
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	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       =['''vqvae''']




							def __init__(       self     ,A__     ,A__     ,A__     ,A__     ,):
												super().__init__()
												self.register_modules(unet=A__     ,scheduler=A__     ,mel=A__     ,vqvae=A__)




							def 				A__   (       self):
												return 5_0 if isinstance(self.scheduler     ,A__) else 1_0_0_0




							@torch.no_grad()
							def __call__(       self     ,A__ = 1     ,A__ = None     ,A__ = None     ,A__ = 0     ,A__ = 0     ,A__ = None     ,A__ = None     ,A__ = 0     ,A__ = 0     ,A__ = None     ,A__ = 0     ,A__ = None     ,A__ = None     ,A__=True     ,):
												lowercase					=		steps or self.get_default_steps()
												self.scheduler.set_timesteps(A__)
												lowercase					=		step_generator or generator
												# For backwards compatibility
												if type(self.unet.config.sample_size) == int:
																	lowercase					=		(self.unet.config.sample_size, self.unet.config.sample_size)
												if noise is None:
																	lowercase					=		randn_tensor(
																	    (
																	        batch_size,
																	        self.unet.config.in_channels,
																	        self.unet.config.sample_size[0],
																	        self.unet.config.sample_size[1],
																	    )     ,generator=A__     ,device=self.device     ,)
												lowercase					=		noise
												lowercase					=		None

												if audio_file is not None or raw_audio is not None:
																	self.mel.load_audio(A__     ,A__)
																	lowercase					=		self.mel.audio_slice_to_image(A__)
																	lowercase					=		np.frombuffer(input_image.tobytes()     ,dtype='''uint8''').reshape(
																	    (input_image.height, input_image.width))
																	lowercase					=		(input_image / 2_5_5) * 2 - 1
																	lowercase					=		torch.tensor(input_image[np.newaxis, :, :]     ,dtype=torch.float).to(self.device)

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

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

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

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

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

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

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

												lowercase					=		(images / 2 + 0.5).clamp(0     ,1)
												lowercase					=		images.cpu().permute(0     ,2     ,3     ,1).numpy()
												lowercase					=		(images * 2_5_5).round().astype('''uint8''')
												lowercase					=		list(
												    (Image.fromarray(_[:, :, 0]) for _ in images)
												    if images.shape[3] == 1
												    else (Image.fromarray(A__     ,mode='''RGB''').convert('''L''') for _ in images))

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

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




							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__ = 5_0):
												assert isinstance(self.scheduler     ,A__)
												self.scheduler.set_timesteps(A__)
												lowercase					=		np.array(
												    [np.frombuffer(image.tobytes()     ,dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
												lowercase					=		(sample / 2_5_5) * 2 - 1
												lowercase					=		torch.Tensor(A__).to(self.device)

												for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,(0,))):
																	lowercase					=		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																	lowercase					=		self.scheduler.alphas_cumprod[t]
																	lowercase					=		(
																	    self.scheduler.alphas_cumprod[prev_timestep]
																	    if prev_timestep >= 0
																	    else self.scheduler.final_alpha_cumprod
																	)
																	lowercase					=		1 - alpha_prod_t
																	lowercase					=		self.unet(A__     ,A__)['''sample''']
																	lowercase					=		(1 - alpha_prod_t_prev) ** 0.5 * model_output
																	lowercase					=		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																	lowercase					=		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

												return sample





							@staticmethod
							def 				A__   (       A__     ,A__     ,A__):
												lowercase					=		acos(torch.dot(torch.flatten(A__)     ,torch.flatten(A__)) / torch.norm(A__) / torch.norm(A__))
												return sin((1 - alpha) * theta) * xa / sin(A__) + sin(alpha * theta) * xa / sin(A__)






# Copyright 2022 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
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase__	:Any										=					"Run commands across TPU VMs for initial setup before running `accelerate launch`."



def  UpperCamelCase					(							lowerCAmelCase__=None	):
					'''simple docstring'''
					if subparsers is not None:
										lowercase					=		subparsers.add_parser('''tpu-config'''				,    description=_description	)
					else:
										lowercase					=		argparse.ArgumentParser('''Accelerate tpu-config command'''				,    description=_description	)
					# Core arguments
					lowercase					=		parser.add_argument_group(
					    '''Config Arguments'''				,    '''Arguments that can be configured through `accelerate config`.'''	)
					config_args.add_argument(
					    '''--config_file'''				,    type=lowerCAmelCase__				,    default=lowerCAmelCase__				,    help='''Path to the config file to use for accelerate.'''				,    )
					config_args.add_argument(
					    '''--tpu_name'''				,    default=lowerCAmelCase__				,    help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''				,    )
					config_args.add_argument(
					    '''--tpu_zone'''				,    default=lowerCAmelCase__				,    help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''				,    )
					lowercase					=		parser.add_argument_group('''TPU Arguments'''				,    '''Arguments for options ran inside the TPU.'''	)
					pod_args.add_argument(
					    '''--use_alpha'''				,    action='''store_true'''				,    help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''				,    )
					pod_args.add_argument(
					    '''--command_file'''				,    default=lowerCAmelCase__				,    help='''The path to the file containing the commands to run on the pod on startup.'''				,    )
					pod_args.add_argument(
					    '''--command'''				,    action='''append'''				,    nargs='''+'''				,    help='''A command to run on the pod. Can be passed multiple times.'''				,    )
					pod_args.add_argument(
					    '''--install_accelerate'''				,    action='''store_true'''				,    help='''Whether to install accelerate on the pod. Defaults to False.'''				,    )
					pod_args.add_argument(
					    '''--accelerate_version'''				,    default='''latest'''				,    help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''				,    )
					pod_args.add_argument(
					    '''--debug'''				,    action='''store_true'''				,    help='''If set, will print the command that would be run instead of running it.'''	)

					if subparsers is not None:
										parser.set_defaults(func=lowerCAmelCase__	)
					return parser



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase__	):
										lowercase					=		load_config_from_file(args.config_file	)
										if not args.command_file and defaults.command_file is not None and not args.command:
															lowercase					=		defaults.command_file
										if not args.command and defaults.commands is not None:
															lowercase					=		defaults.commands
										if not args.tpu_name:
															lowercase					=		defaults.tpu_name
										if not args.tpu_zone:
															lowercase					=		defaults.tpu_zone
					if args.accelerate_version == "dev":
										lowercase					=		'''git+https://github.com/huggingface/accelerate.git'''
					elif args.accelerate_version == "latest":
										lowercase					=		'''accelerate -U'''
					elif isinstance(parse(args.accelerate_version	)				,    lowerCAmelCase__	):
										lowercase					=		f'accelerate=={args.accelerate_version}'

					if not args.command_file and not args.command:
										raise ValueError('''You must specify either a command file or a command to run on the pod.'''	)

					if args.command_file:
										with open(args.command_file				,    '''r'''	) as f:
															lowercase					=		[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]				,    lowerCAmelCase__	):
										lowercase					=		[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					lowercase					=		['''cd /usr/share''']
					if args.install_accelerate:
										new_cmd += [f'pip install {args.accelerate_version}']
					new_cmd += args.command
					lowercase					=		'''; '''.join(lowerCAmelCase__	)

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					lowercase					=		['''gcloud''']
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f'Running {" ".join(lowerCAmelCase__	)}'	)
										return
					subprocess.run(lowerCAmelCase__	)
					print('''Successfully setup pod.'''	)



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		tpu_command_parser()
					lowercase					=		parser.parse_args()

					tpu_command_launcher(lowerCAmelCase__	)




