
"""simple docstring"""



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					bytes     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			f"""{sampling_rate}"""
							_lowerCAmelCase  	=			'1'
							_lowerCAmelCase  	=			'f32le'
							_lowerCAmelCase  	=			[
							    'ffmpeg',
							    '-i',
							    'pipe:0',
							    '-ac',
							    ac,
							    '-ar',
							    ar,
							    '-f',
							    format_for_conversion,
							    '-hide_banner',
							    '-loglevel',
							    'quiet',
							    'pipe:1',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,  stdin=subprocess.PIPE     ,  stdout=subprocess.PIPE						) as ffmpeg_process:
																					_lowerCAmelCase  	=			ffmpeg_process.communicate(SCREAMING_SNAKE_CASE						)
							except FileNotFoundError as error:
														raise ValueError('ffmpeg was not found but is required to load audio files from filename'						) from error
							_lowerCAmelCase  	=			output_stream[0]
							_lowerCAmelCase  	=			np.frombuffer(SCREAMING_SNAKE_CASE     ,  np.floataa						)
							if audio.shape[0] == 0:
														raise ValueError('Malformed soundfile'						)
							return audio






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					float     ,  SCREAMING_SNAKE_CASE:					str = "f32le"     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			f"""{sampling_rate}"""
							_lowerCAmelCase  	=			'1'
							if format_for_conversion == "s16le":
														_lowerCAmelCase  	=			2
							elif format_for_conversion == "f32le":
														_lowerCAmelCase  	=			4
							else:
														raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""						)

							_lowerCAmelCase  	=			platform.system()
							if system == "Linux":
														_lowerCAmelCase  	=			'alsa'
														_lowerCAmelCase  	=			'default'
							elif system == "Darwin":
														_lowerCAmelCase  	=			'avfoundation'
														_lowerCAmelCase  	=			':0'
							elif system == "Windows":
														_lowerCAmelCase  	=			'dshow'
														_lowerCAmelCase  	=			'default'

							_lowerCAmelCase  	=			[
							    'ffmpeg',
							    '-f',
							    format_,
							    '-i',
							    input_,
							    '-ac',
							    ac,
							    '-ar',
							    ar,
							    '-f',
							    format_for_conversion,
							    '-fflags',
							    'nobuffer',
							    '-hide_banner',
							    '-loglevel',
							    'quiet',
							    'pipe:1',
							]
							_lowerCAmelCase  	=			int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
							_lowerCAmelCase  	=			_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							for item in iterator:
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					float     ,  SCREAMING_SNAKE_CASE:					Optional[int] = None     ,  SCREAMING_SNAKE_CASE:					Optional[Union[Tuple[float, float], float]] = None     ,  SCREAMING_SNAKE_CASE:					str = "f32le"     ,  ):






							"""simple docstring"""



							if stream_chunk_s is not None:
														_lowerCAmelCase  	=			stream_chunk_s
							else:
														_lowerCAmelCase  	=			chunk_length_s

							_lowerCAmelCase  	=			ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  format_for_conversion=SCREAMING_SNAKE_CASE						)
							if format_for_conversion == "s16le":
														_lowerCAmelCase  	=			np.intaa
														_lowerCAmelCase  	=			2
							elif format_for_conversion == "f32le":
														_lowerCAmelCase  	=			np.floataa
														_lowerCAmelCase  	=			4
							else:
														raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""						)

							if stride_length_s is None:
														_lowerCAmelCase  	=			chunk_length_s / 6
							_lowerCAmelCase  	=			int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,  (int, float)						):
														_lowerCAmelCase  	=			[stride_length_s, stride_length_s]

							_lowerCAmelCase  	=			int(round(sampling_rate * stride_length_s[0]						)						) * size_of_sample
							_lowerCAmelCase  	=			int(round(sampling_rate * stride_length_s[1]						)						) * size_of_sample
							_lowerCAmelCase  	=			datetime.datetime.now()
							_lowerCAmelCase  	=			datetime.timedelta(seconds=SCREAMING_SNAKE_CASE						)
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  stride=(stride_left, stride_right)     ,  stream=SCREAMING_SNAKE_CASE						):
														# Put everything back in numpy scale
														_lowerCAmelCase  	=			np.frombuffer(item['raw']     ,  dtype=SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			(
														    item['stride'][0] // size_of_sample,
														    item['stride'][1] // size_of_sample,
														)
														_lowerCAmelCase  	=			sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Tuple[int, int]     ,  SCREAMING_SNAKE_CASE:					bool = False						):






							"""simple docstring"""



							_lowerCAmelCase  	=			B''
							_lowerCAmelCase	,		_lowerCAmelCase  	=			stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""						)
							_lowerCAmelCase  	=			0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE						) < chunk_len:
																					_lowerCAmelCase  	=			(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE						) >= chunk_len:
																												# We are flushing the accumulator
																												_lowerCAmelCase  	=			(_stride_left, stride_right)
																												_lowerCAmelCase  	=			{'raw': acc[:chunk_len], 'stride': stride}
																												if stream:
																																			_lowerCAmelCase  	=			False
																												yield item
																												_lowerCAmelCase  	=			stride_left
																												_lowerCAmelCase  	=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE						) > stride_left:
														_lowerCAmelCase  	=			{'raw': acc, 'stride': (_stride_left, 0)}
														if stream:
																					_lowerCAmelCase  	=			False
														yield item






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,  stdout=subprocess.PIPE     ,  bufsize=SCREAMING_SNAKE_CASE						) as ffmpeg_process:
																					while True:
																												_lowerCAmelCase  	=			ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE						)
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('ffmpeg was not found but is required to stream audio files from filename'						) from error








"""simple docstring"""



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					None
				SCREAMING_SNAKE_CASE_:							List[str]							=					BloomTokenizerFast
				SCREAMING_SNAKE_CASE_:							List[str]							=					BloomTokenizerFast
				SCREAMING_SNAKE_CASE_:							Any							=					True
				SCREAMING_SNAKE_CASE_:							List[Any]							=					False
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					"tokenizer_file"
				SCREAMING_SNAKE_CASE_:							str							=					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  str:







											"""simple docstring"""




											super().setUp()
											_lowerCAmelCase  	=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'  )
											tokenizer.save_pretrained(self.tmpdirname  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     **UpperCAmelCase_	:			Any  )		->  Optional[Any]:







											"""simple docstring"""




											kwargs.update(self.special_tokens_map  )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_rust_tokenizer()

											_lowerCAmelCase  	=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											_lowerCAmelCase  	=			[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

											_lowerCAmelCase  	=			tokenizer.batch_encode_plus(UpperCAmelCase_  )['input_ids']
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			tokenizer.batch_decode(UpperCAmelCase_  )
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			List[str]=6  )		->  Any:







											"""simple docstring"""




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																									_lowerCAmelCase  	=			self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_  )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									_lowerCAmelCase  	=			'This is a simple input'
																									_lowerCAmelCase  	=			['This is a simple input 1', 'This is a simple input 2']
																									_lowerCAmelCase  	=			('This is a simple input', 'This is a pair')
																									_lowerCAmelCase  	=			[
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )

																																tokenizer_r.batch_encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.encode(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																																tokenizer_r.batch_encode_plus(UpperCAmelCase_				,     max_length=UpperCAmelCase_  )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'  )

																									_lowerCAmelCase  	=			None  # Hotfixing padding = None
																									self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'  )

																									# Simple input
																									self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'  )

																									# Simple input
																									self.assertRaises(
																									    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'				,     )

																									# Pair input
																									self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'  )

																									# Pair input
																									self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'  )

																									# Pair input
																									self.assertRaises(
																									    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding='max_length'				,     )


				def 				__lowerCamelCase	(					self	:			str  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_rust_tokenizer()
											_lowerCAmelCase  	=			load_dataset('xnli'				,     'all_languages'				,     split='test'				,     streaming=UpperCAmelCase_  )

											_lowerCAmelCase  	=			next(iter(UpperCAmelCase_  )  )['premise']  # pick up one data
											_lowerCAmelCase  	=			list(sample_data.values()  )

											_lowerCAmelCase  	=			list(map(tokenizer.encode				,     UpperCAmelCase_  )  )
											_lowerCAmelCase  	=			[tokenizer.decode(UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_  ) for x in output_tokens]
											self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[Any]:







											"""simple docstring"""




											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map  )				,     1  )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()  )[0]  )				,     1  )






