
'''simple docstring'''






from collections.abc import Generator
from math import sin





def    UpperCAmelCase							(  UpperCAmelCase__							:							bytes):
				if len(UpperCAmelCase__) != 32:
								raise ValueError('Input must be of length 32')

				lowerCamelCase					:			Optional[int]									= b''
				for i in [3, 2, 1, 0]:
								little_endian += string_aa[8 * i : 8 * i + 8]
				return little_endian





def    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				if i < 0:
								raise ValueError('Input must be non-negative')

				lowerCamelCase					:			Tuple									= format(UpperCAmelCase__       ,							'08x')[-8:]
				lowerCamelCase					:			Optional[Any]									= b''
				for i in [3, 2, 1, 0]:
								little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8')
				return little_endian_hex





def    UpperCAmelCase							(  UpperCAmelCase__							:							bytes):
				lowerCamelCase					:			Dict									= b''
				for char in message:
								bit_string += format(UpperCAmelCase__       ,							'08b').encode('utf-8')
				lowerCamelCase					:			str									= format(len(UpperCAmelCase__)       ,							'064b').encode('utf-8')

				# Pad bit_string to a multiple of 512 chars
				bit_string += b"1"
				while len(UpperCAmelCase__) % 5_12 != 4_48:
								bit_string += b"0"
				bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])

				return bit_string





def    UpperCAmelCase							(  UpperCAmelCase__							:							bytes):
				if len(UpperCAmelCase__) % 5_12 != 0:
								raise ValueError('Input must have length that\'s a multiple of 512')

				for pos in range(0       ,							len(UpperCAmelCase__)       ,							5_12):
								lowerCamelCase					:			Tuple									= bit_string[pos : pos + 5_12]
								lowerCamelCase					:			Union[str, Any]									= []
								for i in range(0       ,							5_12       ,							32):
												block_words.append(int(to_little_endian(block[i : i + 32])       ,							2))
								yield block_words





def    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				if i < 0:
								raise ValueError('Input must be non-negative')

				lowerCamelCase					:			List[Any]									= format(UpperCAmelCase__       ,							'032b')
				lowerCamelCase					:			Dict									= ''
				for c in i_str:
								new_str += "1" if c == "0" else "0"
				return int(UpperCAmelCase__       ,							2)





def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							int):
				return (a + b) % 2**32





def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							int):
				if i < 0:
								raise ValueError('Input must be non-negative')
				if shift < 0:
								raise ValueError('Shift must be non-negative')
				return ((i << shift) ^ (i >> (32 - shift))) % 2**32





def    UpperCAmelCase							(  UpperCAmelCase__							:							bytes):
				lowerCamelCase					:			List[Any]									= preprocess(UpperCAmelCase__)

				lowerCamelCase					:			Tuple									= [int(2**32 * abs(sin(i + 1))) for i in range(64)]

				# Starting states
				lowerCamelCase					:			Optional[int]									= 0x6_7_4_5_2_3_0_1
				lowerCamelCase					:			List[Any]									= 0xE_F_C_D_A_B_8_9
				lowerCamelCase					:			int									= 0x9_8_B_A_D_C_F_E
				lowerCamelCase					:			Any									= 0x1_0_3_2_5_4_7_6

				lowerCamelCase					:			List[str]									= [
				    7,
				    12,
				    17,
				    22,
				    7,
				    12,
				    17,
				    22,
				    7,
				    12,
				    17,
				    22,
				    7,
				    12,
				    17,
				    22,
				    5,
				    9,
				    14,
				    20,
				    5,
				    9,
				    14,
				    20,
				    5,
				    9,
				    14,
				    20,
				    5,
				    9,
				    14,
				    20,
				    4,
				    11,
				    16,
				    23,
				    4,
				    11,
				    16,
				    23,
				    4,
				    11,
				    16,
				    23,
				    4,
				    11,
				    16,
				    23,
				    6,
				    10,
				    15,
				    21,
				    6,
				    10,
				    15,
				    21,
				    6,
				    10,
				    15,
				    21,
				    6,
				    10,
				    15,
				    21,
				]

				# Process bit string in chunks, each with 16 32-char words
				for block_words in get_block_words(UpperCAmelCase__):
								lowerCamelCase					:			Dict									= aa
								lowerCamelCase					:			Optional[Any]									= ba
								lowerCamelCase					:			Any									= ca
								lowerCamelCase					:			Union[str, Any]									= da

								# Hash current chunk
								for i in range(64):
												if i <= 15:
																# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																lowerCamelCase					:			int									= d ^ (b & (c ^ d))
																lowerCamelCase					:			str									= i
												elif i <= 31:
																# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																lowerCamelCase					:			Dict									= c ^ (d & (b ^ c))
																lowerCamelCase					:			List[str]									= (5 * i + 1) % 16
												elif i <= 47:
																lowerCamelCase					:			Optional[Any]									= b ^ c ^ d
																lowerCamelCase					:			List[Any]									= (3 * i + 5) % 16
												else:
																lowerCamelCase					:			Tuple									= c ^ (b | not_aa(UpperCAmelCase__))
																lowerCamelCase					:			Any									= (7 * i) % 16
												lowerCamelCase					:			List[Any]									= (f + a + added_consts[i] + block_words[g]) % 2**32
												lowerCamelCase					:			Tuple									= d
												lowerCamelCase					:			int									= c
												lowerCamelCase					:			Union[str, Any]									= b
												lowerCamelCase					:			List[Any]									= sum_aa(UpperCAmelCase__       ,							left_rotate_aa(UpperCAmelCase__       ,							shift_amounts[i]))

								# Add hashed chunk to running total
								lowerCamelCase					:			Dict									= sum_aa(UpperCAmelCase__       ,							UpperCAmelCase__)
								lowerCamelCase					:			List[str]									= sum_aa(UpperCAmelCase__       ,							UpperCAmelCase__)
								lowerCamelCase					:			Tuple									= sum_aa(UpperCAmelCase__       ,							UpperCAmelCase__)
								lowerCamelCase					:			Any									= sum_aa(UpperCAmelCase__       ,							UpperCAmelCase__)

				lowerCamelCase					:			Tuple									= reformat_hex(UpperCAmelCase__) + reformat_hex(UpperCAmelCase__) + reformat_hex(UpperCAmelCase__) + reformat_hex(UpperCAmelCase__)
				return digest


if __name__ == "__main__":
					import doctest

					doctest.testmod()


'''simple docstring'''






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


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''AutoTokenizer'''
					_lowerCAmelCase			     =	['''tokenizer''']

					_lowerCAmelCase			     =	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}
					def __init__(     self,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Union[str, Any]									= speaker_embeddings
					@classmethod
					def     UpperCAmelCase_							(     cls,    A,    A="speaker_embeddings_path.json",    **A      ):

									"""simple docstring"""


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

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

									return cls(tokenizer=A,    speaker_embeddings=A      )
					def     UpperCAmelCase_							(     self,    A,    A="speaker_embeddings_path.json",    A="speaker_embeddings",    A = False,    **A,    ):

									"""simple docstring"""


									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(A,    A,    'v2'      ),    exist_ok=A      )

													lowerCamelCase					:			Any									= {}

													lowerCamelCase					:			Tuple									= save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowerCamelCase					:			Optional[int]									= self._load_voice_preset(A      )

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

																					lowerCamelCase					:			Optional[Any]									= tmp_dict

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

									super().save_pretrained(A,    A,    **A      )
					def     UpperCAmelCase_							(     self,    A = None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.speaker_embeddings[voice_preset]

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

													lowerCamelCase					:			Tuple									= np.load(A      )

									return voice_preset_dict
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""simple docstring"""


									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,    A=None,    A=None,    A="pt",    A=256,    A=False,    A=True,    A=False,    **A,    ):

									"""simple docstring"""


									if voice_preset is not None and not isinstance(A,    A      ):
													if (
													    isinstance(A,    A      )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	lowerCamelCase					:			Dict									= self._load_voice_preset(A      )

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

																	lowerCamelCase					:			Tuple									= np.load(A      )

									if voice_preset is not None:
													self._validate_voice_preset_dict(A,    **A      )
													lowerCamelCase					:			List[Any]									= BatchFeature(data=A,    tensor_type=A      )

									lowerCamelCase					:			str									= self.tokenizer(
									    A,    return_tensors=A,    padding='max_length',    max_length=A,    return_attention_mask=A,    return_token_type_ids=A,    add_special_tokens=A,    **A,    )

									if voice_preset is not None:
													lowerCamelCase					:			Dict									= voice_preset

									return encoded_text
