


'''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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Tuple   						="Salesforce/blip-image-captioning-base"
						_UpperCAmelCase		:       List[str]   						=(
						    "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		:       List[Any]   						="image_captioner"
						_UpperCAmelCase		:       Optional[Any]   						=AutoModelForVisionaSeq

						_UpperCAmelCase		:       Dict   						=["image"]
						_UpperCAmelCase		:       Optional[int]   						=["text"]






						def __init__(  self							:      List[Any] ,  *lowerCAmelCase							:      int ,  **lowerCAmelCase							:      Optional[Any]	):
													requires_backends(self ,  ["vision"]	)
													super().__init__(*lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      "Image"	):
													return self.pre_processor(images=lowerCAmelCase ,  return_tensors="pt"	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Dict	):
													return self.model.generate(**lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Optional[Any]	):
													return self.pre_processor.batch_decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase	)[0].strip()









'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





