






"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
			from PIL import Image







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''Salesforce/blip-image-captioning-base'''
	__magic_name__       =							(
	    '''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
	    '''image to caption, and returns a text that contains the description in English.'''
	)
	__magic_name__       =							'''image_captioner'''
	__magic_name__       =							AutoModelForVisionaSeq

	__magic_name__       =							['''image''']
	__magic_name__       =							['''text''']





	def __init__( self   :					str      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Tuple		)		->     Optional[Any]:
				requires_backends(self      ,      ["vision"]		)
				super().__init__(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					"Image"		)		->     str:
				return self.pre_processor(images=lowerCAmelCase_      ,      return_tensors="pt"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]		)		->     List[str]:
				return self.model.generate(**lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					int		)		->     str:
				return self.pre_processor.batch_decode(lowerCAmelCase_      ,      skip_special_tokens=lowerCAmelCase_		)[0].strip()







'''simple docstring'''






from __future__ import annotations

import numpy as np







def 							UpperCamelCase_     (       A__   :  np.ndarray   ):
  '''simple docstring'''





  lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  np.shape(A__   )
  if rows != columns:
    lowerCAmelCase_     : List[str]     =  (
        """'table' has to be of square shaped array but got a """
        f'{rows}x{columns} array:\n{table}'
    )
    raise ValueError(A__   )

  lowerCAmelCase_     : Optional[int]     =  np.zeros((rows, columns)   )
  lowerCAmelCase_     : Tuple     =  np.zeros((rows, columns)   )
  for i in range(A__   ):
    for j in range(A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      if upper[j][j] == 0:
        raise ArithmeticError("""No LU decomposition exists"""   )
      lowerCAmelCase_     : Union[str, Any]     =  (table[i][j] - total) / upper[j][j]
    lowerCAmelCase_     : Dict     =  1
    for j in range(A__				,       A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      lowerCAmelCase_     : List[str]     =  table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()



