



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin





class 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ):
				__lowerCamelCase =				1




				@register_to_config
				def __init__(							self						,				_snake_case=2000						,				_snake_case=0.1						,				_snake_case=20						,				_snake_case=1e-3		):



									"""simple docstring"""





									_lowerCAmelCase						=    None
									_lowerCAmelCase						=    None
									_lowerCAmelCase						=    None




				def 							snake_case   (							self						,				_snake_case						,				_snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase						=    torch.linspace(1						,				self.config.sampling_eps						,				_snake_case						,				device=_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=None		):



									"""simple docstring"""





									if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""		)

									# TODO(Patrick) better comments + non-PyTorch
									# postprocess model score
									_lowerCAmelCase						=    (
									    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
									)
									_lowerCAmelCase						=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
									_lowerCAmelCase						=    std.flatten()
									while len(std.shape		) < len(score.shape		):
														_lowerCAmelCase						=    std.unsqueeze(-1		)
									_lowerCAmelCase						=    -score / std

									# compute
									_lowerCAmelCase						=    -1.0 / len(self.timesteps		)

									_lowerCAmelCase						=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
									_lowerCAmelCase						=    beta_t.flatten()
									while len(beta_t.shape		) < len(x.shape		):
														_lowerCAmelCase						=    beta_t.unsqueeze(-1		)
									_lowerCAmelCase						=    -0.5 * beta_t * x

									_lowerCAmelCase						=    torch.sqrt(_snake_case		)
									_lowerCAmelCase						=    drift - diffusion**2 * score
									_lowerCAmelCase						=    x + drift * dt

									# add noise
									_lowerCAmelCase						=    randn_tensor(x.shape						,				layout=x.layout						,				generator=_snake_case						,				device=x.device						,				dtype=x.dtype		)
									_lowerCAmelCase						=    x_mean + diffusion * math.sqrt(-dt		) * noise

									return x, x_mean




				def __len__(							self		):



									"""simple docstring"""





									return self.config.num_train_timesteps








"""simple docstring"""







import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowercase__		:				List[str]          =	logging.getLogger()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= {}
  lowerCAmelCase_   :				Union[str, Any]								= os.path.join(lowerCAmelCase__    ,   'all_results.json'						)
  if os.path.exists(lowerCAmelCase__						):
    with open(lowerCAmelCase__    ,   'r'						) as f:
      lowerCAmelCase_   :				Any								= json.load(lowerCAmelCase__						)
  else:
    raise ValueError(f"can't find {path}"						)
  return results


lowercase__		:				Optional[int]          =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        import xla_spawn

        lowerCAmelCase_   :				Union[str, Any]								= self.get_auto_remove_tmp_dir()
        lowerCAmelCase_   :				Optional[Any]								= F"\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        with patch.object(SCREAMING_SNAKE_CASE_    ,			'argv'    ,			SCREAMING_SNAKE_CASE_       ):
          lowerCAmelCase_   :				Optional[Any]								= time()
          xla_spawn.main()
          lowerCAmelCase_   :				Any								= time()

          lowerCAmelCase_   :				Dict								= get_results(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(result['eval_accuracy']    ,			0.75       )

          # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
          self.assertLess(end - start    ,			5_0_0       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        import xla_spawn

        lowerCAmelCase_   :				Dict								= '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
        with patch.object(SCREAMING_SNAKE_CASE_    ,			'argv'    ,			SCREAMING_SNAKE_CASE_       ):
          xla_spawn.main()





