


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

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

     __UpperCamelCase  =					Path('''tests/test_configs'''			)






     @classmethod
     def      lowerCamelCase__     (	cls  :					Dict				):
           if cls.config_path.is_file():
                 cls.config_path.rename(cls.changed_path				)






     @classmethod
     def      lowerCamelCase__     (	cls  :					List[Any]				):
           if cls.changed_path.is_file():
                 cls.changed_path.rename(cls.config_path				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	List[Any]       =   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      lowerCamelCase__     (	self  :					int				):
           for config in sorted(self.test_config_path.glob('''**/*.yaml'''				)				):
                 with self.subTest(config_file=UpperCamelCase_				):
                       execute_subprocess_async(
                           self.base_cmd + ['''--config_file''', str(UpperCamelCase_				), self.test_file_path]					,				env=os.environ.copy()				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           execute_subprocess_async(['''accelerate''', '''test''']					,				env=os.environ.copy()				)



class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					'''test-tpu'''
     __UpperCamelCase  =					'''us-central1-a'''
     __UpperCamelCase  =					'''ls'''
     __UpperCamelCase  =					['''accelerate''', '''tpu-config''']
     __UpperCamelCase  =					'''cd /usr/share'''
     __UpperCamelCase  =					'''tests/test_samples/test_command_file.sh'''
     __UpperCamelCase  =					'''Running gcloud compute tpus tpu-vm ssh'''






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   run_command(
               self.cmd
               + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']					,				return_stdout=UpperCamelCase_					,				)
           self.assertIn(
               F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Any       =   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=UpperCamelCase_					,				)
           self.assertIn(
               F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Dict       =   run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']					,				return_stdout=UpperCamelCase_				)
           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'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	int       =   run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']					,				return_stdout=UpperCamelCase_					,				)
           self.assertIn(
               F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Any       =   run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/latest.yaml''',
                   '''--command''',
                   self.command,
                   '''--command''',
                   '''echo "Hello World"''',
                   '''--debug''',
               ]					,				return_stdout=UpperCamelCase_					,				)
           self.assertIn(
               F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	int       =   run_command(
               self.cmd
               + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']					,				return_stdout=UpperCamelCase_					,				)
           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'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	List[Any]       =   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=UpperCamelCase_					,				)
           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'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[str]       =   run_command(
               self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']					,				return_stdout=UpperCamelCase_					,				)
           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'''					,				UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   run_command(
               self.cmd
               + [
                   '''--config_file''',
                   '''tests/test_configs/latest.yaml''',
                   '''--install_accelerate''',
                   '''--accelerate_version''',
                   '''12.0.0''',
                   '''--debug''',
               ]					,				return_stdout=UpperCamelCase_					,				)
           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'''					,				UpperCamelCase_					,				)






"""simple docstring"""





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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					None



def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			List[str]=0.999 ,   _snake_case				:			Dict="cosine" ,   ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case				:			List[Any]			):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case				:			Optional[int]			):
                  return math.exp(t * -12.0			)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

      lowerCAmelCase					:	List[Any]       =   []
      for i in range(_snake_case			):
            lowerCAmelCase					:	int       =   i / num_diffusion_timesteps
            lowerCAmelCase					:	Tuple       =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,   _snake_case			)			)
      return torch.tensor(_snake_case ,   dtype=torch.floataa			)






class 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					Any					,				UpperCamelCase_  :					int = 1_0_0_0					,				UpperCamelCase_  :					str = "fixed_small_log"					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[float] = 1.0					,				UpperCamelCase_  :					str = "epsilon"					,				UpperCamelCase_  :					str = "squaredcos_cap_v2"					,				):
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

           lowerCAmelCase					:	Any       =   betas_for_alpha_bar(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   1.0 - self.betas
           lowerCAmelCase					:	Union[str, Any]       =   torch.cumprod(self.alphas					,				dim=0				)
           lowerCAmelCase					:	Tuple       =   torch.tensor(1.0				)

           # standard deviation of the initial noise distribution
           lowerCAmelCase					:	Any       =   1.0

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

           lowerCAmelCase					:	List[str]       =   variance_type






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None				):
           return sample






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, torch.device] = None				):
           lowerCAmelCase					:	Any       =   num_inference_steps
           lowerCAmelCase					:	str       =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCAmelCase					:	Tuple       =   (np.arange(0					,				UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
           lowerCAmelCase					:	Any       =   torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Any=None				):
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	int       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	Dict       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	Tuple       =   self.betas[t]
           else:
                 lowerCAmelCase					:	Union[str, Any]       =   1 - alpha_prod_t / 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
           lowerCAmelCase					:	Optional[Any]       =   beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 lowerCAmelCase					:	List[str]       =   self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 lowerCAmelCase					:	Any       =   torch.log(torch.clamp(UpperCamelCase_					,				min=1E-20				)				)
                 lowerCAmelCase					:	Union[str, Any]       =   torch.exp(0.5 * variance				)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 lowerCAmelCase					:	Optional[Any]       =   variance.log()
                 lowerCAmelCase					:	Union[str, Any]       =   beta.log()

                 lowerCAmelCase					:	Dict       =   (predicted_variance + 1) / 2
                 lowerCAmelCase					:	Union[str, Any]       =   frac * max_log + (1 - frac) * min_log

           return variance






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					bool = True					,				):
           lowerCAmelCase					:	Optional[Any]       =   timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   torch.split(UpperCamelCase_					,				sample.shape[1]					,				dim=1				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   None

           # 1. compute alphas, betas
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Optional[int]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	int       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	List[Any]       =   self.betas[t]
                 lowerCAmelCase					:	Optional[int]       =   self.alphas[t]
           else:
                 lowerCAmelCase					:	List[Any]       =   1 - alpha_prod_t / alpha_prod_t_prev
                 lowerCAmelCase					:	Dict       =   1 - beta

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCAmelCase					:	List[Any]       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                 lowerCAmelCase					:	Tuple       =   model_output
           else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     ''' for the UnCLIPScheduler.'''				)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 lowerCAmelCase					:	Dict       =   torch.clamp(
                     UpperCamelCase_					,				-self.config.clip_sample_range					,				self.config.clip_sample_range				)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           lowerCAmelCase					:	int       =   (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           lowerCAmelCase					:	List[Any]       =   alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           lowerCAmelCase					:	str       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           lowerCAmelCase					:	int       =   0
           if t > 0:
                 lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
                     model_output.shape					,				dtype=model_output.dtype					,				generator=UpperCamelCase_					,				device=model_output.device				)

                 lowerCAmelCase					:	Any       =   self._get_variance(
                     UpperCamelCase_					,				predicted_variance=UpperCamelCase_					,				prev_timestep=UpperCamelCase_					,				)

                 if self.variance_type == "fixed_small_log":
                       lowerCAmelCase					:	str       =   variance
                 elif self.variance_type == "learned_range":
                       lowerCAmelCase					:	Optional[Any]       =   (0.5 * variance).exp()
                 else:
                       raise ValueError(
                           F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           ''' for the UnCLIPScheduler.'''				)

                 lowerCAmelCase					:	List[Any]       =   variance * variance_noise

           lowerCAmelCase					:	int       =   pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_					,				pred_original_sample=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.IntTensor					,				):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCAmelCase					:	Tuple       =   self.alphas_cumprod.to(device=original_samples.device					,				dtype=original_samples.dtype				)
           lowerCAmelCase					:	int       =   timesteps.to(original_samples.device				)

           lowerCAmelCase					:	Dict       =   alphas_cumprod[timesteps] ** 0.5
           lowerCAmelCase					:	str       =   sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	Any       =   sqrt_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	List[str]       =   (1 - alphas_cumprod[timesteps]) ** 0.5
           lowerCAmelCase					:	Tuple       =   sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	int       =   sqrt_one_minus_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	Dict       =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples


