


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_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__snake_case   							=						logging.get_logger(__name__)



@add_end_docstrings(_a						)
class 	__lowerCamelCase			(_a						):







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






            '''simple docstring'''




            super().__init__(*A_,**A_					)
            requires_backends(self,'vision'					)
            self.check_model_type(
                TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == 'tf'
                else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING					)







       def 		snake_case_	(			self: Dict,A_: Tuple=None					):






            '''simple docstring'''




            __UpperCamelCase =			{}
            if top_k is not None:
                 __UpperCamelCase =			top_k
            return {}, {}, postprocess_params







       def __call__(			self: List[Any],A_: Union[str, List[str], "Image.Image", List["Image.Image"]],**A_: Optional[int]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			load_image(A_					)
            __UpperCamelCase =			self.image_processor(images=A_,return_tensors=self.framework					)
            return model_inputs







       def 		snake_case_	(			self: str,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model(**A_					)
            return model_outputs







       def 		snake_case_	(			self: Optional[Any],A_: Dict,A_: Optional[int]=5					):






            '''simple docstring'''




            if top_k > self.model.config.num_labels:
                 __UpperCamelCase =			self.model.config.num_labels

            if self.framework == "pt":
                 __UpperCamelCase =			model_outputs.logits.softmax(-1					)[0]
                 __UpperCamelCase,    __UpperCamelCase =			probs.topk(A_					)
            elif self.framework == "tf":
                 __UpperCamelCase =			stable_softmax(model_outputs.logits,axis=-1					)[0]
                 __UpperCamelCase =			tf.math.top_k(A_,k=A_					)
                 __UpperCamelCase,    __UpperCamelCase =			topk.values.numpy(), topk.indices.numpy()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''					)

            __UpperCamelCase =			scores.tolist()
            __UpperCamelCase =			ids.tolist()
            return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A_,A_					)]






__snake_case   							=						{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__snake_case   							=						{value: key for key, value in encode_dict.items()}






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			''
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception('encode() accepts only letters of the alphabet and spaces' )
     return encoded






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     if set(_lowercase ) - {"A", "B", " "} != set():
          raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
     __UpperCamelCase =			''
     for word in coded.split():
          while len(_lowercase ) != 0:
               decoded += decode_dict[word[:5]]
               __UpperCamelCase =			word[5:]
          decoded += " "
     return decoded.strip()


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

    testmod()


