



'''simple docstring'''




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class       _lowerCAmelCase				(     SCREAMING_SNAKE_CASE_					):
	__SCREAMING_SNAKE_CASE  :						Any						     =   (DDPMScheduler,)


	def        _a      (self			, **lowercase				):
				A_      :		List[str]      =	{
				    'num_train_timesteps': 1000,
				    'beta_start': 0.00_01,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**__a				)
				return config


	def        _a      (self				):
				for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=__a				)


	def        _a      (self				):
				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			, [0.0_02, 0.02, 0.2, 2]				):
							self.check_over_configs(beta_start=__a			, beta_end=__a				)


	def        _a      (self				):
				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__a				)


	def        _a      (self				):
				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=__a				)


	def        _a      (self				):
				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__a				)


	def        _a      (self				):
				self.check_over_configs(thresholding=__a				)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=__a			, prediction_type=__a			, sample_max_value=__a			, )


	def        _a      (self				):
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(prediction_type=__a				)


	def        _a      (self				):
				for t in [0, 500, 999]:
							self.check_over_forward(time_step=__a				)


	def        _a      (self				):
				A_      :		Optional[int]      =	self.scheduler_classes[0]
				A_      :		str      =	self.get_scheduler_config()
				A_      :		Optional[Any]      =	scheduler_class(**__a				)

				assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5


	def        _a      (self				):
				A_      :		Dict      =	self.scheduler_classes[0]
				A_      :		str      =	self.get_scheduler_config()
				A_      :		int      =	scheduler_class(**__a				)

				A_      :		Tuple      =	len(__a				)

				A_      :		Dict      =	self.dummy_model()
				A_      :		Optional[Any]      =	self.dummy_sample_deter
				A_      :		List[Any]      =	torch.manual_seed(0				)

				for t in reversed(range(__a				)				):
							# 1. predict noise residual
							A_      :		Optional[int]      =	model(__a			, __a				)

							# 2. predict previous mean of sample x_t-1
							A_      :		Optional[int]      =	scheduler.step(__a			, __a			, __a			, generator=__a				).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							A_      :		Optional[int]      =	pred_prev_sample

				A_      :		Dict      =	torch.sum(torch.abs(__a				)				)
				A_      :		Tuple      =	torch.mean(torch.abs(__a				)				)

				assert abs(result_sum.item() - 258.9606				) < 1E-2
				assert abs(result_mean.item() - 0.33_72				) < 1E-3


	def        _a      (self				):
				A_      :		Union[str, Any]      =	self.scheduler_classes[0]
				A_      :		List[str]      =	self.get_scheduler_config(prediction_type="""v_prediction"""				)
				A_      :		List[str]      =	scheduler_class(**__a				)

				A_      :		str      =	len(__a				)

				A_      :		Optional[int]      =	self.dummy_model()
				A_      :		Optional[int]      =	self.dummy_sample_deter
				A_      :		Dict      =	torch.manual_seed(0				)

				for t in reversed(range(__a				)				):
							# 1. predict noise residual
							A_      :		List[Any]      =	model(__a			, __a				)

							# 2. predict previous mean of sample x_t-1
							A_      :		Optional[Any]      =	scheduler.step(__a			, __a			, __a			, generator=__a				).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							A_      :		int      =	pred_prev_sample

				A_      :		Dict      =	torch.sum(torch.abs(__a				)				)
				A_      :		int      =	torch.mean(torch.abs(__a				)				)

				assert abs(result_sum.item() - 202.0296				) < 1E-2
				assert abs(result_mean.item() - 0.26_31				) < 1E-3


	def        _a      (self				):
				A_      :		List[str]      =	self.scheduler_classes[0]
				A_      :		List[str]      =	self.get_scheduler_config()
				A_      :		int      =	scheduler_class(**__a				)

				A_      :		Optional[Any]      =	[100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=__a				)

				A_      :		Any      =	scheduler.timesteps

				for i, timestep in enumerate(__a				):
							if i == len(__a				) - 1:
										A_      :		Dict      =	-1
							else:
										A_      :		Tuple      =	timesteps[i + 1]

							A_      :		Optional[Any]      =	scheduler.previous_timestep(__a				)
							A_      :		int      =	prev_t.item()

							self.assertEqual(__a			, __a				)


	def        _a      (self				):
				A_      :		List[Any]      =	self.scheduler_classes[0]
				A_      :		List[Any]      =	self.get_scheduler_config()
				A_      :		str      =	scheduler_class(**__a				)

				A_      :		Any      =	[100, 87, 50, 51, 0]

				with self.assertRaises(__a			, msg="""`custom_timesteps` must be in descending order."""				):
							scheduler.set_timesteps(timesteps=__a				)


	def        _a      (self				):
				A_      :		List[Any]      =	self.scheduler_classes[0]
				A_      :		str      =	self.get_scheduler_config()
				A_      :		Optional[Any]      =	scheduler_class(**__a				)

				A_      :		Tuple      =	[100, 87, 50, 1, 0]
				A_      :		List[str]      =	len(__a				)

				with self.assertRaises(__a			, msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""				):
							scheduler.set_timesteps(num_inference_steps=__a			, timesteps=__a				)


	def        _a      (self				):
				A_      :		List[str]      =	self.scheduler_classes[0]
				A_      :		List[Any]      =	self.get_scheduler_config()
				A_      :		Union[str, Any]      =	scheduler_class(**__a				)

				A_      :		List[str]      =	[scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    __a			, msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			, ):
							scheduler.set_timesteps(timesteps=__a				)




'''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)

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





def   a_   (		_UpperCAmelCase    :      int				)    ->  Optional[int]:
      __snake_case :   List[str]													=    {}
      __snake_case :   List[str]													=    os.path.join(_UpperCAmelCase ,'all_results.json'				)
      if os.path.exists(_UpperCAmelCase				):
            with open(_UpperCAmelCase ,'r'				) as f:
                  __snake_case :   Dict													=    json.load(_UpperCAmelCase				)
      else:
            raise ValueError(f'''can\'t find {path}'''				)
      return results


A__    :   Dict           =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   int													=    self.get_auto_remove_tmp_dir()
       __snake_case :   Tuple													=    f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(__a      ,     'argv'      ,     __a  ):
             __snake_case :   Tuple													=    time()
             xla_spawn.main()
             __snake_case :   Optional[int]													=    time()

             __snake_case :   List[Any]													=    get_results(__a  )
             self.assertGreaterEqual(result['eval_accuracy']      ,     0.7_5  )

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





 def A_   (   self						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   Optional[Any]													=    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
       with patch.object(__a      ,     'argv'      ,     __a  ):
             xla_spawn.main()



