
"""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

lowerCAmelCase__				=					logging.get_logger(__name__)





@add_end_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):

							def __init__(self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )

									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							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

									return preprocess_params, {}, {}

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_="This is a photo of {}."     ):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_    			=			{
									    '''candidate_labels''': candidate_labels,
									    '''logits''': outputs.logits_per_image,
									}
									return model_outputs







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

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

"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler