



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   UpperCAmelCase_    (       a):







							def __init__(   self,			*__a,			**__a):

														'''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,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    {}
														if top_k is not None:
																					_lowerCAmelCase     :							Dict    =    top_k
														return {}, {}, postprocess_params







							def __call__(   self,			__a,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    load_image(__a)
														_lowerCAmelCase     :							Tuple    =    self.image_processor(images=__a,			return_tensors=self.framework)
														return model_inputs







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.model(**__a)
														return model_outputs







							def    snake_case__      (   self,			__a,			__a=5):

														'''simple docstring'''

														if top_k > self.model.config.num_labels:
																					_lowerCAmelCase     :							Any    =    self.model.config.num_labels

														if self.framework == "pt":
																					_lowerCAmelCase     :							int    =    model_outputs.logits.softmax(-1)[0]
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    probs.topk(__a)
														elif self.framework == "tf":
																					_lowerCAmelCase     :							Optional[Any]    =    stable_softmax(model_outputs.logits,			axis=-1)[0]
																					_lowerCAmelCase     :							int    =    tf.math.top_k(__a,			k=__a)
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(f"Unsupported framework: {self.framework}")

														_lowerCAmelCase     :							Dict    =    scores.tolist()
														_lowerCAmelCase     :							List[Any]    =    ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__a,			__a)]





def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

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

							return sequence






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

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

							_lowerCAmelCase     :							List[Any]    =    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
							_lowerCAmelCase     :							Optional[int]    =    gray_code_sequence_string(bit_count - 1  )

							_lowerCAmelCase     :							str    =    []

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

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

							return sequence


if __name__ == "__main__":
						import doctest

						doctest.testmod()
