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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 					snake_case						(__lowercase					,			__lowercase=0.999					,			__lowercase="cosine"					,			)       ->       int:

						'''simple docstring'''



						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__lowercase						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__lowercase						):
																		return math.exp(t * -12.0						)

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

						_snake_case						:     Union[str, Any]				=    []
						for i in range(__lowercase						):
												_snake_case						:     Union[str, Any]				=    i / num_diffusion_timesteps
												_snake_case						:     Optional[int]				=    (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(__lowercase						) / alpha_bar_fn(__lowercase						)					,			__lowercase						)						)
						return torch.tensor(__lowercase					,			dtype=torch.floataa						)



class    lowercase_    (     __snake_case						, __snake_case		):
							_lowerCamelCase						=		[e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase						=		2



							@register_to_config
							def __init__(						self      ,				lowercase_ = 1_000      ,				lowercase_ = 0.00_085      ,				lowercase_ = 0.012      ,				lowercase_ = "linear"      ,				lowercase_ = None      ,				lowercase_ = "epsilon"      ,				lowercase_ = "linspace"      ,				lowercase_ = 0      ,				):
													if trained_betas is not None:
																			_snake_case						:     str				=    torch.tensor(lowercase_      ,				dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			_snake_case						:     str				=    torch.linspace(lowercase_      ,				lowercase_      ,				lowercase_      ,				dtype=torch.floataa				)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			_snake_case						:     int				=    (
																			    torch.linspace(beta_start**0.5      ,				beta_end**0.5      ,				lowercase_      ,				dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			_snake_case						:     Dict				=    betas_for_alpha_bar(lowercase_				)
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

													_snake_case						:     Union[str, Any]				=    1.0 - self.betas
													_snake_case						:     Dict				=    torch.cumprod(self.alphas      ,				dim=0				)

													#  set all values
													self.set_timesteps(lowercase_      ,				lowercase_      ,				lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_=None				):
													if schedule_timesteps is None:
																			_snake_case						:     str				=    self.timesteps

													_snake_case						:     Tuple				=    (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:
																			_snake_case						:     Tuple				=    1 if len(lowercase_				) > 1 else 0
													else:
																			_snake_case						:     Optional[int]				=    timestep.cpu().item() if torch.is_tensor(lowercase_				) else timestep
																			_snake_case						:     Union[str, Any]				=    self._index_counter[timestep_int]

													return indices[pos].item()



							@property
							def        UpperCamelCase       (						self				):
													# 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        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Union[str, Any]				=    self.index_for_timestep(lowercase_				)

													if self.state_in_first_order:
																			_snake_case						:     Dict				=    self.sigmas[step_index]
													else:
																			_snake_case						:     List[str]				=    self.sigmas_interpol[step_index - 1]

													_snake_case						:     Tuple				=    sample / ((sigma**2 + 1) ** 0.5)
													return sample



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_ = None      ,				lowercase_ = None      ,				):
													_snake_case						:     Tuple				=    num_inference_steps

													_snake_case						:     Dict				=    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":
																			_snake_case						:     Any				=    np.linspace(0      ,				num_train_timesteps - 1      ,				lowercase_      ,				dtype=lowercase_				)[::-1].copy()
													elif self.config.timestep_spacing == "leading":
																			_snake_case						:     List[str]				=    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
																			_snake_case						:     Dict				=    (np.arange(0      ,				lowercase_				) * step_ratio).round()[::-1].copy().astype(lowercase_				)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			_snake_case						:     str				=    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
																			_snake_case						:     Optional[int]				=    (np.arange(lowercase_      ,				0      ,				-step_ratio				)).round().copy().astype(lowercase_				)
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

													_snake_case						:     int				=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													_snake_case						:     Optional[Any]				=    torch.from_numpy(np.log(lowercase_				)				).to(lowercase_				)

													_snake_case						:     Optional[Any]				=    np.interp(lowercase_      ,				np.arange(0      ,				len(lowercase_				)				)      ,				lowercase_				)
													_snake_case						:     Any				=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													_snake_case						:     List[str]				=    torch.from_numpy(lowercase_				).to(device=lowercase_				)

													# compute up and down sigmas
													_snake_case						:     Any				=    sigmas.roll(-1				)
													_snake_case						:     List[Any]				=    0.0
													_snake_case						:     str				=    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
													_snake_case						:     Any				=    (sigmas_next**2 - sigmas_up**2) ** 0.5
													_snake_case						:     Any				=    0.0

													# compute interpolated sigmas
													_snake_case						:     Dict				=    sigmas.log().lerp(sigmas_down.log()      ,				0.5				).exp()
													_snake_case						:     Union[str, Any]				=    0.0

													# set sigmas
													_snake_case						:     Any				=    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
													_snake_case						:     Dict				=    torch.cat(
													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
													_snake_case						:     List[Any]				=    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
													_snake_case						:     Tuple				=    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

													if str(lowercase_				).startswith("mps"				):
																			# mps does not support float64
																			_snake_case						:     List[str]				=    torch.from_numpy(lowercase_				).to(lowercase_      ,				dtype=torch.floataa				)
													else:
																			_snake_case						:     Dict				=    torch.from_numpy(lowercase_				).to(lowercase_				)

													_snake_case						:     List[str]				=    self.sigma_to_t(lowercase_				).to(lowercase_      ,				dtype=timesteps.dtype				)
													_snake_case						:     Dict				=    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,				dim=-1				).flatten()

													_snake_case						:     Tuple				=    torch.cat([timesteps[:1], interleaved_timesteps]				)

													_snake_case						:     Union[str, Any]				=    None

													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
													# we need an index counter
													_snake_case						:     Dict				=    defaultdict(lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													# get log sigma
													_snake_case						:     List[str]				=    sigma.log()

													# get distribution
													_snake_case						:     str				=    log_sigma - self.log_sigmas[:, None]

													# get sigmas range
													_snake_case						:     Any				=    dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
													_snake_case						:     Optional[int]				=    low_idx + 1

													_snake_case						:     Optional[Any]				=    self.log_sigmas[low_idx]
													_snake_case						:     Optional[Any]				=    self.log_sigmas[high_idx]

													# interpolate sigmas
													_snake_case						:     Union[str, Any]				=    (low - log_sigma) / (low - high)
													_snake_case						:     List[str]				=    w.clamp(0      ,				1				)

													# transform interpolation to time range
													_snake_case						:     int				=    (1 - w) * low_idx + w * high_idx
													_snake_case						:     Optional[Any]				=    t.view(sigma.shape				)
													return t



							@property
							def        UpperCamelCase       (						self				):
													return self.sample is None



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = None      ,				lowercase_ = True      ,				):
													_snake_case						:     Tuple				=    self.index_for_timestep(lowercase_				)

													# advance index counter by 1
													_snake_case						:     Union[str, Any]				=    timestep.cpu().item() if torch.is_tensor(lowercase_				) else timestep
													self._index_counter[timestep_int] += 1

													if self.state_in_first_order:
																			_snake_case						:     Optional[int]				=    self.sigmas[step_index]
																			_snake_case						:     Dict				=    self.sigmas_interpol[step_index]
																			_snake_case						:     Tuple				=    self.sigmas_up[step_index]
																			_snake_case						:     int				=    self.sigmas_down[step_index - 1]
													else:
																			# 2nd order / KPDM2's method
																			_snake_case						:     Optional[int]				=    self.sigmas[step_index - 1]
																			_snake_case						:     Optional[Any]				=    self.sigmas_interpol[step_index - 1]
																			_snake_case						:     List[str]				=    self.sigmas_up[step_index - 1]
																			_snake_case						:     List[Any]				=    self.sigmas_down[step_index - 1]

													# 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
													_snake_case						:     int				=    0
													_snake_case						:     Optional[int]				=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

													_snake_case						:     Optional[Any]				=    model_output.device
													_snake_case						:     Dict				=    randn_tensor(model_output.shape      ,				dtype=model_output.dtype      ,				device=lowercase_      ,				generator=lowercase_				)

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			_snake_case						:     List[Any]				=    sigma_hat if self.state_in_first_order else sigma_interpol
																			_snake_case						:     Dict				=    sample - sigma_input * model_output
													elif self.config.prediction_type == "v_prediction":
																			_snake_case						:     List[Any]				=    sigma_hat if self.state_in_first_order else sigma_interpol
																			_snake_case						:     int				=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError("prediction_type not implemented yet: sample"				)
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													if self.state_in_first_order:
																			# 2. Convert to an ODE derivative for 1st order
																			_snake_case						:     List[Any]				=    (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			_snake_case						:     Any				=    sigma_interpol - sigma_hat

																			# store for 2nd order step
																			_snake_case						:     int				=    sample
																			_snake_case						:     List[str]				=    dt
																			_snake_case						:     Any				=    sample + derivative * dt
													else:
																			# DPM-Solver-2
																			# 2. Convert to an ODE derivative for 2nd order
																			_snake_case						:     Any				=    (sample - pred_original_sample) / sigma_interpol
																			# 3. delta timestep
																			_snake_case						:     Dict				=    sigma_down - sigma_hat

																			_snake_case						:     Optional[Any]				=    self.sample
																			_snake_case						:     Dict				=    None

																			_snake_case						:     Optional[Any]				=    sample + derivative * dt
																			_snake_case						:     Optional[Any]				=    prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													_snake_case						:     Union[str, Any]				=    self.sigmas.to(device=original_samples.device      ,				dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_				):
																			# mps does not support float64
																			_snake_case						:     Any				=    self.timesteps.to(original_samples.device      ,				dtype=torch.floataa				)
																			_snake_case						:     Any				=    timesteps.to(original_samples.device      ,				dtype=torch.floataa				)
													else:
																			_snake_case						:     int				=    self.timesteps.to(original_samples.device				)
																			_snake_case						:     List[str]				=    timesteps.to(original_samples.device				)

													_snake_case						:     Dict				=    [self.index_for_timestep(lowercase_      ,				lowercase_				) for t in timesteps]

													_snake_case						:     Tuple				=    sigmas[step_indices].flatten()
													while len(sigma.shape				) < len(original_samples.shape				):
																			_snake_case						:     List[str]				=    sigma.unsqueeze(-1				)

													_snake_case						:     int				=    original_samples + noise * sigma
													return noisy_samples



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

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation



@require_tf
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
													_snake_case						:     List[Any]				=    get_tf_activation("gelu"				)
													_snake_case						:     Any				=    get_tf_activation("gelu_10"				)

													_snake_case						:     Dict				=    gelu(lowercase_				)
													_snake_case						:     Optional[int]				=    geluaa(lowercase_				)

													_snake_case						:     Tuple				=    tf.where(y_gelu_aa < 10.0      ,				1.0      ,				0.0				)

													self.assertEqual(tf.math.reduce_max(lowercase_				).numpy().item()      ,				10.0				)
													self.assertTrue(np.allclose(y_gelu * clipped_mask      ,				y_gelu_aa * clipped_mask				)				)



							def        UpperCamelCase       (						self				):
													get_tf_activation("gelu"				)
													get_tf_activation("gelu_10"				)
													get_tf_activation("gelu_fast"				)
													get_tf_activation("gelu_new"				)
													get_tf_activation("glu"				)
													get_tf_activation("mish"				)
													get_tf_activation("quick_gelu"				)
													get_tf_activation("relu"				)
													get_tf_activation("sigmoid"				)
													get_tf_activation("silu"				)
													get_tf_activation("swish"				)
													get_tf_activation("tanh"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation("bogus"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation(lowercase_				)