'''simple docstring'''


from collections import UserDict
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_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
       from ..tf_utils import stable_softmax

__snake_case						  =		logging.get_logger(__name__)





@add_end_docstrings(__a      )
class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       List[str]   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


    super().__init__(**lowercase_      )

    requires_backends(self   ,				"""vision"""      )
    self.check_model_type(
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
        if self.framework == """tf"""
        else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING      )




  def __call__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, List[str], "Image", List["Image"]]   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     =       {}
    if "candidate_labels" in kwargs:
      lowercase_     =       kwargs["""candidate_labels"""]
    if "hypothesis_template" in kwargs:
      lowercase_     =       kwargs["""hypothesis_template"""]

    return preprocess_params, {}, {}




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Dict   ,				lowercase_      :       Optional[int]=None   ,				lowercase_      :       Dict="This is a photo of {}."      ):







    '''simple docstring'''


    lowercase_     =       load_image(lowercase_      )
    lowercase_     =       self.image_processor(images=[image]   ,				return_tensors=self.framework      )
    lowercase_     =       candidate_labels
    lowercase_     =       [hypothesis_template.format(lowercase_      ) for x in candidate_labels]
    lowercase_     =       self.tokenizer(lowercase_   ,				return_tensors=self.framework   ,				padding=lowercase_      )
    lowercase_     =       [text_inputs]
    return inputs




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       model_inputs.pop("""candidate_labels"""      )
    lowercase_     =       model_inputs.pop("""text_inputs"""      )
    if isinstance(text_inputs[0]   ,				lowercase_      ):
      lowercase_     =       text_inputs[0]
    else:
      # Batching case.
      lowercase_     =       text_inputs[0][0]

    lowercase_     =       self.model(**lowercase_   ,				**lowercase_      )

    lowercase_     =       {
        """candidate_labels""": candidate_labels,
        """logits""": outputs.logits_per_image,
    }
    return model_outputs





  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       model_outputs.pop("""candidate_labels"""      )
    lowercase_     =       model_outputs["""logits"""][0]
    if self.framework == "pt":
      lowercase_     =       logits.softmax(dim=-1      ).squeeze(-1      )
      lowercase_     =       probs.tolist()
      if not isinstance(lowercase_   ,				lowercase_      ):
        lowercase_     =       [scores]
    elif self.framework == "tf":
      lowercase_     =       stable_softmax(lowercase_   ,				axis=-1      )
      lowercase_     =       probs.numpy().tolist()
    else:
      raise ValueError(F"""Unsupported framework: {self.framework}"""      )

    lowercase_     =       [
        {"""score""": score, """label""": candidate_label}
        for score, candidate_label in sorted(zip(lowercase_   ,				lowercase_      )   ,				key=lambda lowercase_      : -x[0]      )
    ]
    return result


'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
        """variance_type""": """fixed_small""",
        """clip_sample""": True,
    }

    config.update(**lowercase_      )
    return config




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[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=lowercase_   ,				beta_end=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    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 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       self.dummy_sample_deter + 0.1
    lowercase_     =       self.dummy_sample_deter - 0.1

    lowercase_     =       samplea.shape[0]
    lowercase_     =       torch.stack([samplea, samplea, samplea]   ,				dim=0      )
    lowercase_     =       torch.arange(lowercase_      )[0:3, None].repeat(1   ,				lowercase_      )

    lowercase_     =       model(samples.flatten(0   ,				1      )   ,				timesteps.flatten(0   ,				1      )      )
    lowercase_     =       scheduler.batch_step_no_noise(lowercase_   ,				timesteps.flatten(0   ,				1      )   ,				samples.flatten(0   ,				1      )      )

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


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

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    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 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    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 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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

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

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

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

      lowercase_     =       scheduler.previous_timestep(lowercase_      )
      lowercase_     =       prev_t.item()

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

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

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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

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

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





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


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

    lowercase_     =       [scheduler.config.num_train_timesteps]

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

