






import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


snake_case_							=			logging.get_logger(__name__)
class        snake_case_		(			_A):
		lowerCamelCase    :List[Any]						=     "AutoTokenizer"
		lowerCamelCase    :List[str]						=     ["tokenizer"]

		lowerCamelCase    :Any						=     {
		    "semantic_prompt": 1,
		    "coarse_prompt": 2,
		    "fine_prompt": 2,
		}







		def __init__(		self			,     __lowercase			,     __lowercase=None   )				->   Dict:
				super().__init__(__lowercase   )

				lowerCamelCase   : Optional[Any]								=speaker_embeddings







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase			,     __lowercase="speaker_embeddings_path.json"			,     **__lowercase   )				->   Union[str, Any]:

				if speaker_embeddings_dict_path is not None:
						lowerCamelCase   : str								=get_file_from_repo(
						    __lowercase			,     __lowercase			,     subfolder=kwargs.pop('''subfolder'''			,     __lowercase   )			,     cache_dir=kwargs.pop('''cache_dir'''			,     __lowercase   )			,     force_download=kwargs.pop('''force_download'''			,     __lowercase   )			,     proxies=kwargs.pop('''proxies'''			,     __lowercase   )			,     resume_download=kwargs.pop('''resume_download'''			,     __lowercase   )			,     local_files_only=kwargs.pop('''local_files_only'''			,     __lowercase   )			,     use_auth_token=kwargs.pop('''use_auth_token'''			,     __lowercase   )			,     revision=kwargs.pop('''revision'''			,     __lowercase   )			,     )
						if speaker_embeddings_path is None:
								logger.warning(
								    F"`{os.path.join(__lowercase			,     __lowercase   )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."   )
								lowerCamelCase   : Tuple								=None
						else:
								with open(__lowercase   ) as speaker_embeddings_json:
										lowerCamelCase   : Optional[int]								=json.load(__lowercase   )
				else:
						lowerCamelCase   : Optional[Any]								=None

				lowerCamelCase   : int								=AutoTokenizer.from_pretrained(__lowercase			,     **__lowercase   )

				return cls(tokenizer=__lowercase			,     speaker_embeddings=__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase="speaker_embeddings_path.json"			,     __lowercase="speaker_embeddings"			,     __lowercase = False			,     **__lowercase			,     )				->   List[Any]:
				if self.speaker_embeddings is not None:
						os.makedirs(os.path.join(__lowercase			,     __lowercase			,     '''v2'''   )			,     exist_ok=__lowercase   )

						lowerCamelCase   : List[str]								={}

						lowerCamelCase   : List[Any]								=save_directory

						for prompt_key in self.speaker_embeddings:
								if prompt_key != "repo_or_path":
										lowerCamelCase   : str								=self._load_voice_preset(__lowercase   )

										lowerCamelCase   : str								={}
										for key in self.speaker_embeddings[prompt_key]:
												np.save(
												    os.path.join(
												        embeddings_dict['''repo_or_path''']			,     __lowercase			,     F"{prompt_key}_{key}"   )			,     voice_preset[key]			,     allow_pickle=__lowercase			,     )
												lowerCamelCase   : Tuple								=os.path.join(__lowercase			,     F"{prompt_key}_{key}.npy"   )

										lowerCamelCase   : Dict								=tmp_dict

						with open(os.path.join(__lowercase			,     __lowercase   )			,     '''w'''   ) as fp:
								json.dump(__lowercase			,     __lowercase   )

				super().save_pretrained(__lowercase			,     __lowercase			,     **__lowercase   )







		def 			__lowercase   (		self			,     __lowercase = None			,     **__lowercase   )				->   Union[str, Any]:
				lowerCamelCase   : str								=self.speaker_embeddings[voice_preset]

				lowerCamelCase   : Dict								={}
				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
						if key not in voice_preset_paths:
								raise ValueError(
								    F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."   )

						lowerCamelCase   : str								=get_file_from_repo(
						    self.speaker_embeddings.get('''repo_or_path'''			,     '''/'''   )			,     voice_preset_paths[key]			,     subfolder=kwargs.pop('''subfolder'''			,     __lowercase   )			,     cache_dir=kwargs.pop('''cache_dir'''			,     __lowercase   )			,     force_download=kwargs.pop('''force_download'''			,     __lowercase   )			,     proxies=kwargs.pop('''proxies'''			,     __lowercase   )			,     resume_download=kwargs.pop('''resume_download'''			,     __lowercase   )			,     local_files_only=kwargs.pop('''local_files_only'''			,     __lowercase   )			,     use_auth_token=kwargs.pop('''use_auth_token'''			,     __lowercase   )			,     revision=kwargs.pop('''revision'''			,     __lowercase   )			,     )
						if path is None:
								raise ValueError(
								    F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'			,     '/'   )			,     voice_preset_paths[key]   )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."   )

						lowerCamelCase   : Tuple								=np.load(__lowercase   )

				return voice_preset_dict







		def 			__lowercase   (		self			,     __lowercase = None   )				->   Optional[int]:
				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
						if key not in voice_preset:
								raise ValueError(F"Voice preset unrecognized, missing {key} as a key."   )

						if not isinstance(voice_preset[key]			,     np.ndarray   ):
								raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray."   )

						if len(voice_preset[key].shape   ) != self.preset_shape[key]:
								raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]   )}D ndarray."   )







		def __call__(		self			,     __lowercase=None			,     __lowercase=None			,     __lowercase="pt"			,     __lowercase=2_5_6			,     __lowercase=False			,     __lowercase=True			,     __lowercase=False			,     **__lowercase			,     )				->   Any:
				if voice_preset is not None and not isinstance(__lowercase			,     __lowercase   ):
						if (
						    isinstance(__lowercase			,     __lowercase   )
						    and self.speaker_embeddings is not None
						    and voice_preset in self.speaker_embeddings
						):
								lowerCamelCase   : Union[str, Any]								=self._load_voice_preset(__lowercase   )

						else:
								if isinstance(__lowercase			,     __lowercase   ) and not voice_preset.endswith('''.npz'''   ):
										lowerCamelCase   : Union[str, Any]								=voice_preset + '''.npz'''

								lowerCamelCase   : List[str]								=np.load(__lowercase   )

				if voice_preset is not None:
						self._validate_voice_preset_dict(__lowercase			,     **__lowercase   )
						lowerCamelCase   : Tuple								=BatchFeature(data=__lowercase			,     tensor_type=__lowercase   )

				lowerCamelCase   : Union[str, Any]								=self.tokenizer(
				    __lowercase			,     return_tensors=__lowercase			,     padding='''max_length'''			,     max_length=__lowercase			,     return_attention_mask=__lowercase			,     return_token_type_ids=__lowercase			,     add_special_tokens=__lowercase			,     **__lowercase			,     )

				if voice_preset is not None:
						lowerCamelCase   : str								=voice_preset

				return encoded_text








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = False							,       )   ->     tuple[int, float, str]:
		lowerCamelCase   : Union[str, Any]								=cipher_alphabet or [chr(SCREAMING_SNAKE_CASE_		) for i in range(9_7							,       1_2_3		)]

		# 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)
				lowerCamelCase   : str								={
				    '''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
				lowerCamelCase   : Tuple								=frequencies_dict

		if not case_sensitive:
				lowerCamelCase   : Any								=ciphertext.lower()

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

		# cycle through all of the shifts
		for shift in range(len(SCREAMING_SNAKE_CASE_		)		):
				lowerCamelCase   : int								=''''''

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

				lowerCamelCase   : str								=0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								lowerCamelCase   : Union[str, Any]								=letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										lowerCamelCase   : List[str]								=decrypted_with_shift.lower().count(SCREAMING_SNAKE_CASE_		)

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

										# Complete the chi squared statistic formula
										lowerCamelCase   : Tuple								=((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
										lowerCamelCase   : Optional[int]								=decrypted_with_shift.count(SCREAMING_SNAKE_CASE_		)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										lowerCamelCase   : Union[str, Any]								=frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										lowerCamelCase   : int								=((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
				lowerCamelCase   : Dict								=(
				    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(SCREAMING_SNAKE_CASE_		) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		lowerCamelCase   : int								=min(
		    SCREAMING_SNAKE_CASE_							,       key=SCREAMING_SNAKE_CASE_							,       )

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    lowerCamelCase
		)		,   (
		    lowerCamelCase
		)		,   
		)   : 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,
		)

