

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

SCREAMING_SNAKE_CASE		:Any						=     logging.get_logger(__name__)






@add_end_docstrings(snake_case				)
class 						__magic_name__					(							snake_case				):






     def __init__(				self		,						*_lowercase		,						**_lowercase	)->     str:
         super().__init__(*_lowercase		,						**_lowercase	)
         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 	UpperCAmelCase_				(				self		,						_lowercase=None	)->     Optional[Any]:
         UpperCamelCase_			  =						{}
         if top_k is not None:
             UpperCamelCase_			  =						top_k
         return {}, {}, postprocess_params






     def __call__(				self		,						_lowercase		,						**_lowercase	)->     int:
         return super().__call__(_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Optional[int]:
         UpperCamelCase_			  =						load_image(_lowercase	)
         UpperCamelCase_			  =						self.image_processor(images=_lowercase		,						return_tensors=self.framework	)
         return model_inputs






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Tuple:
         UpperCamelCase_			  =						self.model(**_lowercase	)
         return model_outputs






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase=5	)->     Tuple:
         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(_lowercase	)
         elif self.framework == "tf":
             UpperCamelCase_			  =						stable_softmax(model_outputs.logits		,						axis=-1	)[0]
             UpperCamelCase_			  =						tf.math.top_k(_lowercase		,						k=_lowercase	)
             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(_lowercase		,						_lowercase	)]



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count < 0:
        raise ValueError("The given input must be positive"			)

    # get the generated string sequence
    UpperCamelCase_			  =						gray_code_sequence_string(SCREAMING_SNAKE_CASE_			)
    #
    # convert them to integers
    for i in range(len(SCREAMING_SNAKE_CASE_			)			):
        UpperCamelCase_			  =						int(sequence[i]			,						2			)

    return sequence




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    UpperCamelCase_			  =						1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    UpperCamelCase_			  =						gray_code_sequence_string(bit_count - 1			)

    UpperCamelCase_			  =						[]

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        UpperCamelCase_			  =						"0" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        UpperCamelCase_			  =						"1" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()
