
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       :				List[str]       =			logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase					)
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  **lowercase	:    List[str]	):


				'''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	:    Optional[Any]			,  lowercase	:    Union[str, List[str], "Image", List["Image"]]			,  **lowercase	:    str	):


				'''simple docstring'''


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



	def  A							(  self	:    Union[str, Any]			,  **lowercase	:    int	):


				'''simple docstring'''


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

				return preprocess_params, {}, {}



	def  A							(  self	:    str			,  lowercase	:    Tuple			,  lowercase	:    Optional[Any]=None			,  lowercase	:    Dict="This is a photo of {}."	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple			,  lowercase	:    str	):


				'''simple docstring'''


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

				_snake_case		=			self.model(**lowercase			,  **lowercase	)

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





	def  A							(  self	:    List[str]			,  lowercase	:    str	):


				'''simple docstring'''


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

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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()