




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       torch.FloatTensor
	__magic_name__   :       Optional[torch.FloatTensor]      	=		None






def 				A__ (snake_case   :		Optional[int]		,	snake_case   :		Union[str, Any]=0.999		,	snake_case   :		Union[str, Any]="cosine"		,	)     ->						List[str]:
					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case   :		Optional[Any]				):
															return math.exp(t * -12.0				)

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

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







class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Union[str, Any]      	=		1






	@register_to_config
	def __init__(					self		:						Dict					,     lowerCAmelCase		:						int = 1000					,     lowerCAmelCase		:						float = 0.00_01					,     lowerCAmelCase		:						float = 0.02					,     lowerCAmelCase		:						str = "linear"					,     lowerCAmelCase		:						Optional[Union[np.ndarray, List[float]]] = None					,     lowerCAmelCase		:						bool = True					,     lowerCAmelCase		:						bool = True					,     lowerCAmelCase		:						int = 0					,     lowerCAmelCase		:						str = "epsilon"					,     lowerCAmelCase		:						float = 1.0					,     **lowerCAmelCase		:						Optional[Any]					,     )	->       Optional[Any]:







						"""simple docstring"""







						if kwargs.get("""set_alpha_to_one"""					,     lowerCAmelCase	) is not None:
											__UpperCamelCase					:	Dict             =      (
											    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
											)
											deprecate("""set_alpha_to_one"""					,     """1.0.0"""					,     lowerCAmelCase					,     standard_warn=lowerCAmelCase	)
											__UpperCamelCase					:	Optional[int]             =      kwargs["""set_alpha_to_one"""]
						if trained_betas is not None:
											__UpperCamelCase					:	int             =      torch.tensor(lowerCAmelCase					,     dtype=torch.floataa	)
						elif beta_schedule == "linear":
											__UpperCamelCase					:	int             =      torch.linspace(lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__UpperCamelCase					:	Dict             =      (
											    torch.linspace(beta_start**0.5					,     beta_end**0.5					,     lowerCAmelCase					,     dtype=torch.floataa	) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__UpperCamelCase					:	List[str]             =      betas_for_alpha_bar(lowerCAmelCase	)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

						__UpperCamelCase					:	Tuple             =      1.0 - self.betas
						__UpperCamelCase					:	str             =      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.
						__UpperCamelCase					:	Any             =      torch.tensor(0.0	) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						__UpperCamelCase					:	Tuple             =      1.0

						# setable values
						__UpperCamelCase					:	Tuple             =      None
						__UpperCamelCase					:	List[Any]             =      torch.from_numpy(np.arange(0					,     lowerCAmelCase	).copy().astype(np.intaa	)	)






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						torch.FloatTensor					,     lowerCAmelCase		:						Optional[int] = None	)	->       torch.FloatTensor:







						"""simple docstring"""







						return sample






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						Union[str, torch.device] = None	)	->       Any:







						"""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.'''	)

						__UpperCamelCase					:	List[str]             =      num_inference_steps
						__UpperCamelCase					:	Optional[int]             =      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
						__UpperCamelCase					:	Dict             =      (np.arange(0					,     lowerCAmelCase	) * step_ratio).round().copy().astype(np.intaa	)
						__UpperCamelCase					:	Tuple             =      torch.from_numpy(lowerCAmelCase	).to(lowerCAmelCase	)
						self.timesteps += self.config.steps_offset






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						torch.FloatTensor					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						torch.FloatTensor					,     lowerCAmelCase		:						float = 0.0					,     lowerCAmelCase		:						bool = False					,     lowerCAmelCase		:						Optional[torch.FloatTensor] = None					,     lowerCAmelCase		:						bool = True					,     )	->       Union[DDIMSchedulerOutput, Tuple]:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      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
						__UpperCamelCase					:	str             =      self.alphas_cumprod[timestep]
						__UpperCamelCase					:	str             =      (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						__UpperCamelCase					:	Tuple             =      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":
											__UpperCamelCase					:	Optional[Any]             =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											__UpperCamelCase					:	Optional[Any]             =      model_output
						elif self.config.prediction_type == "sample":
											__UpperCamelCase					:	Union[str, Any]             =      model_output
											__UpperCamelCase					:	Any             =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
											__UpperCamelCase					:	Union[str, Any]             =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											__UpperCamelCase					:	Tuple             =      (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:
											__UpperCamelCase					:	Dict             =      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
						__UpperCamelCase					:	Optional[int]             =      (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
						__UpperCamelCase					:	str             =      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=lowerCAmelCase					,     pred_original_sample=lowerCAmelCase	)






	def __len__(					self		:						str	)	->       Union[str, Any]:







						"""simple docstring"""







						return self.config.num_train_timesteps












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






