
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        a_		(       __lowercase   :	Dict       ,					__lowercase   :	str=0.9_9_9       ,					__lowercase   :	Union[str, Any]="cosine"       ,					) ->  Union[str, Any]:
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__lowercase   :	Union[str, Any] ):
									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(__lowercase   :	Any ):
									return math.exp(t * -1_2.0 )

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

			_snake_case		=			[]
			for i in range(__lowercase ):
						_snake_case		=			i / num_diffusion_timesteps
						_snake_case		=			(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    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					[e.name for e in KarrasDiffusionSchedulers]
	_UpperCAmelCase       :		List[str]       =					2



	@register_to_config
	def __init__(  self	:    Tuple			,  lowercase	:    int = 1_000			,  lowercase	:    float = 0.00085			,  lowercase	:    float = 0.012			,  lowercase	:    str = "linear"			,  lowercase	:    Optional[Union[np.ndarray, List[float]]] = None			,  lowercase	:    str = "epsilon"			,  lowercase	:    Optional[bool] = False			,  lowercase	:    Optional[bool] = False			,  lowercase	:    float = 1.0			,  lowercase	:    str = "linspace"			,  lowercase	:    int = 0			,  ):


				'''simple docstring'''


				if trained_betas is not None:
							_snake_case		=			torch.tensor(lowercase			,  dtype=torch.floataa	)
				elif beta_schedule == "linear":
							_snake_case		=			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		=			(
							    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		=			betas_for_alpha_bar(lowercase			,  alpha_transform_type='cosine'	)
				elif beta_schedule == "exp":
							_snake_case		=			betas_for_alpha_bar(lowercase			,  alpha_transform_type='exp'	)
				else:
							raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

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



	def  A							(  self	:    Any			,  lowercase	:    Optional[Any]			,  lowercase	:    Any=None	):


				'''simple docstring'''


				if schedule_timesteps is None:
							_snake_case		=			self.timesteps

				_snake_case		=			(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		=			1 if len(lowercase	) > 1 else 0
				else:
							_snake_case		=			timestep.cpu().item() if torch.is_tensor(lowercase	) else timestep
							_snake_case		=			self._index_counter[timestep_int]

				return indices[pos].item()



	@property
	def  A							(  self	:    Any	):


				'''simple docstring'''


				if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

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



	def  A							(  self	:    List[str]			,  lowercase	:    torch.FloatTensor			,  lowercase	:    Union[float, torch.FloatTensor]			,  ):


				'''simple docstring'''


				_snake_case		=			self.index_for_timestep(lowercase	)

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



	def  A							(  self	:    Any			,  lowercase	:    int			,  lowercase	:    Union[str, torch.device] = None			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				_snake_case		=			num_inference_steps

				_snake_case		=			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		=			np.linspace(0			,  num_train_timesteps - 1			,  lowercase			,  dtype=lowercase	)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
							_snake_case		=			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		=			(np.arange(0			,  lowercase	) * step_ratio).round()[::-1].copy().astype(lowercase	)
							timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
							_snake_case		=			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		=			(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		=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
				_snake_case		=			np.log(lowercase	)
				_snake_case		=			np.interp(lowercase			,  np.arange(0			,  len(lowercase	)	)			,  lowercase	)

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

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

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

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

				# empty dt and derivative
				_snake_case		=			None
				_snake_case		=			None

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



	def  A							(  self	:    List[str]			,  lowercase	:    str			,  lowercase	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			np.log(lowercase	)

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

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

				_snake_case		=			log_sigmas[low_idx]
				_snake_case		=			log_sigmas[high_idx]

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

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



	def  A							(  self	:    Optional[int]			,  lowercase	:    torch.FloatTensor			,  lowercase	:    str	):


				'''simple docstring'''


				_snake_case		=			in_sigmas[-1].item()
				_snake_case		=			in_sigmas[0].item()

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



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				return self.dt is None



	def  A							(  self	:    List[Any]			,  lowercase	:    Union[torch.FloatTensor, np.ndarray]			,  lowercase	:    Union[float, torch.FloatTensor]			,  lowercase	:    Union[torch.FloatTensor, np.ndarray]			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			self.index_for_timestep(lowercase	)

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

				if self.state_in_first_order:
							_snake_case		=			self.sigmas[step_index]
							_snake_case		=			self.sigmas[step_index + 1]
				else:
							# 2nd order / Heun's method
							_snake_case		=			self.sigmas[step_index - 1]
							_snake_case		=			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
				_snake_case		=			0
				_snake_case		=			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":
							_snake_case		=			sigma_hat if self.state_in_first_order else sigma_next
							_snake_case		=			sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
							_snake_case		=			sigma_hat if self.state_in_first_order else sigma_next
							_snake_case		=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
							    sample / (sigma_input**2 + 1)
							)
				elif self.config.prediction_type == "sample":
							_snake_case		=			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:
							_snake_case		=			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
							_snake_case		=			(sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							_snake_case		=			sigma_next - sigma_hat

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

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

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

				_snake_case		=			sample + derivative * dt

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=lowercase	)



	def  A							(  self	:    Dict			,  lowercase	:    torch.FloatTensor			,  lowercase	:    torch.FloatTensor			,  lowercase	:    torch.FloatTensor			,  ):


				'''simple docstring'''


				_snake_case		=			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		=			self.timesteps.to(original_samples.device			,  dtype=torch.floataa	)
							_snake_case		=			timesteps.to(original_samples.device			,  dtype=torch.floataa	)
				else:
							_snake_case		=			self.timesteps.to(original_samples.device	)
							_snake_case		=			timesteps.to(original_samples.device	)

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

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

				_snake_case		=			original_samples + noise * sigma
				return noisy_samples





	def __len__(  self	:    Dict	):


				'''simple docstring'''


				return self.config.num_train_timesteps

import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_lowerCamelCase       :				Dict       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  *lowercase	:    Optional[int]			,  **lowercase	:    Any	):


				'''simple docstring'''


				warnings.warn(
				    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use ChineseCLIPImageProcessor instead.'			,  lowercase			,  )
				super().__init__(*lowercase			,  **lowercase	)