

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

a		=logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class 	A_		(	SCREAMING_SNAKE_CASE    ):
       def __init__(		self		:      int       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
        super().__init__(**_lowerCAmelCase)

        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		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, List[str], "Image", List["Image"]]       ,**SCREAMING_SNAKE_CASE__		:      Any):
        return super().__call__(_lowerCAmelCase       ,**_lowerCAmelCase)
       def    lowerCAmelCase					(		self		:      List[str]       ,**SCREAMING_SNAKE_CASE__		:      List[str]):
        __lowerCamelCase    :    Tuple 	=			{}
        if "candidate_labels" in kwargs:
         __lowerCamelCase    :    List[str] 	=			kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
         __lowerCamelCase    :    Optional[int] 	=			kwargs['hypothesis_template']

        return preprocess_params, {}, {}
       def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str=None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]="This is a photo of {}."):
        __lowerCamelCase    :    Optional[int] 	=			load_image(_lowerCAmelCase)
        __lowerCamelCase    :    List[str] 	=			self.image_processor(images=[image]       ,return_tensors=self.framework)
        __lowerCamelCase    :    Tuple 	=			candidate_labels
        __lowerCamelCase    :    List[str] 	=			[hypothesis_template.format(_lowerCAmelCase) for x in candidate_labels]
        __lowerCamelCase    :    Any 	=			self.tokenizer(_lowerCAmelCase       ,return_tensors=self.framework       ,padding=_lowerCAmelCase)
        __lowerCamelCase    :    str 	=			[text_inputs]
        return inputs
       def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str):
        __lowerCamelCase    :    Any 	=			model_inputs.pop('candidate_labels')
        __lowerCamelCase    :    Tuple 	=			model_inputs.pop('text_inputs')
        if isinstance(text_inputs[0]       ,_lowerCAmelCase):
         __lowerCamelCase    :    List[Any] 	=			text_inputs[0]
        else:
         # Batching case.
         __lowerCamelCase    :    Any 	=			text_inputs[0][0]

        __lowerCamelCase    :    str 	=			self.model(**_lowerCAmelCase       ,**_lowerCAmelCase)

        __lowerCamelCase    :    List[str] 	=			{
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_image,
        }
        return model_outputs


       def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
        __lowerCamelCase    :    Optional[Any] 	=			model_outputs.pop('candidate_labels')
        __lowerCamelCase    :    Any 	=			model_outputs['logits'][0]
        if self.framework == "pt":
         __lowerCamelCase    :    Union[str, Any] 	=			logits.softmax(dim=-1).squeeze(-1)
         __lowerCamelCase    :    List[Any] 	=			probs.tolist()
         if not isinstance(_lowerCAmelCase       ,_lowerCAmelCase):
          __lowerCamelCase    :    Tuple 	=			[scores]
        elif self.framework == "tf":
         __lowerCamelCase    :    Dict 	=			stable_softmax(_lowerCAmelCase       ,axis=-1)
         __lowerCamelCase    :    Dict 	=			probs.numpy().tolist()
        else:
         raise ValueError(F"Unsupported framework: {self.framework}")

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







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




