





"""simple docstring"""



from __future__ import annotations







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  list[str] | None = None				,snake_case_  :  dict[str, float] | None = None				,snake_case_  :  bool = False				,)   ->tuple[int, float, str]:



    '''simple docstring'''







    __A    :						Optional[int]						 =     cipher_alphabet or [chr(snake_case_						) for i in range(97				,123						)]

    # If the argument is None or the user provided an empty dictionary
    if not frequencies_dict:
        # Frequencies of letters in the english language (how much they show up)
        __A    :						int						 =     {
            '''a''': 0.08_497,
            '''b''': 0.01_492,
            '''c''': 0.02_202,
            '''d''': 0.04_253,
            '''e''': 0.11_162,
            '''f''': 0.02_228,
            '''g''': 0.02_015,
            '''h''': 0.06_094,
            '''i''': 0.07_546,
            '''j''': 0.00_153,
            '''k''': 0.01_292,
            '''l''': 0.04_025,
            '''m''': 0.02_406,
            '''n''': 0.06_749,
            '''o''': 0.07_507,
            '''p''': 0.01_929,
            '''q''': 0.00_095,
            '''r''': 0.07_587,
            '''s''': 0.06_327,
            '''t''': 0.09_356,
            '''u''': 0.02_758,
            '''v''': 0.00_978,
            '''w''': 0.02_560,
            '''x''': 0.00_150,
            '''y''': 0.01_994,
            '''z''': 0.00_077,
        }
    else:
        # Custom frequencies dictionary
        __A    :						List[Any]						 =     frequencies_dict

    if not case_sensitive:
        __A    :						str						 =     ciphertext.lower()

    # Chi squared statistic values
    __A    :						dict[int, tuple[float, str]]						 =     {}

    # cycle through all of the shifts
    for shift in range(len(snake_case_						)						):
        __A    :						Tuple						 =     ''''''

        # decrypt the message with the shift
        for letter in ciphertext:
            try:
                # Try to index the letter in the alphabet
                __A    :						str						 =     (alphabet_letters.index(letter.lower()						) - shift) % len(
                    snake_case_						)
                decrypted_with_shift += (
                    alphabet_letters[new_key].upper()
                    if case_sensitive and letter.isupper()
                    else alphabet_letters[new_key]
                )
            except ValueError:
                # Append the character if it isn't in the alphabet
                decrypted_with_shift += letter

        __A    :						Optional[Any]						 =     0.0

        # Loop through each letter in the decoded message with the shift
        for letter in decrypted_with_shift:
            if case_sensitive:
                __A    :						Optional[Any]						 =     letter.lower()
                if letter in frequencies:
                    # Get the amount of times the letter occurs in the message
                    __A    :						Optional[Any]						 =     decrypted_with_shift.lower().count(snake_case_						)

                    # Get the excepcted amount of times the letter should appear based
                    # on letter frequencies
                    __A    :						int						 =     frequencies[letter] * occurrences

                    # Complete the chi squared statistic formula
                    __A    :						Union[str, Any]						 =     ((occurrences - expected) ** 2) / expected

                    # Add the margin of error to the total chi squared statistic
                    chi_squared_statistic += chi_letter_value
            else:
                if letter.lower() in frequencies:
                    # Get the amount of times the letter occurs in the message
                    __A    :						Optional[Any]						 =     decrypted_with_shift.count(snake_case_						)

                    # Get the excepcted amount of times the letter should appear based
                    # on letter frequencies
                    __A    :						Optional[int]						 =     frequencies[letter] * occurrences

                    # Complete the chi squared statistic formula
                    __A    :						Any						 =     ((occurrences - expected) ** 2) / expected

                    # Add the margin of error to the total chi squared statistic
                    chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
        __A    :						Optional[int]						 =     (
            chi_squared_statistic,
            decrypted_with_shift,
        )

    # Get the most likely cipher by finding the cipher with the smallest chi squared
    # statistic
    def chi_squared_statistic_values_sorting_key(snake_case_  :  int						) -> tuple[float, str]:
        return chi_squared_statistic_values[key]

    __A    :						int						 =     min(
        snake_case_				,key=snake_case_				,)

    # Get all the data from the most likely cipher (key, decoded message)
    (
        (
        __A
    ) ,       (
        __A
    ) ,       
    )    :						Optional[Any]						 =     chi_squared_statistic_values[most_likely_cipher]

    # Return the data on the most likely shift
    return (
        most_likely_cipher,
        most_likely_cipher_chi_squared_value,
        decoded_most_likely_cipher,
    )











"""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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """facebook/bart-large-mnli"""
 _lowerCamelCase        =     (
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 _lowerCamelCase        =     """text_classifier"""
 _lowerCamelCase        =     AutoTokenizer
 _lowerCamelCase        =     AutoModelForSequenceClassification

 _lowerCamelCase        =     ["""text""", ["""text"""]]
 _lowerCamelCase        =     ["""text"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[str]						 =     self.model.config
     __A    :						int						 =     -1
     for idx, label in config.idalabel.items():
         if label.lower().startswith('''entail'''  ):
             __A    :						List[str]						 =     int(__lowerCamelCase  )
     if self.entailment_id == -1:
         raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     labels
     return self.pre_processor(
         [text] * len(__lowerCamelCase  )					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='''pt'''					,						padding='''max_length'''					,						)







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     outputs.logits
     __A    :						List[str]						 =     torch.argmax(logits[:, 2]  ).item()
     return self._labels[label_id]




