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

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





@add_end_docstrings(UpperCamelCase_)
class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Union[str, Any]					,	**UpperCamelCase__			:       int		):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												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			:       Tuple					,	UpperCamelCase__			:       Union[str, List[str], "Image", List["Image"]]					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       List[Any]					,	**UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							{}
												if "candidate_labels" in kwargs:
																		SCREAMING_SNAKE_CASE							:					Dict           =							kwargs['''candidate_labels''']
												if "hypothesis_template" in kwargs:
																		SCREAMING_SNAKE_CASE							:					int           =							kwargs['''hypothesis_template''']

												return preprocess_params, {}, {}


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Any="This is a photo of {}."		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							load_image(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.image_processor(images=[image]					,	return_tensors=self.framework		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							candidate_labels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[hypothesis_template.format(UpperCamelCase__		) for x in candidate_labels]
												SCREAMING_SNAKE_CASE							:					Dict           =							self.tokenizer(UpperCamelCase__					,	return_tensors=self.framework					,	padding=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[text_inputs]
												return inputs


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							model_inputs.pop('''candidate_labels'''		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model_inputs.pop('''text_inputs'''		)
												if isinstance(text_inputs[0]					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_inputs[0]
												else:
																		# Batching case.
																		SCREAMING_SNAKE_CASE							:					int           =							text_inputs[0][0]

												SCREAMING_SNAKE_CASE							:					Any           =							self.model(**UpperCamelCase__					,	**UpperCamelCase__		)

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



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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

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






import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





