

# 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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				'''microsoft/speecht5_tts'''
							_UpperCAmelCase   :  Optional[Any]										=				(
							    '''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.'''
							)
							_UpperCAmelCase   :  int										=				'''text_reader'''
							_UpperCAmelCase   :  Optional[int]										=				SpeechTaProcessor
							_UpperCAmelCase   :  int										=				SpeechTaForTextToSpeech
							_UpperCAmelCase   :  Optional[int]										=				SpeechTaHifiGan

							_UpperCAmelCase   :  Dict										=				['''text''']
							_UpperCAmelCase   :  Optional[Any]										=				['''audio''']
							def    lowerCAmelCase					(		self		:      Any):
								if self.post_processor is None:
									__lowerCamelCase    :    int 	=			'microsoft/speecht5_hifigan'
								super().setup()
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str=None):
								__lowerCamelCase    :    int 	=			self.pre_processor(text=SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,truncation=SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase    :    int 	=			load_dataset('Matthijs/cmu-arctic-xvectors'       ,split='validation')
									__lowerCamelCase    :    List[Any] 	=			torch.tensor(embeddings_dataset[7_3_0_5]['xvector']).unsqueeze(0)

								return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict):
								with torch.no_grad():
									return self.model.generate_speech(**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								with torch.no_grad():
									return self.post_processor(SCREAMING_SNAKE_CASE__).cpu().detach()







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a		=3






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	print('Generating primitive root of p'      )
	while True:
		__lowerCamelCase    :    Tuple 	=			random.randrange(3     ,			lowerCamelCase__      )
		if pow(lowerCamelCase__     ,			2     ,			lowerCamelCase__      ) == 1:
			continue
		if pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      ) == 1:
			continue
		return g






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					tuple[tuple[int, int, int, int], tuple[int, int]]:
	print('Generating prime p...'      )
	__lowerCamelCase    :    List[str] 	=			rabin_miller.generate_large_prime(lowerCamelCase__      )  # select large prime number.
	__lowerCamelCase    :    Dict 	=			primitive_root(lowerCamelCase__      )  # one primitive root on modulo p.
	__lowerCamelCase    :    Optional[int] 	=			random.randrange(3     ,			lowerCamelCase__      )  # private_key -> have to be greater than 2 for safety.
	__lowerCamelCase    :    List[Any] 	=			cryptomath.find_mod_inverse(pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )     ,			lowerCamelCase__      )

	__lowerCamelCase    :    int 	=			(key_size, e_a, e_a, p)
	__lowerCamelCase    :    str 	=			(key_size, d)

	return public_key, private_key






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	if os.path.exists(F"{name}_pubkey.txt"      ) or os.path.exists(F"{name}_privkey.txt"      ):
		print('\nWARNING:'      )
		print(
		    F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
		    'Use a different name or delete these files and re-run this program.'      )
		sys.exit()

	__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			generate_key(lowerCamelCase__      )
	print(F"\nWriting public key to file {name}_pubkey.txt..."      )
	with open(F"{name}_pubkey.txt"     ,			'w'      ) as fo:
		fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"      )

	print(F"Writing private key to file {name}_privkey.txt..."      )
	with open(F"{name}_privkey.txt"     ,			'w'      ) as fo:
		fo.write(F"{private_key[0]},{private_key[1]}"      )






def        SCREAMING_SNAKE_CASE__	(  )  ->					None:
	print('Making key files...'      )
	make_key_files('elgamal'     ,			2_0_4_8      )
	print('Key files generation successful'      )


if __name__ == "__main__":
					main()




