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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class      SCREAMING_SNAKE_CASE__  (			A__      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[Any]					     =      4_2
	_UpperCAmelCase :	List[str]					     =      4_2






def  a_	( __lowercase     :			List[str] ,      __lowercase     :			int=0.9_9_9 ,      __lowercase     :			List[Any]="cosine" ,      )				->						Optional[int]:







			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__lowercase     :			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     :			Tuple				):
									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__  (			A__							,A__      ):





	'''simple docstring'''



	_UpperCAmelCase :	Dict					     =      1



	@register_to_config
	def __init__(						self			:					str    ,						lowercase			:					int = 1_000    ,						lowercase			:					float = 0.0001    ,						lowercase			:					float = 0.02    ,						lowercase			:					str = "linear"    ,						lowercase			:					float = 0.0    ,						lowercase			:					Optional[np.ndarray] = None    ,						lowercase			:					bool = True    ,						):


				'''simple docstring'''


				if trained_betas is not None:
							_snake_case						=    torch.from_numpy(_lowerCAmelCase			)
				elif beta_schedule == "linear":
							_snake_case						=    torch.linspace(_lowerCAmelCase    ,						_lowerCAmelCase    ,						_lowerCAmelCase    ,						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    ,						_lowerCAmelCase    ,						dtype=torch.floataa			) ** 2
							)
				elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							_snake_case						=    betas_for_alpha_bar(_lowerCAmelCase			)
				elif beta_schedule == "sigmoid":
							# GeoDiff sigmoid schedule
							_snake_case						=    torch.linspace(-6    ,						6    ,						_lowerCAmelCase			)
							_snake_case						=    torch.sigmoid(_lowerCAmelCase			) * (beta_end - beta_start) + beta_start
				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			)
				_snake_case						=    torch.tensor(1.0			)

				_snake_case						=    torch.tensor(1.0			)

				# standard deviation of the initial noise distribution
				_snake_case						=    1.0

				# setable values
				_snake_case						=    None
				_snake_case						=    torch.from_numpy(np.arange(0    ,						_lowerCAmelCase			)[::-1].copy()			)

				_snake_case						=    eta



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


				'''simple docstring'''


				return sample



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					int    ,						lowercase			:					int = 10    ,						lowercase			:					int = 10    ,						lowercase			:					Union[str, torch.device] = None    ,						):


				'''simple docstring'''


				_snake_case						=    min(self.config.num_train_timesteps    ,						_lowerCAmelCase			)
				_snake_case						=    num_inference_steps

				_snake_case						=    []

				_snake_case						=    {}
				for j in range(0    ,						num_inference_steps - jump_length    ,						_lowerCAmelCase			):
							_snake_case						=    jump_n_sample - 1

				_snake_case						=    num_inference_steps
				while t >= 1:
							_snake_case						=    t - 1
							timesteps.append(_lowerCAmelCase			)

							if jumps.get(_lowerCAmelCase    ,						0			) > 0:
										_snake_case						=    jumps[t] - 1
										for _ in range(_lowerCAmelCase			):
													_snake_case						=    t + 1
													timesteps.append(_lowerCAmelCase			)

				_snake_case						=    np.array(_lowerCAmelCase			) * (self.config.num_train_timesteps // self.num_inference_steps)
				_snake_case						=    torch.from_numpy(_lowerCAmelCase			).to(_lowerCAmelCase			)



	def 				A							(						self			:					Dict    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    t - self.config.num_train_timesteps // self.num_inference_steps

				_snake_case						=    self.alphas_cumprod[t]
				_snake_case						=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				_snake_case						=    1 - alpha_prod_t
				_snake_case						=    1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				_snake_case						=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance



	def 				A							(						self			:					List[Any]    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					int    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					Optional[torch.Generator] = None    ,						lowercase			:					bool = True    ,						):


				'''simple docstring'''


				_snake_case						=    timestep
				_snake_case						=    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				_snake_case						=    self.alphas_cumprod[t]
				_snake_case						=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				_snake_case						=    1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				_snake_case						=    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
							_snake_case						=    torch.clamp(_lowerCAmelCase    ,						-1    ,						1			)

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				_snake_case						=    model_output.device
				_snake_case						=    randn_tensor(model_output.shape    ,						generator=_lowerCAmelCase    ,						device=_lowerCAmelCase    ,						dtype=model_output.dtype			)
				_snake_case						=    self.eta * self._get_variance(_lowerCAmelCase			) ** 0.5

				_snake_case						=    0
				if t > 0 and self.eta > 0:
							_snake_case						=    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				_snake_case						=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				_snake_case						=    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				_snake_case						=    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				_snake_case						=    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
							return (
							    pred_prev_sample,
							    pred_original_sample,
							)

				return RePaintSchedulerOutput(prev_sample=_lowerCAmelCase    ,						pred_original_sample=_lowerCAmelCase			)



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


				'''simple docstring'''


				_snake_case						=    self.config.num_train_timesteps // self.num_inference_steps

				for i in range(_lowerCAmelCase			):
							_snake_case						=    self.betas[timestep + i]
							if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										_snake_case						=    randn_tensor(sample.shape    ,						dtype=sample.dtype    ,						generator=_lowerCAmelCase			)
										_snake_case						=    noise.to(sample.device			)
							else:
										_snake_case						=    randn_tensor(sample.shape    ,						generator=_lowerCAmelCase    ,						device=sample.device    ,						dtype=sample.dtype			)

							# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case						=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample



	def 				A							(						self			:					List[Any]    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					torch.IntTensor    ,						):


				'''simple docstring'''


				raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'			)





	def __len__(						self			:					List[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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)