

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

snake_case_			  = logging.get_logger(__name__)


@add_end_docstrings(__snake_case     )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   **a):
         super().__init__(**a)

         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						,   a						,   **a):
         return super().__call__(a						,   **a)





    def       snake_case_				(      self						,   **a):
         lowercase__    :   Optional[Any]      =       {}
         if "candidate_labels" in kwargs:
              lowercase__    :   int      =       kwargs['candidate_labels']
         if "hypothesis_template" in kwargs:
              lowercase__    :   Tuple      =       kwargs['hypothesis_template']

         return preprocess_params, {}, {}





    def       snake_case_				(      self						,   a						,   a=None						,   a="This is a photo of {}."):
         lowercase__    :   List[str]      =       load_image(a)
         lowercase__    :   str      =       self.image_processor(images=[image]						,   return_tensors=self.framework)
         lowercase__    :   Optional[Any]      =       candidate_labels
         lowercase__    :   Tuple      =       [hypothesis_template.format(a) for x in candidate_labels]
         lowercase__    :   Optional[int]      =       self.tokenizer(a						,   return_tensors=self.framework						,   padding=a)
         lowercase__    :   List[str]      =       [text_inputs]
         return inputs





    def       snake_case_				(      self						,   a):
         lowercase__    :   str      =       model_inputs.pop('candidate_labels')
         lowercase__    :   List[Any]      =       model_inputs.pop('text_inputs')
         if isinstance(text_inputs[0]						,   a):
              lowercase__    :   Tuple      =       text_inputs[0]
         else:
              # Batching case.
              lowercase__    :   int      =       text_inputs[0][0]

         lowercase__    :   int      =       self.model(**a						,   **a)

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





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

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

