




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					(IPNDMScheduler,)
						UpperCamelCase     						=					(("""num_inference_steps""", 50),)

						def     A__						(		self   :List[str]    , **__snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								={"""num_train_timesteps""": 10_00}
										config.update(**__snake_case )
										return config

						def     A__						(		self   :int    , __snake_case   :str=0    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=dict(self.forward_default_kwargs )
										__magic_name__	:	Dict								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	Dict								=self.dummy_sample
										__magic_name__	:	List[Any]								=0.1 * sample
										__magic_name__	:	Any								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	List[str]								=self.get_scheduler_config(**__snake_case )
														__magic_name__	:	List[str]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )
														# copy over dummy past residuals
														__magic_name__	:	List[str]								=dummy_past_residuals[:]

														if time_step is None:
																		__magic_name__	:	Tuple								=scheduler.timesteps[len(scheduler.timesteps ) // 2]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case )
																		__magic_name__	:	List[Any]								=scheduler_class.from_pretrained(__snake_case )
																		new_scheduler.set_timesteps(__snake_case )
																		# copy over dummy past residuals
																		__magic_name__	:	Union[str, Any]								=dummy_past_residuals[:]

														__magic_name__	:	int								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Optional[int]								=new_scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

														__magic_name__	:	List[Any]								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	str								=new_scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, Any]=0    , **__snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=dict(self.forward_default_kwargs )
										__magic_name__	:	Tuple								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	Optional[int]								=self.dummy_sample
										__magic_name__	:	Tuple								=0.1 * sample
										__magic_name__	:	List[str]								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	List[str]								=self.get_scheduler_config()
														__magic_name__	:	Optional[int]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )

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

														if time_step is None:
																		__magic_name__	:	Union[str, Any]								=scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

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

														__magic_name__	:	int								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	List[Any]								=new_scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

														__magic_name__	:	Optional[Any]								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Optional[int]								=new_scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

						def     A__						(		self   :int    , **__snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.scheduler_classes[0]
										__magic_name__	:	Optional[int]								=self.get_scheduler_config(**__snake_case )
										__magic_name__	:	str								=scheduler_class(**__snake_case )

										__magic_name__	:	Dict								=10
										__magic_name__	:	Dict								=self.dummy_model()
										__magic_name__	:	int								=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case )

										for i, t in enumerate(scheduler.timesteps ):
														__magic_name__	:	Dict								=model(__snake_case    , __snake_case )
														__magic_name__	:	int								=scheduler.step(__snake_case    , __snake_case    , __snake_case ).prev_sample

										for i, t in enumerate(scheduler.timesteps ):
														__magic_name__	:	str								=model(__snake_case    , __snake_case )
														__magic_name__	:	str								=scheduler.step(__snake_case    , __snake_case    , __snake_case ).prev_sample

										return sample

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=dict(self.forward_default_kwargs )

										__magic_name__	:	Any								=kwargs.pop("""num_inference_steps"""    , __snake_case )

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Optional[int]								=self.get_scheduler_config()
														__magic_name__	:	Optional[int]								=scheduler_class(**__snake_case )

														__magic_name__	:	str								=self.dummy_sample
														__magic_name__	:	Optional[int]								=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case    , """set_timesteps""" ):
																		scheduler.set_timesteps(__snake_case )
														elif num_inference_steps is not None and not hasattr(__snake_case    , """set_timesteps""" ):
																		__magic_name__	:	Optional[int]								=num_inference_steps

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

														__magic_name__	:	Union[str, Any]								=scheduler.timesteps[5]
														__magic_name__	:	Dict								=scheduler.timesteps[6]

														__magic_name__	:	Dict								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Optional[int]								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

														__magic_name__	:	Dict								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Union[str, Any]								=scheduler.step(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

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

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case    , time_step=__snake_case )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]    , [10, 50, 1_00] ):
														self.check_over_forward(num_inference_steps=__snake_case    , time_step=__snake_case )


						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.full_loop()
										__magic_name__	:	Optional[Any]								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_mean.item() - 2_54_05_29 ) < 10










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_		:				str									=     logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__			)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Any    , *__snake_case   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										super().__init__(*__snake_case    , **__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     A__						(		self   :int    , __snake_case   :Optional[Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Any								={}
										if top_k is not None:
														__magic_name__	:	str								=top_k
										return {}, {}, postprocess_params

						def __call__(		self   :Tuple    , __snake_case   :Union[str, List[str], "Image.Image", List["Image.Image"]]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=load_image(__snake_case )
										__magic_name__	:	Any								=self.image_processor(images=__snake_case    , return_tensors=self.framework )
										return model_inputs

						def     A__						(		self   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model(**__snake_case )
										return model_outputs


						def     A__						(		self   :List[str]    , __snake_case   :int    , __snake_case   :Union[str, Any]=5 ):
										'''simple docstring'''

										if top_k > self.model.config.num_labels:
														__magic_name__	:	Dict								=self.model.config.num_labels

										if self.framework == "pt":
														__magic_name__	:	int								=model_outputs.logits.softmax(-1 )[0]
														__magic_name__				, __magic_name__	:	str								=probs.topk(__snake_case )
										elif self.framework == "tf":
														__magic_name__	:	Union[str, Any]								=stable_softmax(model_outputs.logits    , axis=-1 )[0]
														__magic_name__	:	List[str]								=tf.math.top_k(__snake_case    , k=__snake_case )
														__magic_name__				, __magic_name__	:	str								=topk.values.numpy(), topk.indices.numpy()
										else:
														raise ValueError(f"Unsupported framework: {self.framework}" )

										__magic_name__	:	str								=scores.tolist()
										__magic_name__	:	Union[str, Any]								=ids.tolist()
										return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__snake_case    , __snake_case )]




