



import numpy

# List of input, output pairs
_lowercase						  =		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_lowercase						  =		(((515, 22, 13), 555), ((61, 35, 49), 150))
_lowercase						  =		[2, 4, 1, 5]
_lowercase						  =		len(train_data)
_lowercase						  =		0.009



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any ,       UpperCamelCase_				:       int="train"	)->  int:
    return calculate_hypothesis_value(UpperCamelCase_ ,       UpperCamelCase_	) - output(
        UpperCamelCase_ ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any]	)->  int:
    A__		 =					0
    for i in range(len(UpperCamelCase_	) - 1	):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  Tuple:
    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       int	)->  Tuple:
    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]	)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]	)
    return None



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       List[Any]=m	)->  List[Any]:
    A__		 =					0
    for i in range(UpperCamelCase_	):
        if index == -1:
            summation_value += _error(UpperCamelCase_	)
        else:
            summation_value += _error(UpperCamelCase_	) * train_data[i][0][index]
    return summation_value



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any]	)->  Optional[Any]:
    A__		 =					summation_of_cost_derivative(UpperCamelCase_ ,       UpperCamelCase_	) / m
    return cost_derivative_value



def 		lowerCAmelCase__    (					)->  Dict:
    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A__		 =					0.000002
    A__		 =					0
    A__		 =					0
    while True:
        j += 1
        A__		 =					[0, 0, 0, 0]
        for i in range(0 ,       len(UpperCamelCase_	)	):
            A__		 =					get_cost_derivative(i - 1	)
            A__		 =					(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            UpperCamelCase_ ,       UpperCamelCase_ ,       atol=UpperCamelCase_ ,       rtol=UpperCamelCase_ ,       ):
            break
        A__		 =					temp_parameter_vector
    print(('''Number of iterations:''', j)	)



def 		lowerCAmelCase__    (					)->  Tuple:
    for i in range(len(UpperCamelCase_	)	):
        print(('''Actual output value:''', output(UpperCamelCase_ ,       '''test'''	))	)
        print(('''Hypothesis output:''', calculate_hypothesis_value(UpperCamelCase_ ,       '''test'''	))	)


if __name__ == "__main__":
   run_gradient_descent()
   print("\nTesting gradient descent for a linear hypothesis function.\n")
   test_gradient_descent()





# 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__     ):
     UpperCamelCase__		    =				'''Salesforce/blip-image-captioning-base'''
     UpperCamelCase__		    =				(
         '''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.'''
     )
     UpperCamelCase__		    =				'''image_captioner'''
     UpperCamelCase__		    =				AutoModelForVisionaSeq

     UpperCamelCase__		    =				['''image''']
     UpperCamelCase__		    =				['''text''']




     def __init__(				self ,						*a__ ,						**a__):
         requires_backends(self ,						['''vision'''])
         super().__init__(*a__ ,						**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor(images=a__ ,						return_tensors='''pt''')




     def 		snake_case_		(				self ,						a__):
         return self.model.generate(**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor.batch_decode(a__ ,						skip_special_tokens=a__)[0].strip()
