

"""simple docstring"""




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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=0.9_99     ,				lowercase__="cosine"     ,				):
					"""simple docstring"""
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(lowercase__ ):
															return math.cos((t + 0.0_08) / 1.0_08 * 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}""" )

					A     =   []
					for i in range(lowercase__ ):
										A     =   i / num_diffusion_timesteps
										A     =   (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      __UpperCamelCase					(  _A	,						_A		):
		SCREAMING_SNAKE_CASE														=       [e.name for e in KarrasDiffusionSchedulers]
		SCREAMING_SNAKE_CASE														=       2


		@register_to_config
		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						int = 1_0_0_0	,	__SCREAMING_SNAKE_CASE					:						float = 0.0_0_0_8_5	,	__SCREAMING_SNAKE_CASE					:						float = 0.0_1_2	,	__SCREAMING_SNAKE_CASE					:						str = "linear"	,	__SCREAMING_SNAKE_CASE					:						Optional[Union[np.ndarray, List[float]]] = None	,	__SCREAMING_SNAKE_CASE					:						str = "epsilon"	,	__SCREAMING_SNAKE_CASE					:						str = "linspace"	,	__SCREAMING_SNAKE_CASE					:						int = 0	,	):
							if trained_betas is not None:
												A     =   torch.tensor(__SCREAMING_SNAKE_CASE	,	dtype=torch.floataa)
							elif beta_schedule == "linear":
												A     =   torch.linspace(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	dtype=torch.floataa)
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												A     =   (
												    torch.linspace(beta_start**0.5	,	beta_end**0.5	,	__SCREAMING_SNAKE_CASE	,	dtype=torch.floataa) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												A     =   betas_for_alpha_bar(__SCREAMING_SNAKE_CASE)
							else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int=None):
							if schedule_timesteps is None:
												A     =   self.timesteps

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

							return indices[pos].item()


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							# 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	,	__SCREAMING_SNAKE_CASE					:						torch.FloatTensor	,	__SCREAMING_SNAKE_CASE					:						Union[float, torch.FloatTensor]	,	):
							A     =   self.index_for_timestep(__SCREAMING_SNAKE_CASE)

							if self.state_in_first_order:
												A     =   self.sigmas[step_index]
							else:
												A     =   self.sigmas_interpol[step_index - 1]

							A     =   sample / ((sigma**2 + 1) ** 0.5)
							return sample


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, torch.device] = None	,	__SCREAMING_SNAKE_CASE					:						Optional[int] = None	,	):
							A     =   num_inference_steps

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

							A     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
							A     =   torch.from_numpy(np.log(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

							A     =   np.interp(__SCREAMING_SNAKE_CASE	,	np.arange(0	,	len(__SCREAMING_SNAKE_CASE))	,	__SCREAMING_SNAKE_CASE)
							A     =   np.concatenate([sigmas, [0.0]]).astype(np.floataa)
							A     =   torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)

							# compute up and down sigmas
							A     =   sigmas.roll(-1)
							A     =   0.0
							A     =   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							A     =   (sigmas_next**2 - sigmas_up**2) ** 0.5
							A     =   0.0

							# compute interpolated sigmas
							A     =   sigmas.log().lerp(sigmas_down.log()	,	0.5).exp()
							A     =   0.0

							# set sigmas
							A     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
							A     =   torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
							A     =   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
							A     =   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

							if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
												# mps does not support float64
												A     =   torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE	,	dtype=torch.floataa)
							else:
												A     =   torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)

							A     =   self.sigma_to_t(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE	,	dtype=timesteps.dtype)
							A     =   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,	dim=-1).flatten()

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

							A     =   None

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							# get log sigma
							A     =   sigma.log()

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

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

							A     =   self.log_sigmas[low_idx]
							A     =   self.log_sigmas[high_idx]

							# interpolate sigmas
							A     =   (low - log_sigma) / (low - high)
							A     =   w.clamp(0	,	1)

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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						Union[torch.FloatTensor, np.ndarray]	,	__SCREAMING_SNAKE_CASE					:						Union[float, torch.FloatTensor]	,	__SCREAMING_SNAKE_CASE					:						Union[torch.FloatTensor, np.ndarray]	,	__SCREAMING_SNAKE_CASE					:						Optional[torch.Generator] = None	,	__SCREAMING_SNAKE_CASE					:						bool = True	,	):
							A     =   self.index_for_timestep(__SCREAMING_SNAKE_CASE)

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

							if self.state_in_first_order:
												A     =   self.sigmas[step_index]
												A     =   self.sigmas_interpol[step_index]
												A     =   self.sigmas_up[step_index]
												A     =   self.sigmas_down[step_index - 1]
							else:
												# 2nd order / KPDM2's method
												A     =   self.sigmas[step_index - 1]
												A     =   self.sigmas_interpol[step_index - 1]
												A     =   self.sigmas_up[step_index - 1]
												A     =   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
							A     =   0
							A     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							A     =   model_output.device
							A     =   randn_tensor(model_output.shape	,	dtype=model_output.dtype	,	device=__SCREAMING_SNAKE_CASE	,	generator=__SCREAMING_SNAKE_CASE)

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
												A     =   sigma_hat if self.state_in_first_order else sigma_interpol
												A     =   sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
												A     =   sigma_hat if self.state_in_first_order else sigma_interpol
												A     =   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
												A     =   (sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												A     =   sigma_interpol - sigma_hat

												# store for 2nd order step
												A     =   sample
												A     =   dt
												A     =   sample + derivative * dt
							else:
												# DPM-Solver-2
												# 2. Convert to an ODE derivative for 2nd order
												A     =   (sample - pred_original_sample) / sigma_interpol
												# 3. delta timestep
												A     =   sigma_down - sigma_hat

												A     =   self.sample
												A     =   None

												A     =   sample + derivative * dt
												A     =   prev_sample + noise * sigma_up

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE)


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

							A     =   [self.index_for_timestep(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE) for t in timesteps]

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

							A     =   original_samples + noise * sigma
							return noisy_samples


		def __len__(self					:						Tuple):
							return self.config.num_train_timesteps







'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



