






'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      (DDPMScheduler,)






      def 					A__   (			self      :		Tuple  ,   **UpperCAmelCase      :		str		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Dict						={
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0_0_0_1,
           '''beta_end''': 0.0_2,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**UpperCAmelCase		)
       return config






      def 					A__   (			self      :		Optional[int]		)	->	List[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,   [0.0_0_2, 0.0_2, 0.2, 2]		):
        self.check_over_configs(beta_start=UpperCAmelCase  ,   beta_end=UpperCAmelCase		)






      def 					A__   (			self      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Union[str, Any]		)	->	Tuple:


       '''simple docstring'''

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






      def 					A__   (			self      :		str		)	->	Dict:


       '''simple docstring'''

       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=UpperCAmelCase		)






      def 					A__   (			self      :		Union[str, Any]		)	->	Any:


       '''simple docstring'''

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






      def 					A__   (			self      :		int		)	->	Dict:


       '''simple docstring'''

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






      def 					A__   (			self      :		List[Any]		)	->	Optional[int]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Any		)	->	Any:


       '''simple docstring'''

       lowercase       : List[Any]						=self.scheduler_classes[0]
       lowercase       : str						=self.get_scheduler_config()
       lowercase       : List[str]						=scheduler_class(**UpperCAmelCase		)

       assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0_9_7_9		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.0_2		)		) < 1e-5






      def 					A__   (			self      :		Dict		)	->	str:


       '''simple docstring'''

       lowercase       : Optional[int]						=self.scheduler_classes[0]
       lowercase       : Union[str, Any]						=self.get_scheduler_config()
       lowercase       : Tuple						=scheduler_class(**UpperCAmelCase		)

       lowercase       : Optional[Any]						=len(UpperCAmelCase		)

       lowercase       : List[Any]						=self.dummy_model()
       lowercase       : List[str]						=self.dummy_sample_deter
       lowercase       : Tuple						=torch.manual_seed(0		)

       for t in reversed(range(UpperCAmelCase		)		):
        # 1. predict noise residual
        lowercase       : Any						=model(UpperCAmelCase  ,   UpperCAmelCase		)

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

        # if t > 0:
        #     noise = self.dummy_sample_deter
        #     variance = scheduler.get_variance(t) ** (0.5) * noise
        #
        # sample = pred_prev_sample + variance
        lowercase       : List[str]						=pred_prev_sample

       lowercase       : Optional[int]						=torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase       : Tuple						=torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 2_5_8.9_6_0_6		) < 1e-2
       assert abs(result_mean.item() - 0.3_3_7_2		) < 1e-3






      def 					A__   (			self      :		List[str]		)	->	Dict:


       '''simple docstring'''

       lowercase       : Dict						=self.scheduler_classes[0]
       lowercase       : Union[str, Any]						=self.get_scheduler_config(prediction_type='''v_prediction'''		)
       lowercase       : int						=scheduler_class(**UpperCAmelCase		)

       lowercase       : List[Any]						=len(UpperCAmelCase		)

       lowercase       : List[str]						=self.dummy_model()
       lowercase       : List[str]						=self.dummy_sample_deter
       lowercase       : List[Any]						=torch.manual_seed(0		)

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

        # 2. predict previous mean of sample x_t-1
        lowercase       : Dict						=scheduler.step(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   generator=UpperCAmelCase		).prev_sample

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

       lowercase       : Tuple						=torch.sum(torch.abs(UpperCAmelCase		)		)
       lowercase       : List[str]						=torch.mean(torch.abs(UpperCAmelCase		)		)

       assert abs(result_sum.item() - 2_0_2.0_2_9_6		) < 1e-2
       assert abs(result_mean.item() - 0.2_6_3_1		) < 1e-3






      def 					A__   (			self      :		Optional[Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self.scheduler_classes[0]
       lowercase       : int						=self.get_scheduler_config()
       lowercase       : Optional[int]						=scheduler_class(**UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[100, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=UpperCAmelCase		)

       lowercase       : Optional[int]						=scheduler.timesteps

       for i, timestep in enumerate(UpperCAmelCase		):
        if i == len(UpperCAmelCase		) - 1:
         lowercase       : str						=-1
        else:
         lowercase       : Optional[Any]						=timesteps[i + 1]

        lowercase       : Union[str, Any]						=scheduler.previous_timestep(UpperCAmelCase		)
        lowercase       : Optional[int]						=prev_t.item()

        self.assertEqual(UpperCAmelCase  ,   UpperCAmelCase		)






      def 					A__   (			self      :		List[Any]		)	->	Any:


       '''simple docstring'''

       lowercase       : List[Any]						=self.scheduler_classes[0]
       lowercase       : Optional[int]						=self.get_scheduler_config()
       lowercase       : List[str]						=scheduler_class(**UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[100, 87, 50, 51, 0]

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






      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Any						=self.scheduler_classes[0]
       lowercase       : Union[str, Any]						=self.get_scheduler_config()
       lowercase       : Optional[Any]						=scheduler_class(**UpperCAmelCase		)

       lowercase       : Any						=[100, 87, 50, 1, 0]
       lowercase       : int						=len(UpperCAmelCase		)

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






      def 					A__   (			self      :		List[Any]		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : str						=self.scheduler_classes[0]
       lowercase       : Optional[int]						=self.get_scheduler_config()
       lowercase       : Union[str, Any]						=scheduler_class(**UpperCAmelCase		)

       lowercase       : str						=[scheduler.config.num_train_timesteps]

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











import os
import sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


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


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)