
"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Tuple          =   (DDPMScheduler,)
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "variance_type": "fixed_small",
										    "clip_sample": True,
										}

										config.update(**_lowerCamelCase       )
										return config
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										for timesteps in [1, 5, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,    [0.002, 0.02, 0.2, 2]       ):
													self.check_over_configs(beta_start=_lowerCamelCase  ,    beta_end=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										self.check_over_configs(thresholding=_lowerCamelCase       )
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=_lowerCamelCase  ,    prediction_type=_lowerCamelCase  ,    sample_max_value=_lowerCamelCase  ,    )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for t in [0, 500, 999]:
													self.check_over_forward(time_step=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_0979       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1e-5
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       len(_lowerCamelCase       )

										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter
										__lowercase       =       torch.manual_seed(0       )

										for t in reversed(range(_lowerCamelCase       )       ):
													# 1. predict noise residual
													__lowercase       =       model(_lowerCamelCase  ,    _lowerCamelCase       )

													# 2. predict previous mean of sample x_t-1
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    generator=_lowerCamelCase       ).prev_sample

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

										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 258.9_606       ) < 1e-2
										assert abs(result_mean.item() - 0.3372       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config(prediction_type="v_prediction"       )
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       len(_lowerCamelCase       )

										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter
										__lowercase       =       torch.manual_seed(0       )

										for t in reversed(range(_lowerCamelCase       )       ):
													# 1. predict noise residual
													__lowercase       =       model(_lowerCamelCase  ,    _lowerCamelCase       )

													# 2. predict previous mean of sample x_t-1
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    generator=_lowerCamelCase       ).prev_sample

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

										__lowercase       =       torch.sum(torch.abs(_lowerCamelCase       )       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_sum.item() - 202.0_296       ) < 1e-2
										assert abs(result_mean.item() - 0.2631       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       [100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=_lowerCamelCase       )

										__lowercase       =       scheduler.timesteps

										for i, timestep in enumerate(_lowerCamelCase       ):
													if i == len(_lowerCamelCase       ) - 1:
																__lowercase       =       -1
													else:
																__lowercase       =       timesteps[i + 1]

													__lowercase       =       scheduler.previous_timestep(_lowerCamelCase       )
													__lowercase       =       prev_t.item()

													self.assertEqual(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       [100, 87, 50, 51, 0]

										with self.assertRaises(_lowerCamelCase  ,    msg="`custom_timesteps` must be in descending order."       ):
													scheduler.set_timesteps(timesteps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       [100, 87, 50, 1, 0]
										__lowercase       =       len(_lowerCamelCase       )

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





							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config()
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       [scheduler.config.num_train_timesteps]

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






'''simple docstring'''

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
 from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
 from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
 from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A_     :						Optional[Any]    							=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A_     :						Tuple    							='''main'''
# Default branch name
A_     :						Dict    							='''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
A_     :						int    							='''aaaaaaa'''
# This commit does not exist, so we should 404.

A_     :						List[str]    							='''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
A_     :						List[str]    							='''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''



@contextlib.contextmanager
def snake_case_      (					)			->					Tuple:
   print('''Welcome!''')
   yield
   print('''Bye!''')



@contextlib.contextmanager
def snake_case_      (					)			->					str:
   print('''Bonjour!''')
   yield
   print('''Au revoir!''')


class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self ):
     # If the spec is missing, importlib would not be able to import the module dynamically.
     assert transformers.__spec__ is not None
     assert importlib.util.find_spec('''transformers''' ) is not None






class     __UpperCAmelCase       ( unittest.TestCase		):





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([] ):
        print('''Transformers are awesome!''' )
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()    ,     '''Transformers are awesome!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Welcome!\nTransformers are awesome!\nBye!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_fr(), context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )





  @require_torch
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )





  @require_tf
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )







  @require_flax
  def 						UpperCAmelCase_	(	self ):
     # Flax models don't have labels
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )





