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       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""vqvae"""]


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       AutoencoderKL					,	UpperCamelCase__			:       UNetaDConditionModel					,	UpperCamelCase__			:       Mel					,	UpperCamelCase__			:       Union[DDIMScheduler, DDPMScheduler]					,	):





												'''simple docstring'''





												super().__init__()
												self.register_modules(unet=UpperCamelCase__					,	scheduler=UpperCamelCase__					,	mel=UpperCamelCase__					,	vqvae=UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												return 50 if isinstance(self.scheduler					,	UpperCamelCase__		) else 1000


						@torch.no_grad()
						def __call__( self			:       str					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       str = None					,	UpperCamelCase__			:       np.ndarray = None					,	UpperCamelCase__			:       int = 0					,	UpperCamelCase__			:       int = 0					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       torch.Generator = None					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       torch.Generator = None					,	UpperCamelCase__			:       float = 0					,	UpperCamelCase__			:       torch.Tensor = None					,	UpperCamelCase__			:       torch.Tensor = None					,	UpperCamelCase__			:       Union[str, Any]=True					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							steps or self.get_default_steps()
												self.scheduler.set_timesteps(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							step_generator or generator
												# For backwards compatibility
												if type(self.unet.config.sample_size		) == int:
																		SCREAMING_SNAKE_CASE							:					int           =							(self.unet.config.sample_size, self.unet.config.sample_size)
												if noise is None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							randn_tensor(
																		    (
																		        batch_size,
																		        self.unet.config.in_channels,
																		        self.unet.config.sample_size[0],
																		        self.unet.config.sample_size[1],
																		    )					,	generator=UpperCamelCase__					,	device=self.device					,	)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							noise
												SCREAMING_SNAKE_CASE							:					List[str]           =							None

												if audio_file is not None or raw_audio is not None:
																		self.mel.load_audio(UpperCamelCase__					,	UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.mel.audio_slice_to_image(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							np.frombuffer(input_image.tobytes()					,	dtype='''uint8'''		).reshape(
																		    (input_image.height, input_image.width)		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							(input_image / 255) * 2 - 1
																		SCREAMING_SNAKE_CASE							:					Dict           =							torch.tensor(input_image[np.newaxis, :, :]					,	dtype=torch.float		).to(self.device		)

																		if self.vqvae is not None:
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.vqvae.encode(torch.unsqueeze(UpperCamelCase__					,	0		)		).latent_dist.sample(
																								    generator=UpperCamelCase__		)[0]
																								SCREAMING_SNAKE_CASE							:					Any           =							self.vqvae.config.scaling_factor * input_images

																		if start_step > 0:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.scheduler.add_noise(UpperCamelCase__					,	UpperCamelCase__					,	self.scheduler.timesteps[start_step - 1]		)

																		SCREAMING_SNAKE_CASE							:					Tuple           =							(
																		    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							int(mask_start_secs * pixels_per_second		)
																		SCREAMING_SNAKE_CASE							:					Any           =							int(mask_end_secs * pixels_per_second		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.scheduler.add_noise(UpperCamelCase__					,	UpperCamelCase__					,	torch.tensor(self.scheduler.timesteps[start_step:]		)		)

												for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
																		if isinstance(self.unet					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.unet(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)['''sample''']
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							self.unet(UpperCamelCase__					,	UpperCamelCase__		)['''sample''']

																		if isinstance(self.scheduler					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					str           =							self.scheduler.step(
																								    model_output=UpperCamelCase__					,	timestep=UpperCamelCase__					,	sample=UpperCamelCase__					,	eta=UpperCamelCase__					,	generator=UpperCamelCase__					,	)['''prev_sample''']
																		else:
																								SCREAMING_SNAKE_CASE							:					str           =							self.scheduler.step(
																								    model_output=UpperCamelCase__					,	timestep=UpperCamelCase__					,	sample=UpperCamelCase__					,	generator=UpperCamelCase__					,	)['''prev_sample''']

																		if mask is not None:
																								if mask_start > 0:
																														SCREAMING_SNAKE_CASE							:					Dict           =							mask[:, step, :, :mask_start]
																								if mask_end > 0:
																														SCREAMING_SNAKE_CASE							:					Optional[int]           =							mask[:, step, :, -mask_end:]

												if self.vqvae is not None:
																		# 0.18215 was scaling factor used in training to ensure unit variance
																		SCREAMING_SNAKE_CASE							:					List[str]           =							1 / self.vqvae.config.scaling_factor * images
																		SCREAMING_SNAKE_CASE							:					Any           =							self.vqvae.decode(UpperCamelCase__		)['''sample''']

												SCREAMING_SNAKE_CASE							:					str           =							(images / 2 + 0.5).clamp(0					,	1		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							images.cpu().permute(0					,	2					,	3					,	1		).numpy()
												SCREAMING_SNAKE_CASE							:					List[str]           =							(images * 255).round().astype('''uint8'''		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							list(
												    (Image.fromarray(_[:, :, 0]		) for _ in images)
												    if images.shape[3] == 1
												    else (Image.fromarray(UpperCamelCase__					,	mode='''RGB'''		).convert('''L'''		) for _ in images)		)

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

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


						@torch.no_grad()
						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Image.Image]					,	UpperCamelCase__			:       int = 50		):





												'''simple docstring'''





												assert isinstance(self.scheduler					,	UpperCamelCase__		)
												self.scheduler.set_timesteps(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.array(
												    [np.frombuffer(image.tobytes()					,	dtype='''uint8'''		).reshape((1, image.height, image.width)		) for image in images]		)
												SCREAMING_SNAKE_CASE							:					Any           =							(sample / 255) * 2 - 1
												SCREAMING_SNAKE_CASE							:					str           =							torch.Tensor(UpperCamelCase__		).to(self.device		)

												for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,	(0,)		)		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																		SCREAMING_SNAKE_CASE							:					Tuple           =							self.scheduler.alphas_cumprod[t]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							(
																		    self.scheduler.alphas_cumprod[prev_timestep]
																		    if prev_timestep >= 0
																		    else self.scheduler.final_alpha_cumprod
																		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							1 - alpha_prod_t
																		SCREAMING_SNAKE_CASE							:					Any           =							self.unet(UpperCamelCase__					,	UpperCamelCase__		)['''sample''']
																		SCREAMING_SNAKE_CASE							:					Any           =							(1 - alpha_prod_t_prev) ** 0.5 * model_output
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																		SCREAMING_SNAKE_CASE							:					Dict           =							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

												return sample



						@staticmethod
						def  __A ( UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       torch.Tensor					,	UpperCamelCase__			:       float		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							acos(torch.dot(torch.flatten(UpperCamelCase__		)					,	torch.flatten(UpperCamelCase__		)		) / torch.norm(UpperCamelCase__		) / torch.norm(UpperCamelCase__		)		)
												return sin((1 - alpha) * theta		) * xa / sin(UpperCamelCase__		) + sin(alpha * theta		) * xa / sin(UpperCamelCase__		)






# Copyright 2021 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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





