





'''simple docstring'''

import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							inspect.getfile(accelerate.test_utils							)
 __lowercase  						=							os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ["""scripts""", """test_cli.py"""]							)

 __lowercase  						=							["""accelerate""", """launch"""]
 __lowercase  						=							Path.home() / """.cache/huggingface/accelerate"""
 __lowercase  						=							"""default_config.yaml"""
 __lowercase  						=							config_folder / config_file
 __lowercase  						=							config_folder / """_default_config.yaml"""

 __lowercase  						=							Path("""tests/test_configs"""							)
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[int]				)->				Optional[int]:
     if cls.config_path.is_file():
         cls.config_path.rename(cls.changed_path				)
 @classmethod
 def 	UpperCAmelCase_				(							cls :Any				)->				Dict:
     if cls.changed_path.is_file():
         cls.changed_path.rename(cls.config_path				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     A__							=     self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
         cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]		,			env=os.environ.copy()				)
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     for config in sorted(self.test_config_path.glob("**/*.yaml"				)				):
         with self.subTest(config_file=lowercase_				):
             execute_subprocess_async(
                 self.base_cmd + ["--config_file", str(lowercase_				), self.test_file_path]		,			env=os.environ.copy()				)



 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     execute_subprocess_async(["accelerate", "test"]		,			env=os.environ.copy()				)






class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							"""test-tpu"""
 __lowercase  						=							"""us-central1-a"""
 __lowercase  						=							"""ls"""
 __lowercase  						=							["""accelerate""", """tpu-config"""]
 __lowercase  						=							"""cd /usr/share"""
 __lowercase  						=							"""tests/test_samples/test_command_file.sh"""
 __lowercase  						=							"""Running gcloud compute tpus tpu-vm ssh"""
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[Any]:
     A__							=     run_command(
         self.cmd
         + ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Tuple:
     A__							=     run_command(
         self.cmd
         + [
             "--config_file",
             "tests/test_configs/0_12_0.yaml",
             "--command",
             self.command,
             "--tpu_zone",
             self.tpu_zone,
             "--tpu_name",
             self.tpu_name,
             "--debug",
         ]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     A__							=     run_command(
         self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"]		,			return_stdout=lowercase_				)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Union[str, Any]:
     A__							=     run_command(
         self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :Any				)->				List[str]:
     A__							=     run_command(
         self.cmd
         + [
             "--config_file",
             "tests/test_configs/latest.yaml",
             "--command",
             self.command,
             "--command",
             "echo \"Hello World\"",
             "--debug",
         ]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[Any]:
     A__							=     run_command(
         self.cmd
         + ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     A__							=     run_command(
         self.cmd
         + [
             "--config_file",
             "tests/test_configs/0_12_0.yaml",
             "--command_file",
             self.command_file,
             "--tpu_zone",
             self.tpu_zone,
             "--tpu_name",
             self.tpu_name,
             "--debug",
         ]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"		,			lowercase_		,			)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Dict:
     A__							=     run_command(
         self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"		,			lowercase_		,			)



 def 	UpperCAmelCase_				(							self :int				)->				Tuple:
     A__							=     run_command(
         self.cmd
         + [
             "--config_file",
             "tests/test_configs/latest.yaml",
             "--install_accelerate",
             "--accelerate_version",
             "12.0.0",
             "--debug",
         ]		,			return_stdout=lowercase_		,			)
     self.assertIn(
         F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"		,			lowercase_		,			)










'''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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

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

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

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

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

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

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

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

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

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

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

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

     return sample



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



