





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

UpperCAmelCase_     							= logging.get_logger(__name__)






@add_end_docstrings(__lowerCamelCase				)
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		*__magic_name__,		**__magic_name__							) ->      Dict:






       """simple docstring"""
       super().__init__(*__magic_name__,		**__magic_name__							)
       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,		__magic_name__=None							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      {}
       if top_k is not None:
           UpperCamelCase__							:				List[Any]          =      top_k
       return {}, {}, postprocess_params




   def __call__(    self,		__magic_name__,		**__magic_name__							) ->      List[str]:






       """simple docstring"""
       return super().__call__(__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      load_image(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      self.image_processor(images=__magic_name__,		return_tensors=self.framework							)
       return model_inputs




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.model(**__magic_name__							)
       return model_outputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=5							) ->      Any:






       """simple docstring"""
       if top_k > self.model.config.num_labels:
           UpperCamelCase__							:				List[str]          =      self.model.config.num_labels

       if self.framework == "pt":
           UpperCamelCase__							:				Union[str, Any]          =      model_outputs.logits.softmax(-1							)[0]
           UpperCamelCase__							:				str          =      probs.topk(__magic_name__							)
       elif self.framework == "tf":
           UpperCamelCase__							:				List[Any]          =      stable_softmax(model_outputs.logits,		axis=-1							)[0]
           UpperCamelCase__							:				Tuple          =      tf.math.top_k(__magic_name__,		k=__magic_name__							)
           UpperCamelCase__							:				str          =      topk.values.numpy(), topk.indices.numpy()
       else:
           raise ValueError(f"Unsupported framework: {self.framework}"							)

       UpperCamelCase__							:				str          =      scores.tolist()
       UpperCamelCase__							:				Optional[int]          =      ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__magic_name__,		__magic_name__							)]







"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()