

"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase    (a_      ):
			lowerCamelCase__ :		Dict       		=				(DDPMScheduler,)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					**__UpperCAmelCase					:    List[Any]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   {
						    """num_train_timesteps""": 1_0_0_0,
						    """beta_start""": 0.0_001,
						    """beta_end""": 0.02,
						    """beta_schedule""": """linear""",
						    """variance_type""": """fixed_small""",
						    """clip_sample""": True,
						}

						config.update(**_SCREAMING_SNAKE_CASE							)
						return config

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

						for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:

						for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]     ,					[0.002, 0.02, 0.2, 2]							):
									self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE     ,					beta_end=_SCREAMING_SNAKE_CASE							)

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

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

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

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

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

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[Any]:
						self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE							)
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(
												    thresholding=_SCREAMING_SNAKE_CASE     ,					prediction_type=_SCREAMING_SNAKE_CASE     ,					sample_max_value=_SCREAMING_SNAKE_CASE     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     int:

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

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

						for t in [0, 5_0_0, 9_9_9]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.00_979							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.02							)							) < 1e-5

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   len(_SCREAMING_SNAKE_CASE							)

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

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

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

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

						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

						assert abs(result_sum.item() - 258.9_606							) < 1e-2
						assert abs(result_mean.item() - 0.3_372							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(prediction_type="""v_prediction"""							)
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   len(_SCREAMING_SNAKE_CASE							)

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

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

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

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

						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(_SCREAMING_SNAKE_CASE							)							)

						assert abs(result_sum.item() - 202.0_296							) < 1e-2
						assert abs(result_mean.item() - 0.2_631							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   [1_0_0, 8_7, 5_0, 1, 0]

						scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   scheduler.timesteps

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

									SCREAMING_SNAKE_CASE__										=   scheduler.previous_timestep(_SCREAMING_SNAKE_CASE							)
									SCREAMING_SNAKE_CASE__										=   prev_t.item()

									self.assertEqual(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   [1_0_0, 8_7, 5_0, 5_1, 0]

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

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   [1_0_0, 8_7, 5_0, 1, 0]
						SCREAMING_SNAKE_CASE__										=   len(_SCREAMING_SNAKE_CASE							)

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





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**_SCREAMING_SNAKE_CASE							)

						SCREAMING_SNAKE_CASE__										=   [scheduler.config.num_train_timesteps]

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





'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)



@add_end_docstrings(a_			)
class 				_snake_case					(   a_			):

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      requires_backends(self      , 'vision'	)
      self.check_model_type(
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      if top_k is not None:
          lowerCAmelCase 						=							top_k
      return {}, {}, postprocess_params

  def __call__(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return super().__call__(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							load_image(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors=self.framework	)
      return model_inputs

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model(**_SCREAMING_SNAKE_CASE	)
      return model_outputs


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=5	):





      '''simple docstring'''

      if top_k > self.model.config.num_labels:
          lowerCAmelCase 						=							self.model.config.num_labels

      if self.framework == "pt":
          lowerCAmelCase 						=							model_outputs.logits.softmax(-1	)[0]
          lowerCAmelCase						,   lowerCAmelCase 						=							probs.topk(_SCREAMING_SNAKE_CASE	)
      elif self.framework == "tf":
          lowerCAmelCase 						=							stable_softmax(model_outputs.logits      , axis=-1	)[0]
          lowerCAmelCase 						=							tf.math.top_k(_SCREAMING_SNAKE_CASE      , k=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase						,   lowerCAmelCase 						=							topk.values.numpy(), topk.indices.numpy()
      else:
          raise ValueError(F'Unsupported framework: {self.framework}'	)

      lowerCAmelCase 						=							scores.tolist()
      lowerCAmelCase 						=							ids.tolist()
      return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)]





