






'''simple docstring'''


from __future__ import annotations







def 	__magic_name__( _A						,      _A = None						,      _A = None						,      _A = False						,      ):
						'''simple docstring'''





						UpperCamelCase__         = cipher_alphabet or [chr(_A					) 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)
												UpperCamelCase__         = {
												    """a""": 0.0_8_4_9_7,
												    """b""": 0.0_1_4_9_2,
												    """c""": 0.0_2_2_0_2,
												    """d""": 0.0_4_2_5_3,
												    """e""": 0.1_1_1_6_2,
												    """f""": 0.0_2_2_2_8,
												    """g""": 0.0_2_0_1_5,
												    """h""": 0.0_6_0_9_4,
												    """i""": 0.0_7_5_4_6,
												    """j""": 0.0_0_1_5_3,
												    """k""": 0.0_1_2_9_2,
												    """l""": 0.0_4_0_2_5,
												    """m""": 0.0_2_4_0_6,
												    """n""": 0.0_6_7_4_9,
												    """o""": 0.0_7_5_0_7,
												    """p""": 0.0_1_9_2_9,
												    """q""": 0.0_0_0_9_5,
												    """r""": 0.0_7_5_8_7,
												    """s""": 0.0_6_3_2_7,
												    """t""": 0.0_9_3_5_6,
												    """u""": 0.0_2_7_5_8,
												    """v""": 0.0_0_9_7_8,
												    """w""": 0.0_2_5_6_0,
												    """x""": 0.0_0_1_5_0,
												    """y""": 0.0_1_9_9_4,
												    """z""": 0.0_0_0_7_7,
												}
						else:
												# Custom frequencies dictionary
												UpperCamelCase__         = frequencies_dict

						if not case_sensitive:
												UpperCamelCase__         = ciphertext.lower()

						# Chi squared statistic values
						UpperCamelCase__         = {}

						# cycle through all of the shifts
						for shift in range(len(_A					)					):
												UpperCamelCase__         = """"""

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								UpperCamelCase__         = (alphabet_letters.index(letter.lower()					) - shift) % len(
																								    _A					)
																								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

												UpperCamelCase__         = 0.0

												# Loop through each letter in the decoded message with the shift
												for letter in decrypted_with_shift:
																		if case_sensitive:
																								UpperCamelCase__         = letter.lower()
																								if letter in frequencies:
																														# Get the amount of times the letter occurs in the message
																														UpperCamelCase__         = decrypted_with_shift.lower().count(_A					)

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

																														# Complete the chi squared statistic formula
																														UpperCamelCase__         = ((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
																														UpperCamelCase__         = decrypted_with_shift.count(_A					)

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

																														# Complete the chi squared statistic formula
																														UpperCamelCase__         = ((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
												UpperCamelCase__         = (
												    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(_A					) -> tuple[float, str]:
												return chi_squared_statistic_values[key]

						UpperCamelCase__         = min(
						    _A						,      key=_A						,      )

						# Get all the data from the most likely cipher (key, decoded message)
						(
						    (
						    UpperCamelCase__   
						)    ,   (
						    UpperCamelCase__   
						)    ,      
						)         = 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.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					"microsoft/speecht5_tts"
			__a							:							int					     =					(
			    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
			    "text to read (in English) and returns a waveform object containing the sound."
			)
			__a							:							Optional[Any]					     =					"text_reader"
			__a							:							Tuple					     =					SpeechTaProcessor
			__a							:							int					     =					SpeechTaForTextToSpeech
			__a							:							int					     =					SpeechTaHifiGan

			__a							:							Optional[int]					     =					["text"]
			__a							:							Union[str, Any]					     =					["audio"]






			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if self.post_processor is None:
															UpperCamelCase__         = """microsoft/speecht5_hifigan"""
									super().setup()






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any]=None			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.pre_processor(text=lowercase ,      return_tensors="""pt""" ,      truncation=lowercase			)

									if speaker_embeddings is None:
															if not is_datasets_available():
																					raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""			)

															UpperCamelCase__         = load_dataset("""Matthijs/cmu-arctic-xvectors""" ,      split="""validation"""			)
															UpperCamelCase__         = torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]			).unsqueeze(0			)

									return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







									with torch.no_grad():
															return self.model.generate_speech(**lowercase			)







			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									with torch.no_grad():
															return self.post_processor(lowercase			).cpu().detach()
