



"""simple docstring"""





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				lowercase__       (   __snake_case				):







       '''simple docstring'''


       UpperCamelCase						=					(PNDMScheduler,)
       UpperCamelCase						=					(('num_inference_steps', 50),)



       def 					lowercase__    (							self      :  Optional[Any]		,    **_UpperCAmelCase      :  Any   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							{
                 "num_train_timesteps": 1000,
                 "beta_start": 0.0001,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
             }

             config.update(**_UpperCAmelCase   )
             return config



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[Any]=0		,    **_UpperCAmelCase      :  Any   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							dict(self.forward_default_kwargs   )
             UpperCAmelCase_      			=							kwargs.pop("num_inference_steps"		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							self.dummy_sample
             UpperCAmelCase_      			=							0.1 * sample
             UpperCAmelCase_      			=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   UpperCAmelCase_      			=							self.get_scheduler_config(**_UpperCAmelCase   )
                   UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )
                   scheduler.set_timesteps(_UpperCAmelCase   )
                   # copy over dummy past residuals
                   UpperCAmelCase_      			=							dummy_past_residuals[:]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							scheduler_class.from_pretrained(_UpperCAmelCase   )
                         new_scheduler.set_timesteps(_UpperCAmelCase   )
                         # copy over dummy past residuals
                         UpperCAmelCase_      			=							dummy_past_residuals[:]

                   UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							new_scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

                   UpperCAmelCase_      			=							scheduler.step_plms(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							new_scheduler.step_plms(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"



       def 					lowercase__    (							self      :  List[Any]   )			->						Dict:


             '''simple docstring'''




             pass



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Optional[Any]=0		,    **_UpperCAmelCase      :  List[Any]   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							dict(self.forward_default_kwargs   )
             UpperCAmelCase_      			=							kwargs.pop("num_inference_steps"		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							self.dummy_sample
             UpperCAmelCase_      			=							0.1 * sample
             UpperCAmelCase_      			=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   UpperCAmelCase_      			=							self.get_scheduler_config()
                   UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )
                   scheduler.set_timesteps(_UpperCAmelCase   )

                   # copy over dummy past residuals (must be after setting timesteps)
                   UpperCAmelCase_      			=							dummy_past_residuals[:]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							scheduler_class.from_pretrained(_UpperCAmelCase   )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(_UpperCAmelCase   )

                         # copy over dummy past residual (must be after setting timesteps)
                         UpperCAmelCase_      			=							dummy_past_residuals[:]

                   UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							new_scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

                   UpperCAmelCase_      			=							scheduler.step_plms(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							new_scheduler.step_plms(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"



       def 					lowercase__    (							self      :  int		,    **_UpperCAmelCase      :  int   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.scheduler_classes[0]
             UpperCAmelCase_      			=							self.get_scheduler_config(**_UpperCAmelCase   )
             UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )

             UpperCAmelCase_      			=							10
             UpperCAmelCase_      			=							self.dummy_model()
             UpperCAmelCase_      			=							self.dummy_sample_deter
             scheduler.set_timesteps(_UpperCAmelCase   )

             for i, t in enumerate(scheduler.prk_timesteps   ):
                   UpperCAmelCase_      			=							model(_UpperCAmelCase		,    _UpperCAmelCase   )
                   UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   ).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps   ):
                   UpperCAmelCase_      			=							model(_UpperCAmelCase		,    _UpperCAmelCase   )
                   UpperCAmelCase_      			=							scheduler.step_plms(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   ).prev_sample

             return sample



       def 					lowercase__    (							self      :  str   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							dict(self.forward_default_kwargs   )

             UpperCAmelCase_      			=							kwargs.pop("num_inference_steps"		,    _UpperCAmelCase   )

             for scheduler_class in self.scheduler_classes:
                   UpperCAmelCase_      			=							self.get_scheduler_config()
                   UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )

                   UpperCAmelCase_      			=							self.dummy_sample
                   UpperCAmelCase_      			=							0.1 * sample

                   if num_inference_steps is not None and hasattr(_UpperCAmelCase		,    "set_timesteps"   ):
                         scheduler.set_timesteps(_UpperCAmelCase   )
                   elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,    "set_timesteps"   ):
                         UpperCAmelCase_      			=							num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   UpperCAmelCase_      			=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                   UpperCAmelCase_      			=							dummy_past_residuals[:]

                   UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    0		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    1		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   self.assertEqual(output_a.shape		,    sample.shape   )
                   self.assertEqual(output_a.shape		,    output_a.shape   )

                   UpperCAmelCase_      			=							scheduler.step_plms(_UpperCAmelCase		,    0		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample
                   UpperCAmelCase_      			=							scheduler.step_plms(_UpperCAmelCase		,    1		,    _UpperCAmelCase		,    **_UpperCAmelCase   ).prev_sample

                   self.assertEqual(output_a.shape		,    sample.shape   )
                   self.assertEqual(output_a.shape		,    output_a.shape   )



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


             '''simple docstring'''




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



       def 					lowercase__    (							self      :  str   )			->						Optional[Any]:


             '''simple docstring'''




             for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=_UpperCAmelCase   )

             UpperCAmelCase_      			=							self.scheduler_classes[0]
             UpperCAmelCase_      			=							self.get_scheduler_config(steps_offset=1   )
             UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )
             scheduler.set_timesteps(10   )
             assert torch.equal(
                 scheduler.timesteps		,    torch.LongTensor(
                     [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )		,    )



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


             '''simple docstring'''




             for beta_start, beta_end in zip([0.0001, 0.001]		,    [0.002, 0.02]   ):
                   self.check_over_configs(beta_start=_UpperCAmelCase		,    beta_end=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						Any:


             '''simple docstring'''




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



       def 					lowercase__    (							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




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



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


             '''simple docstring'''




             for t in [1, 5, 10]:
                   self.check_over_forward(time_step=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  str   )			->						Any:


             '''simple docstring'''




             for t, num_inference_steps in zip([1, 5, 10]		,    [10, 50, 100]   ):
                   self.check_over_forward(num_inference_steps=_UpperCAmelCase   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							27

             for scheduler_class in self.scheduler_classes:
                   UpperCAmelCase_      			=							self.dummy_sample
                   UpperCAmelCase_      			=							0.1 * sample

                   UpperCAmelCase_      			=							self.get_scheduler_config()
                   UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )

                   scheduler.set_timesteps(_UpperCAmelCase   )

                   # before power of 3 fix, would error on first step, so we only need to do two
                   for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
                         UpperCAmelCase_      			=							scheduler.step_prk(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   ).prev_sample



       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             with self.assertRaises(_UpperCAmelCase   ):
                   UpperCAmelCase_      			=							self.scheduler_classes[0]
                   UpperCAmelCase_      			=							self.get_scheduler_config()
                   UpperCAmelCase_      			=							scheduler_class(**_UpperCAmelCase   )

                   scheduler.step_plms(self.dummy_sample		,    1		,    self.dummy_sample   ).prev_sample



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.full_loop()
             UpperCAmelCase_      			=							torch.sum(torch.abs(_UpperCAmelCase   )   )
             UpperCAmelCase_      			=							torch.mean(torch.abs(_UpperCAmelCase   )   )

             assert abs(result_sum.item() - 198.1318   ) < 1e-2
             assert abs(result_mean.item() - 0.2580   ) < 1e-3



       def 					lowercase__    (							self      :  Dict   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.full_loop(prediction_type="v_prediction"   )
             UpperCAmelCase_      			=							torch.sum(torch.abs(_UpperCAmelCase   )   )
             UpperCAmelCase_      			=							torch.mean(torch.abs(_UpperCAmelCase   )   )

             assert abs(result_sum.item() - 67.3986   ) < 1e-2
             assert abs(result_mean.item() - 0.0878   ) < 1e-3



       def 					lowercase__    (							self      :  str   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.full_loop(set_alpha_to_one=_UpperCAmelCase		,    beta_start=0.01   )
             UpperCAmelCase_      			=							torch.sum(torch.abs(_UpperCAmelCase   )   )
             UpperCAmelCase_      			=							torch.mean(torch.abs(_UpperCAmelCase   )   )

             assert abs(result_sum.item() - 230.0399   ) < 1e-2
             assert abs(result_mean.item() - 0.2995   ) < 1e-3







       def 					lowercase__    (							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.full_loop(set_alpha_to_one=_UpperCAmelCase		,    beta_start=0.01   )
             UpperCAmelCase_      			=							torch.sum(torch.abs(_UpperCAmelCase   )   )
             UpperCAmelCase_      			=							torch.mean(torch.abs(_UpperCAmelCase   )   )

             assert abs(result_sum.item() - 186.9482   ) < 1e-2
             assert abs(result_mean.item() - 0.2434   ) < 1e-3









"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''ViTImageProcessor'''
       UpperCamelCase						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')



       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."		,    _UpperCAmelCase		,    )
                   UpperCAmelCase_      			=							kwargs.pop("feature_extractor"   )

             UpperCAmelCase_      			=							image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`."   )
             if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`."   )

             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )



       def __call__(							self      :  Optional[Any]		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  str   )			->						Optional[int]:


             '''simple docstring'''




             if text is None and visual_prompt is None and images is None:
                   raise ValueError("You have to specify either text, visual prompt or images."   )

             if text is not None and visual_prompt is not None:
                   raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if images is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None and images is not None:
                   UpperCAmelCase_      			=							{
                       "pixel_values": image_features.pixel_values,
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             elif text is not None and images is not None:
                   UpperCAmelCase_      			=							image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             elif visual_prompt is not None:
                   UpperCAmelCase_      			=							{
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_UpperCAmelCase   )		,    tensor_type=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  List[Any]   )			->						Any:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Dict		,    *_UpperCAmelCase      :  Tuple		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             return self.tokenizer.decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor_class







       @property
       def 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor




