

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class       __magic_name__       (     _UpperCamelCase ):
			SCREAMING_SNAKE_CASE				  =		42
			SCREAMING_SNAKE_CASE				  =		None






def 			UpperCamelCase_(				_snake_case  :    Dict			,	_snake_case  :    int=0.999			,	_snake_case  :    List[Any]="cosine"			,	):




			"""simple docstring"""






			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_snake_case  :    Union[str, Any]      ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_snake_case  :    Dict      ):
									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       __magic_name__       (     _UpperCamelCase    ,       _UpperCamelCase ):
			SCREAMING_SNAKE_CASE				  =		1





			@register_to_config
			def __init__(						self   ,    __snake_case = 1000   ,    __snake_case = 0.0001   ,    __snake_case = 0.02   ,    __snake_case = "linear"   ,    __snake_case = None   ,    __snake_case = True   ,    __snake_case = True   ,    __snake_case = 0   ,    __snake_case = "epsilon"   ,    __snake_case = 1.0   ,    **__snake_case   ,    )  ->					Optional[int]:




						'''simple docstring'''
						if kwargs.get('set_alpha_to_one'   ,    __a   ) is not None:
									__a     =(
									    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
									)
									deprecate('set_alpha_to_one'   ,    '1.0.0'   ,    __a   ,    standard_warn=__a   )
									__a     =kwargs["set_alpha_to_one"]
						if trained_betas is not None:
									__a     =torch.tensor(__a   ,    dtype=torch.floataa   )
						elif beta_schedule == "linear":
									__a     =torch.linspace(__a   ,    __a   ,    __a   ,    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   ,    __a   ,    dtype=torch.floataa   ) ** 2
									)
						elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__a     =betas_for_alpha_bar(__a   )
						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   )

						# At every step in inverted ddim, we are looking into the next alphas_cumprod
						# For the final step, there is no next alphas_cumprod, and the index is out of bounds
						# `set_alpha_to_zero` decides whether we set this parameter simply to zero
						# in this case, self.step() just output the predicted noise
						# or whether we use the final alpha of the "non-previous" one.
						__a     =torch.tensor(0.0   ) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						__a     =1.0

						# setable values
						__a     =None
						__a     =torch.from_numpy(np.arange(0   ,    __a   ).copy().astype(np.intaa   )   )





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case = None   )  ->					torch.FloatTensor:




						'''simple docstring'''
						return sample





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case = None   )  ->					Dict:




						'''simple docstring'''

						if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
									    f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
									    f' maximal {self.config.num_train_timesteps} timesteps.'   )

						__a     =num_inference_steps
						__a     =self.config.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   ,    __a   ) * step_ratio).round().copy().astype(np.intaa   )
						__a     =torch.from_numpy(__a   ).to(__a   )
						self.timesteps += self.config.steps_offset





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case = 0.0   ,    __snake_case = False   ,    __snake_case = None   ,    __snake_case = True   ,    )  ->					Union[DDIMSchedulerOutput, Tuple]:




						'''simple docstring'''
						# 1. get previous step value (=t+1)
						__a     =timestep + self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						# change original implementation to exactly match noise levels for analogous forward process
						__a     =self.alphas_cumprod[timestep]
						__a     =(
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						__a     =1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						if self.config.prediction_type == "epsilon":
									__a     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									__a     =model_output
						elif self.config.prediction_type == "sample":
									__a     =model_output
									__a     =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
									__a     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									__a     =(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
						else:
									raise ValueError(
									    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
									    ' `v_prediction`'   )

						# 4. Clip or threshold "predicted x_0"
						if self.config.clip_sample:
									__a     =pred_original_sample.clamp(
									    -self.config.clip_sample_range   ,    self.config.clip_sample_range   )

						# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						__a     =(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

						# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						__a     =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if not return_dict:
									return (prev_sample, pred_original_sample)
						return DDIMSchedulerOutput(prev_sample=__a   ,    pred_original_sample=__a   )





			def __len__(						self   )  ->					str:




						'''simple docstring'''
						return self.config.num_train_timesteps













"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
