betas_for_alpha_bar(lowerCAmelCase__		,    alpha_transform_type="exp"     )
										else:
														raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

										lowercase         =      1.0 - self.betas
										lowercase         =      torch.cumprod(self.alphas		,    dim=0     )

										#  set all values
										self.set_timesteps(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      use_karras_sigmas





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Optional[int]=None     )  ->List[Any]:
										if schedule_timesteps is None:
														lowercase         =      self.timesteps

										lowercase         =      (schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter     ) == 0:
														lowercase         =      1 if len(lowerCAmelCase__     ) > 1 else 0
										else:
														lowercase         =      timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__     ) else timestep
														lowercase         =      self._index_counter[timestep_int]

										return indices[pos].item()





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:torch.FloatTensor		,    lowerCAmelCase__							:Union[float, torch.FloatTensor]		,    )  ->torch.FloatTensor:
										lowercase         =      self.index_for_timestep(lowerCAmelCase__     )

										lowercase         =      self.sigmas[step_index]
										lowercase         =      sample / ((sigma**2 + 1) ** 0.5)
										return sample





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:Union[str, torch.device] = None		,    lowerCAmelCase__							:Optional[int] = None		,    )  ->Dict:
										lowercase         =      num_inference_steps

										lowercase         =      num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
														lowercase         =      np.linspace(0		,    num_train_timesteps - 1		,    lowerCAmelCase__		,    dtype=lowerCAmelCase__     )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														lowercase         =      num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowercase         =      (np.arange(0		,    lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowercase         =      num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowercase         =      (np.arange(lowerCAmelCase__		,    0		,    -step_ratio     )).round().copy().astype(lowerCAmelCase__     )
														timesteps -= 1
										else:
														raise ValueError(
														    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

										lowercase         =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										lowercase         =      np.log(lowerCAmelCase__     )
										lowercase         =      np.interp(lowerCAmelCase__		,    np.arange(0		,    len(lowerCAmelCase__     )     )		,    lowerCAmelCase__     )

										if self.config.use_karras_sigmas:
														lowercase         =      self._convert_to_karras(in_sigmas=lowerCAmelCase__		,    num_inference_steps=self.num_inference_steps     )
														lowercase         =      np.array([self._sigma_to_t(lowerCAmelCase__		,    lowerCAmelCase__     ) for sigma in sigmas]     )

										lowercase         =      np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
										lowercase         =      torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
										lowercase         =      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2     ), sigmas[-1:]]     )

										lowercase         =      torch.from_numpy(lowerCAmelCase__     )
										lowercase         =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2     )]     )

										if str(lowerCAmelCase__     ).startswith("mps"     ):
														# mps does not support float64
														lowercase         =      timesteps.to(lowerCAmelCase__		,    dtype=torch.floataa     )
										else:
														lowercase         =      timesteps.to(device=lowerCAmelCase__     )

										# empty dt and derivative
										lowercase         =      None
										lowercase         =      None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowercase         =      defaultdict(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Dict     )  ->Optional[int]:
										# get log sigma
										lowercase         =      np.log(lowerCAmelCase__     )

										# get distribution
										lowercase         =      log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										lowercase         =      np.cumsum((dists >= 0)		,    axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
										lowercase         =      low_idx + 1

										lowercase         =      log_sigmas[low_idx]
										lowercase         =      log_sigmas[high_idx]

										# interpolate sigmas
										lowercase         =      (low - log_sigma) / (low - high)
										lowercase         =      np.clip(lowerCAmelCase__		,    0		,    1     )

										# transform interpolation to time range
										lowercase         =      (1 - w) * low_idx + w * high_idx
										lowercase         =      t.reshape(sigma.shape     )
										return t





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:torch.FloatTensor		,    lowerCAmelCase__							:Tuple     )  ->torch.FloatTensor:
										lowercase         =      in_sigmas[-1].item()
										lowercase         =      in_sigmas[0].item()

										lowercase         =      7.0  # 7.0 is the value used in the paper
										lowercase         =      np.linspace(0		,    1		,    lowerCAmelCase__     )
										lowercase         =      sigma_min ** (1 / rho)
										lowercase         =      sigma_max ** (1 / rho)
										lowercase         =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas





						@property
						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->List[Any]:
										return self.dt is None





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Union[torch.FloatTensor, np.ndarray]		,    lowerCAmelCase__							:Union[float, torch.FloatTensor]		,    lowerCAmelCase__							:Union[torch.FloatTensor, np.ndarray]		,    lowerCAmelCase__							:bool = True		,    )  ->Union[SchedulerOutput, Tuple]:
										lowercase         =      self.index_for_timestep(lowerCAmelCase__     )

										# advance index counter by 1
										lowercase         =      timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__     ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
														lowercase         =      self.sigmas[step_index]
														lowercase         =      self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														lowercase         =      self.sigmas[step_index - 1]
														lowercase         =      self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										lowercase         =      0
										lowercase         =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
														lowercase         =      sigma_hat if self.state_in_first_order else sigma_next
														lowercase         =      sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														lowercase         =      sigma_hat if self.state_in_first_order else sigma_next
														lowercase         =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										elif self.config.prediction_type == "sample":
														lowercase         =      model_output
										else:
														raise ValueError(
														    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

										if self.config.clip_sample:
														lowercase         =      pred_original_sample.clamp(
														    -self.config.clip_sample_range		,    self.config.clip_sample_range     )

										if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														lowercase         =      (sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														lowercase         =      sigma_next - sigma_hat

														# store for 2nd order step
														lowercase         =      derivative
														lowercase         =      dt
														lowercase         =      sample
										else:
														# 2. 2nd order / Heun's method
														lowercase         =      (sample - pred_original_sample) / sigma_next
														lowercase         =      (self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowercase         =      self.dt
														lowercase         =      self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowercase         =      None
														lowercase         =      None
														lowercase         =      None

										lowercase         =      sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:torch.FloatTensor		,    lowerCAmelCase__							:torch.FloatTensor		,    lowerCAmelCase__							:torch.FloatTensor		,    )  ->torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowercase         =      self.sigmas.to(device=original_samples.device		,    dtype=original_samples.dtype     )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__     ):
														# mps does not support float64
														lowercase         =      self.timesteps.to(original_samples.device		,    dtype=torch.floataa     )
														lowercase         =      timesteps.to(original_samples.device		,    dtype=torch.floataa     )
										else:
														lowercase         =      self.timesteps.to(original_samples.device     )
														lowercase         =      timesteps.to(original_samples.device     )

										lowercase         =      [self.index_for_timestep(lowerCAmelCase__		,    lowerCAmelCase__     ) for t in timesteps]

										lowercase         =      sigmas[step_indices].flatten()
										while len(sigma.shape     ) < len(original_samples.shape     ):
														lowercase         =      sigma.unsqueeze(-1     )

										lowercase         =      original_samples + noise * sigma
										return noisy_samples





						def __len__(			self							:Dict     )  ->List[Any]:
										return self.config.num_train_timesteps


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							int     	=    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

										if len(lowerCAmelCase__     ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__     ) > 2:
														raise ValueError(
														    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														lowercase         =      "gelu_new"

										super().__init__(
										    pad_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__		,    is_encoder_decoder=lowerCAmelCase__		,    **lowerCAmelCase__		,    )
