"""simple docstring"""



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		(DPMSolverSinglestepScheduler,)
 _snake_case   :			Optional[Any]           =		(("""num_inference_steps""", 25),)


 def 		a__ (				self  ,						**lowerCamelCase		):
        __a     				=  {
            "num_train_timesteps": 1000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "prediction_type": "epsilon",
            "thresholding": False,
            "sample_max_value": 1.0,
            "algorithm_type": "dpmsolver++",
            "solver_type": "midpoint",
            "lambda_min_clipped": -float("inf"		),
            "variance_type": None,
        }

        config.update(**lowerCamelCase		)
        return config


 def 		a__ (				self  ,						lowerCamelCase=0  ,						**lowerCamelCase		):
        __a     				=  dict(self.forward_default_kwargs		)
        __a     				=  kwargs.pop("num_inference_steps"  ,						lowerCamelCase		)
        __a     				=  self.dummy_sample
        __a     				=  0.1 * sample
        __a     				=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               __a     				=  self.get_scheduler_config(**lowerCamelCase		)
               __a     				=  scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)
               # copy over dummy past residuals
               __a     				=  dummy_past_residuals[: scheduler.config.solver_order]

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

               __a				, __a     				=  sample, sample
               for t in range(lowerCamelCase  ,						time_step + scheduler.config.solver_order + 1		):
                      __a     				=  scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		).prev_sample
                      __a     				=  new_scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		).prev_sample

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


 def 		a__ (				self		):
        pass


 def 		a__ (				self  ,						lowerCamelCase=0  ,						**lowerCamelCase		):
        __a     				=  dict(self.forward_default_kwargs		)
        __a     				=  kwargs.pop("num_inference_steps"  ,						lowerCamelCase		)
        __a     				=  self.dummy_sample
        __a     				=  0.1 * sample
        __a     				=  [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               __a     				=  self.get_scheduler_config()
               __a     				=  scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)

               # copy over dummy past residuals (must be after setting timesteps)
               __a     				=  dummy_past_residuals[: scheduler.config.solver_order]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      __a     				=  dummy_past_residuals[: new_scheduler.config.solver_order]

               __a     				=  scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		).prev_sample
               __a     				=  new_scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		).prev_sample

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


 def 		a__ (				self  ,						lowerCamelCase=None  ,						**lowerCamelCase		):
        if scheduler is None:
               __a     				=  self.scheduler_classes[0]
               __a     				=  self.get_scheduler_config(**lowerCamelCase		)
               __a     				=  scheduler_class(**lowerCamelCase		)

        __a     				=  self.scheduler_classes[0]
        __a     				=  self.get_scheduler_config(**lowerCamelCase		)
        __a     				=  scheduler_class(**lowerCamelCase		)

        __a     				=  10
        __a     				=  self.dummy_model()
        __a     				=  self.dummy_sample_deter
        scheduler.set_timesteps(lowerCamelCase		)

        for i, t in enumerate(scheduler.timesteps		):
               __a     				=  model(lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		).prev_sample

        return sample


 def 		a__ (				self		):
        __a     				=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        __a     				=  50
        __a     				=  self.dummy_model()
        __a     				=  self.dummy_sample_deter
        scheduler.set_timesteps(lowerCamelCase		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
               __a     				=  model(lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		).prev_sample

        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.2574		) < 1E-3


 def 		a__ (				self		):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


 def 		a__ (				self		):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __a     				=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        __a     				=  self.full_loop(scheduler=lowerCamelCase		)
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.2791		) < 1E-3

        __a     				=  DEISMultistepScheduler.from_config(scheduler.config		)
        __a     				=  DPMSolverMultistepScheduler.from_config(scheduler.config		)
        __a     				=  UniPCMultistepScheduler.from_config(scheduler.config		)
        __a     				=  DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        __a     				=  self.full_loop(scheduler=lowerCamelCase		)
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.2791		) < 1E-3


 def 		a__ (				self		):
        self.check_over_configs(thresholding=lowerCamelCase		)
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=lowerCamelCase  ,						prediction_type=lowerCamelCase  ,						sample_max_value=lowerCamelCase  ,						algorithm_type="dpmsolver++"  ,						solver_order=lowerCamelCase  ,						solver_type=lowerCamelCase  ,						)


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


 def 		a__ (				self		):
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        solver_order=lowerCamelCase  ,						solver_type=lowerCamelCase  ,						prediction_type=lowerCamelCase  ,						algorithm_type=lowerCamelCase  ,						)
                                    __a     				=  self.full_loop(
                                        solver_order=lowerCamelCase  ,						solver_type=lowerCamelCase  ,						prediction_type=lowerCamelCase  ,						algorithm_type=lowerCamelCase  ,						)
                                    assert not torch.isnan(lowerCamelCase		).any(), "Samples have nan numbers"


 def 		a__ (				self		):
        self.check_over_configs(lower_order_final=lowerCamelCase		)
        self.check_over_configs(lower_order_final=lowerCamelCase		)


 def 		a__ (				self		):
        self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


 def 		a__ (				self		):
        self.check_over_configs(variance_type=lowerCamelCase		)
        self.check_over_configs(variance_type="learned_range"		)


 def 		a__ (				self		):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=lowerCamelCase  ,						time_step=0		)


 def 		a__ (				self		):
        __a     				=  self.full_loop()
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.2791		) < 1E-3


 def 		a__ (				self		):
        __a     				=  self.full_loop(use_karras_sigmas=lowerCamelCase		)
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.2248		) < 1E-3


 def 		a__ (				self		):
        __a     				=  self.full_loop(prediction_type="v_prediction"		)
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.1453		) < 1E-3


 def 		a__ (				self		):
        __a     				=  self.full_loop(prediction_type="v_prediction"  ,						use_karras_sigmas=lowerCamelCase		)
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

        assert abs(result_mean.item() - 0.0649		) < 1E-3






 def 		a__ (				self		):
        __a     				=  self.scheduler_classes[0]
        __a     				=  self.get_scheduler_config(thresholding=lowerCamelCase  ,						dynamic_thresholding_ratio=0		)
        __a     				=  scheduler_class(**lowerCamelCase		)

        __a     				=  10
        __a     				=  self.dummy_model()
        __a     				=  self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCamelCase		)

        for i, t in enumerate(scheduler.timesteps		):
               __a     				=  model(lowerCamelCase  ,						lowerCamelCase		)
               __a     				=  scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		).prev_sample

        assert sample.dtype == torch.floataa

"""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

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        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 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

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

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