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 		_lowerCamelCase		(   snake_case_    ):
    __a  =      42
    __a  =      None







def 			A__      (	snake_case_ :		str       ,				snake_case_ :		Optional[Any]=0.9_99       ,				snake_case_ :		Tuple="cosine"       ,				):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case_ :		Any      ):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(snake_case_ :		List[Any]      ):
   return math.exp(t * -12.0      )

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

 SCREAMING_SNAKE_CASE__:   Optional[int]=      []
 for i in range(snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Dict=      i / num_diffusion_timesteps
  SCREAMING_SNAKE_CASE__:   int=      (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 		_lowerCamelCase		(   snake_case_    ,     snake_case_    ):
    @register_to_config
    def __init__(  self , lowerCAmelCase = 1000 , lowerCAmelCase = "fixed_small_log" , lowerCAmelCase = True , lowerCAmelCase = 1.0 , lowerCAmelCase = "epsilon" , lowerCAmelCase = "squaredcos_cap_v2" , )       ->					Dict:
     if beta_schedule != "squaredcos_cap_v2":
      raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''   )

     SCREAMING_SNAKE_CASE__:   int=      betas_for_alpha_bar(lowerCAmelCase   )

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

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

     # setable values
     SCREAMING_SNAKE_CASE__:   Dict=      None
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.from_numpy(np.arange(0 , lowerCAmelCase   )[::-1].copy()   )

     SCREAMING_SNAKE_CASE__:   List[str]=      variance_type
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					Union[str, Any]:
     return sample
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      num_inference_steps
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
     SCREAMING_SNAKE_CASE__:   List[str]=      (np.arange(0 , lowerCAmelCase   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
     SCREAMING_SNAKE_CASE__:   Tuple=      torch.from_numpy(lowerCAmelCase   ).to(lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None   )       ->					int:
     if prev_timestep is None:
      SCREAMING_SNAKE_CASE__:   Tuple=      t - 1

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

     if prev_timestep == t - 1:
      SCREAMING_SNAKE_CASE__:   str=      self.betas[t]
     else:
      SCREAMING_SNAKE_CASE__:   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
     SCREAMING_SNAKE_CASE__:   Tuple=      beta_prod_t_prev / beta_prod_t * beta

     if variance_type is None:
      SCREAMING_SNAKE_CASE__:   str=      self.config.variance_type

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

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

     return variance
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase=None , lowerCAmelCase = True , )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   str=      timestep

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

     # 1. compute alphas, betas
     if prev_timestep is None:
      SCREAMING_SNAKE_CASE__:   List[Any]=      t - 1

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

     if prev_timestep == t - 1:
      SCREAMING_SNAKE_CASE__:   List[str]=      self.betas[t]
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.alphas[t]
     else:
      SCREAMING_SNAKE_CASE__:   Dict=      1 - alpha_prod_t / alpha_prod_t_prev
      SCREAMING_SNAKE_CASE__:   Optional[int]=      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":
      SCREAMING_SNAKE_CASE__:   Optional[int]=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
      SCREAMING_SNAKE_CASE__:   Any=      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:
      SCREAMING_SNAKE_CASE__:   int=      torch.clamp(
          lowerCAmelCase , -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
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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
     SCREAMING_SNAKE_CASE__:   List[Any]=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     SCREAMING_SNAKE_CASE__:   int=      0
     if t > 0:
      SCREAMING_SNAKE_CASE__:   int=      randn_tensor(
          model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase , device=model_output.device   )

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

      if self.variance_type == "fixed_small_log":
       SCREAMING_SNAKE_CASE__:   List[str]=      variance
      elif self.variance_type == "learned_range":
       SCREAMING_SNAKE_CASE__:   int=      (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.'''   )

      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      variance * variance_noise

     SCREAMING_SNAKE_CASE__:   Tuple=      pred_prev_sample + variance

     if not return_dict:
      return (pred_prev_sample,)

     return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase , pred_original_sample=lowerCAmelCase   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					int:
     # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      timesteps.to(original_samples.device   )

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

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (1 - alphas_cumprod[timesteps]) ** 0.5
     SCREAMING_SNAKE_CASE__:   Tuple=      sqrt_one_minus_alpha_prod.flatten()
     while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
      SCREAMING_SNAKE_CASE__:   Any=      sqrt_one_minus_alpha_prod.unsqueeze(-1   )

     SCREAMING_SNAKE_CASE__:   Tuple=      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase_     :   Optional[Any] 				=				[
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     SCREAMING_SNAKE_CASE__:   Dict=      os.path.abspath(os.path.join('''examples''' , '''by_feature'''   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath('''examples'''   )
     for item in os.listdir(lowerCAmelCase   ):
      if item not in EXCLUDE_EXAMPLES:
       SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(lowerCAmelCase , lowerCAmelCase   )
       if os.path.isfile(lowerCAmelCase   ) and ".py" in item_path:
        with self.subTest(
            tested_script=lowerCAmelCase , feature_script=lowerCAmelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
         SCREAMING_SNAKE_CASE__:   List[Any]=      compare_against_test(
             os.path.join(lowerCAmelCase , lowerCAmelCase   ) , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
         SCREAMING_SNAKE_CASE__:   str=      '''\n'''.join(lowerCAmelCase   )
         if special_strings is not None:
          for string in special_strings:
           SCREAMING_SNAKE_CASE__:   Union[str, Any]=      diff.replace(lowerCAmelCase , ''''''   )
         self.assertEqual(lowerCAmelCase , ''''''   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )



    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath(os.path.join('''examples''' , '''cv_example.py'''   )   )
     SCREAMING_SNAKE_CASE__:   Dict=      [
         ''' ''' * 16 + '''{\n\n''',
         ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
         ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
         ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
         ''' ''' * 20 + '''"epoch": epoch,\n\n''',
         ''' ''' * 16 + '''},\n\n''',
         ''' ''' * 16 + '''step=epoch,\n''',
         ''' ''' * 12,
         ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
     ]
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )






@mock.patch.dict(os.environ    ,     {"TESTING_MOCKED_DATALOADERS": "1"}    )
class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      False
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().setUpClass()
     SCREAMING_SNAKE_CASE__:   List[Any]=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(cls._tmpdir , '''default_config.yml'''   )

     write_basic_config(save_location=cls.configPath   )
     SCREAMING_SNAKE_CASE__:   Dict=      ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().tearDownClass()
     shutil.rmtree(cls._tmpdir   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
     run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   List[Any]=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
     SCREAMING_SNAKE_CASE__:   List[Any]=      run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Tuple=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
     self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     if torch.cuda.is_available():
      SCREAMING_SNAKE_CASE__:   List[str]=      torch.cuda.device_count()
     else:
      SCREAMING_SNAKE_CASE__:   List[Any]=      1
     if num_processes > 1:
      self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
     else:
      self.assertIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
     with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
      SCREAMING_SNAKE_CASE__:   Any=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Dict=      re.findall('''({.+})''' , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      [r for r in results if '''accuracy''' in r][-1]
      SCREAMING_SNAKE_CASE__:   List[str]=      ast.literal_eval(lowerCAmelCase   )
      self.assertGreaterEqual(results['''accuracy'''] , 0.75   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      ['''examples/by_feature/multi_process_metrics.py''']
     run_command(self._launch_args + testargs   )
    @require_trackers
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''}   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     with tempfile.TemporaryDirectory() as tmpdir:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
      run_command(self._launch_args + testargs   )
      self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase , '''tracking'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   int=      ['''examples/by_feature/gradient_accumulation.py''']
     run_command(self._launch_args + testargs   )



    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      ['''examples/by_feature/local_sgd.py''']
     run_command(self._launch_args + testargs   )

