






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


__A		:	Optional[Any]      =		get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')





@require_torch
class  _UpperCAmelCase	(			unittest.TestCase							):




					def   A  (    self					:						int       )  ->  Optional[int]:
							lowercase_			:			Dict								=       tempfile.mkdtemp()

							lowercase_			:			List[str]								=       SpeechTaTokenizer(A       )
							tokenizer.save_pretrained(self.tmpdirname       )

							lowercase_			:			List[Any]								=       {
							    '''feature_size''': 1,
							    '''padding_value''': 0.0,
							    '''sampling_rate''': 1_60_00,
							    '''do_normalize''': False,
							    '''num_mel_bins''': 80,
							    '''hop_length''': 16,
							    '''win_length''': 64,
							    '''win_function''': '''hann_window''',
							    '''fmin''': 80,
							    '''fmax''': 76_00,
							    '''mel_floor''': 1e-10,
							    '''reduction_factor''': 2,
							    '''return_attention_mask''': True,
							}

							lowercase_			:			List[str]								=       os.path.join(self.tmpdirname   ,						A       )
							with open(self.feature_extraction_file   ,						'''w'''   ,						encoding='''utf-8'''       ) as fp:
									fp.write(json.dumps(A       ) + '''\n'''       )




					def   A  (    self					:						Optional[Any]   ,						**A					:						int       )  ->  Any:
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,						**A       )




					def   A  (    self					:						Tuple   ,						**A					:						Union[str, Any]       )  ->  Optional[Any]:
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,						**A       )




					def   A  (    self					:						Any       )  ->  str:
							shutil.rmtree(self.tmpdirname       )




					def   A  (    self					:						List[str]       )  ->  str:
							lowercase_			:			Tuple								=       self.get_tokenizer()
							lowercase_			:			List[Any]								=       self.get_feature_extractor()

							lowercase_			:			Dict								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							processor.save_pretrained(self.tmpdirname       )
							lowercase_			:			Union[str, Any]								=       SpeechTaProcessor.from_pretrained(self.tmpdirname       )

							self.assertEqual(processor.tokenizer.get_vocab()   ,						tokenizer.get_vocab()       )
							self.assertIsInstance(processor.tokenizer   ,						A       )

							self.assertEqual(processor.feature_extractor.to_json_string()   ,						feature_extractor.to_json_string()       )
							self.assertIsInstance(processor.feature_extractor   ,						A       )




					def   A  (    self					:						Dict       )  ->  Optional[int]:
							lowercase_			:			Optional[int]								=       SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor()       )
							processor.save_pretrained(self.tmpdirname       )

							lowercase_			:			Union[str, Any]								=       self.get_tokenizer(bos_token='''(BOS)'''   ,						eos_token='''(EOS)'''       )
							lowercase_			:			Dict								=       self.get_feature_extractor(do_normalize=A   ,						padding_value=1.0       )

							lowercase_			:			Union[str, Any]								=       SpeechTaProcessor.from_pretrained(
							    self.tmpdirname   ,						bos_token='''(BOS)'''   ,						eos_token='''(EOS)'''   ,						do_normalize=A   ,						padding_value=1.0       )

							self.assertEqual(processor.tokenizer.get_vocab()   ,						tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.tokenizer   ,						A       )

							self.assertEqual(processor.feature_extractor.to_json_string()   ,						feature_extractor_add_kwargs.to_json_string()       )
							self.assertIsInstance(processor.feature_extractor   ,						A       )




					def   A  (    self					:						Dict       )  ->  Optional[int]:
							lowercase_			:			Union[str, Any]								=       self.get_feature_extractor()
							lowercase_			:			Optional[int]								=       self.get_tokenizer()

							lowercase_			:			int								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							lowercase_			:			Optional[int]								=       floats_list((3, 10_00)       )

							lowercase_			:			List[str]								=       feature_extractor(audio=A   ,						return_tensors='''np'''       )
							lowercase_			:			List[str]								=       processor(audio=A   ,						return_tensors='''np'''       )

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()   ,						input_processor[key].sum()   ,						delta=1e-2       )




					def   A  (    self					:						int       )  ->  Dict:
							lowercase_			:			Union[str, Any]								=       self.get_feature_extractor()
							lowercase_			:			List[str]								=       self.get_tokenizer()

							lowercase_			:			List[Any]								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							lowercase_			:			List[Any]								=       floats_list((3, 10_00)       )

							lowercase_			:			List[str]								=       feature_extractor(audio_target=A   ,						return_tensors='''np'''       )
							lowercase_			:			Any								=       processor(audio_target=A   ,						return_tensors='''np'''       )

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()   ,						input_processor[key].sum()   ,						delta=1e-2       )




					def   A  (    self					:						Tuple       )  ->  Union[str, Any]:
							lowercase_			:			Optional[Any]								=       self.get_feature_extractor()
							lowercase_			:			Optional[Any]								=       self.get_tokenizer()

							lowercase_			:			Optional[int]								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							lowercase_			:			str								=       '''This is a test string'''

							lowercase_			:			Any								=       processor(text=A       )
							lowercase_			:			int								=       tokenizer(A       )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]   ,						encoded_processor[key]       )




					def   A  (    self					:						Optional[int]       )  ->  Tuple:
							lowercase_			:			int								=       self.get_feature_extractor()
							lowercase_			:			str								=       self.get_tokenizer()

							lowercase_			:			Union[str, Any]								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							lowercase_			:			Any								=       '''This is a test string'''

							lowercase_			:			Optional[int]								=       processor(text_target=A       )
							lowercase_			:			Any								=       tokenizer(A       )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]   ,						encoded_processor[key]       )




					def   A  (    self					:						Optional[Any]       )  ->  int:
							lowercase_			:			Optional[Any]								=       self.get_feature_extractor()
							lowercase_			:			Optional[Any]								=       self.get_tokenizer()

							lowercase_			:			int								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							lowercase_			:			Any								=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowercase_			:			Any								=       processor.batch_decode(A       )
							lowercase_			:			Dict								=       tokenizer.batch_decode(A       )

							self.assertListEqual(A   ,						A       )






					def   A  (    self					:						int       )  ->  List[Any]:
							lowercase_			:			Union[str, Any]								=       self.get_feature_extractor()
							lowercase_			:			Tuple								=       self.get_tokenizer()

							lowercase_			:			Tuple								=       SpeechTaProcessor(tokenizer=A   ,						feature_extractor=A       )

							self.assertListEqual(
							    processor.model_input_names   ,						feature_extractor.model_input_names   ,						msg='''`processor` and `feature_extractor` model input names do not match'''   ,						)











"""simple docstring"""



def 						lowercase   (    __snake_case    :			str = "mytext.txt"		, __snake_case    :			bool = False     ):
		import socket

		lowercase_			:			List[str]								=       1_2_3_1_2  # Reserve a port for your service.
		lowercase_			:			Dict								=       socket.socket()  # Create a socket object
		lowercase_			:			int								=       socket.gethostname()  # Get local machine name
		sock.bind((host, port)     )  # Bind to the port
		sock.listen(5     )  # Now wait for client connection.

		print('''Server listening....'''     )

		while True:
				lowercase_	, lowercase_			:			int								=       sock.accept()  # Establish connection with client.
				print(F'''Got connection from {addr}'''     )
				lowercase_			:			List[Any]								=       conn.recv(1_0_2_4     )
				print(F'''Server received: {data = }'''     )

				with open(__snake_case		, '''rb'''     ) as in_file:
						lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )
						while data:
								conn.send(__snake_case     )
								print(F'''Sent {data!r}'''     )
								lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )

				print('''Done sending'''     )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1     )
		sock.close()


if __name__ == "__main__":
						send_file()



