
"""simple docstring"""







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 				__magic_name__  (		UpperCAmelCase_ ):
					_SCREAMING_SNAKE_CASE							:       str  				= ["vqvae"]





					def __init__(					self :				int				,    snake_case_ :				AutoencoderKL				,    snake_case_ :				UNetaDConditionModel				,    snake_case_ :				Mel				,    snake_case_ :				Union[DDIMScheduler, DDPMScheduler]				,    ):
												super().__init__()
												self.register_modules(unet=_snake_case				,    scheduler=_snake_case				,    mel=_snake_case				,    vqvae=_snake_case     )





					def 				lowerCAmelCase   (					self :				Tuple     ):
												return 50 if isinstance(self.scheduler				,    _snake_case     ) else 1000





					@torch.no_grad()
					def __call__(					self :				List[str]				,    snake_case_ :				int = 1				,    snake_case_ :				str = None				,    snake_case_ :				np.ndarray = None				,    snake_case_ :				int = 0				,    snake_case_ :				int = 0				,    snake_case_ :				int = None				,    snake_case_ :				torch.Generator = None				,    snake_case_ :				float = 0				,    snake_case_ :				float = 0				,    snake_case_ :				torch.Generator = None				,    snake_case_ :				float = 0				,    snake_case_ :				torch.Tensor = None				,    snake_case_ :				torch.Tensor = None				,    snake_case_ :				int=True				,    ):
												__snake_case        =							steps or self.get_default_steps()
												self.scheduler.set_timesteps(_snake_case     )
												__snake_case        =							step_generator or generator
												# For backwards compatibility
												if type(self.unet.config.sample_size     ) == int:
																			__snake_case        =							(self.unet.config.sample_size, self.unet.config.sample_size)
												if noise is None:
																			__snake_case        =							randn_tensor(
																			    (
																			        batch_size,
																			        self.unet.config.in_channels,
																			        self.unet.config.sample_size[0],
																			        self.unet.config.sample_size[1],
																			    )				,    generator=_snake_case				,    device=self.device				,    )
												__snake_case        =							noise
												__snake_case        =							None

												if audio_file is not None or raw_audio is not None:
																			self.mel.load_audio(_snake_case				,    _snake_case     )
																			__snake_case        =							self.mel.audio_slice_to_image(_snake_case     )
																			__snake_case        =							np.frombuffer(input_image.tobytes()				,    dtype="uint8"     ).reshape(
																			    (input_image.height, input_image.width)     )
																			__snake_case        =							(input_image / 255) * 2 - 1
																			__snake_case        =							torch.tensor(input_image[np.newaxis, :, :]				,    dtype=torch.float     ).to(self.device     )

																			if self.vqvae is not None:
																										__snake_case        =							self.vqvae.encode(torch.unsqueeze(_snake_case				,    0     )     ).latent_dist.sample(
																										    generator=_snake_case     )[0]
																										__snake_case        =							self.vqvae.config.scaling_factor * input_images

																			if start_step > 0:
																										__snake_case        =							self.scheduler.add_noise(_snake_case				,    _snake_case				,    self.scheduler.timesteps[start_step - 1]     )

																			__snake_case        =							(
																			    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																			)
																			__snake_case        =							int(mask_start_secs * pixels_per_second     )
																			__snake_case        =							int(mask_end_secs * pixels_per_second     )
																			__snake_case        =							self.scheduler.add_noise(_snake_case				,    _snake_case				,    torch.tensor(self.scheduler.timesteps[start_step:]     )     )

												for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]     )     ):
																			if isinstance(self.unet				,    _snake_case     ):
																										__snake_case        =							self.unet(_snake_case				,    _snake_case				,    _snake_case     )["sample"]
																			else:
																										__snake_case        =							self.unet(_snake_case				,    _snake_case     )["sample"]

																			if isinstance(self.scheduler				,    _snake_case     ):
																										__snake_case        =							self.scheduler.step(
																										    model_output=_snake_case				,    timestep=_snake_case				,    sample=_snake_case				,    eta=_snake_case				,    generator=_snake_case				,    )["prev_sample"]
																			else:
																										__snake_case        =							self.scheduler.step(
																										    model_output=_snake_case				,    timestep=_snake_case				,    sample=_snake_case				,    generator=_snake_case				,    )["prev_sample"]

																			if mask is not None:
																										if mask_start > 0:
																																	__snake_case        =							mask[:, step, :, :mask_start]
																										if mask_end > 0:
																																	__snake_case        =							mask[:, step, :, -mask_end:]

												if self.vqvae is not None:
																			# 0.18215 was scaling factor used in training to ensure unit variance
																			__snake_case        =							1 / self.vqvae.config.scaling_factor * images
																			__snake_case        =							self.vqvae.decode(_snake_case     )["sample"]

												__snake_case        =							(images / 2 + 0.5).clamp(0				,    1     )
												__snake_case        =							images.cpu().permute(0				,    2				,    3				,    1     ).numpy()
												__snake_case        =							(images * 255).round().astype("uint8"     )
												__snake_case        =							list(
												    (Image.fromarray(_[:, :, 0]     ) for _ in images)
												    if images.shape[3] == 1
												    else (Image.fromarray(_snake_case				,    mode="RGB"     ).convert("L"     ) for _ in images)     )

												__snake_case        =							[self.mel.image_to_audio(_snake_case     ) for _ in images]
												if not return_dict:
																			return images, (self.mel.get_sample_rate(), audios)

												return BaseOutput(**AudioPipelineOutput(np.array(_snake_case     )[:, np.newaxis, :]     )				,    **ImagePipelineOutput(_snake_case     )     )





					@torch.no_grad()
					def 				lowerCAmelCase   (					self :				Optional[Any]				,    snake_case_ :				List[Image.Image]				,    snake_case_ :				int = 50     ):
												assert isinstance(self.scheduler				,    _snake_case     )
												self.scheduler.set_timesteps(_snake_case     )
												__snake_case        =							np.array(
												    [np.frombuffer(image.tobytes()				,    dtype="uint8"     ).reshape((1, image.height, image.width)     ) for image in images]     )
												__snake_case        =							(sample / 255) * 2 - 1
												__snake_case        =							torch.Tensor(_snake_case     ).to(self.device     )

												for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,    (0,)     )     ):
																			__snake_case        =							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																			__snake_case        =							self.scheduler.alphas_cumprod[t]
																			__snake_case        =							(
																			    self.scheduler.alphas_cumprod[prev_timestep]
																			    if prev_timestep >= 0
																			    else self.scheduler.final_alpha_cumprod
																			)
																			__snake_case        =							1 - alpha_prod_t
																			__snake_case        =							self.unet(_snake_case				,    _snake_case     )["sample"]
																			__snake_case        =							(1 - alpha_prod_t_prev) ** 0.5 * model_output
																			__snake_case        =							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																			__snake_case        =							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

												return sample





					@staticmethod
					def 				lowerCAmelCase   (					snake_case_ :				torch.Tensor				,    snake_case_ :				torch.Tensor				,    snake_case_ :				float     ):
												__snake_case        =							acos(torch.dot(torch.flatten(_snake_case     )				,    torch.flatten(_snake_case     )     ) / torch.norm(_snake_case     ) / torch.norm(_snake_case     )     )
												return sin((1 - alpha) * theta     ) * xa / sin(_snake_case     ) + sin(alpha * theta     ) * xa / sin(_snake_case     )





# 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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

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




