






'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_ 		=       get_tests_dir('fixtures/test_sentencepiece.model')

if is_sentencepiece_available():
  import sentencepiece as sp


a_ 		=       5
a_ 		=       1_0






@require_sentencepiece
@require_tokenizers
class    __SCREAMING_SNAKE_CASE					(	__SCREAMING_SNAKE_CASE		, unittest.TestCase ):
 snake_case_     							=							SpeechaTextTokenizer
 snake_case_     							=							False
 snake_case_     							=							True




 def    __magic_name__		(					self		:							Any					)		->  str:
       super().setUp()

       SCREAMING_SNAKE_CASE__   : Any													=sp.SentencePieceProcessor()
       spm_model.Load(_SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Tuple													=["<s>", "<pad>", "</s>", "<unk>"]

       vocab += [spm_model.IdToPiece(id_					) for id_ in range(len(_SCREAMING_SNAKE_CASE					)					)]
       SCREAMING_SNAKE_CASE__   : Dict													=dict(zip(_SCREAMING_SNAKE_CASE    ,					range(len(_SCREAMING_SNAKE_CASE					)					)					)					)

       SCREAMING_SNAKE_CASE__   : Any													=Path(self.tmpdirname					)
       save_json(_SCREAMING_SNAKE_CASE    ,					save_dir / VOCAB_FILES_NAMES['''vocab_file''']					)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
             copyfile(_SCREAMING_SNAKE_CASE    ,					save_dir / VOCAB_FILES_NAMES['''spm_file''']					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
       tokenizer.save_pretrained(self.tmpdirname					)




 def    __magic_name__		(					self		:							str					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													="<pad>"
       SCREAMING_SNAKE_CASE__   : str													=1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE					)    ,					_SCREAMING_SNAKE_CASE					)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE					)    ,					_SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=list(self.get_tokenizer().get_vocab().keys()					)

       self.assertEqual(vocab_keys[0]    ,					'''<s>'''					)
       self.assertEqual(vocab_keys[1]    ,					'''<pad>'''					)
       self.assertEqual(vocab_keys[-1]    ,					'''j'''					)
       self.assertEqual(len(_SCREAMING_SNAKE_CASE					)    ,					10_01					)




 def    __magic_name__		(					self		:							int					)		->  str:
       self.assertEqual(self.get_tokenizer().vocab_size    ,					10_01					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Dict													=SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE__   : List[str]													=tokenizer.tokenize('''This is a test'''					)
       self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)    ,					[2_89, 50, 14, 1_74, 3_86]    ,					)

       SCREAMING_SNAKE_CASE__   : Any													=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
       self.assertListEqual(
           _SCREAMING_SNAKE_CASE    ,					[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']    ,					)
       SCREAMING_SNAKE_CASE__   : Tuple													=tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
       self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					[12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]					)

       SCREAMING_SNAKE_CASE__   : List[str]													=tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					)
       self.assertListEqual(
           _SCREAMING_SNAKE_CASE    ,					[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']    ,					)




 @slow
 def    __magic_name__		(					self		:							Union[str, Any]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : Tuple													={"input_ids": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_SCREAMING_SNAKE_CASE    ,					model_name='''facebook/s2t-small-mustc-en-de-st'''    ,					revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''    ,					)






@require_sentencepiece
class    __SCREAMING_SNAKE_CASE					(	unittest.TestCase ):
 snake_case_     							=							"""valhalla/s2t_mustc_multilinguial_medium"""

 snake_case_     							=							"""C\'est trop cool"""
 snake_case_     							=							"""Esto es genial"""




 @classmethod
 def    __magic_name__		(					cls		:							str					)		->  Any:
       SCREAMING_SNAKE_CASE__   : SpeechaTextTokenizer													=SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name					)
       return cls




 def    __magic_name__		(					self		:							Dict					)		->  str:
       self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']    ,					4					)
       self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']    ,					6					)
       self.assertEqual(self.tokenizer.lang_code_to_id['''it''']    ,					9					)
       self.assertEqual(self.tokenizer.lang_code_to_id['''de''']    ,					11					)




 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Optional[Any]:
       self.assertEqual(self.tokenizer.vocab_size    ,					1_00_00					)




 def    __magic_name__		(					self		:							Dict					)		->  int:
       self.assertIn(_SCREAMING_SNAKE_CASE    ,					self.tokenizer.all_special_ids					)
       SCREAMING_SNAKE_CASE__   : Any													=[ES_CODE, 4, 16_01, 47, 76_47, 2]
       SCREAMING_SNAKE_CASE__   : str													=self.tokenizer.decode(_SCREAMING_SNAKE_CASE    ,					skip_special_tokens=_SCREAMING_SNAKE_CASE					)
       SCREAMING_SNAKE_CASE__   : Tuple													=self.tokenizer.decode(generated_ids[1:]    ,					skip_special_tokens=_SCREAMING_SNAKE_CASE					)
       self.assertEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE					)
       self.assertNotIn(self.tokenizer.eos_token    ,					_SCREAMING_SNAKE_CASE					)




 def    __magic_name__		(					self		:							Optional[int]					)		->  int:
       SCREAMING_SNAKE_CASE__   : Tuple													="fr"
       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.tokenizer(self.french_text					).input_ids
       self.assertEqual(encoded[0]    ,					_SCREAMING_SNAKE_CASE					)
       self.assertEqual(encoded[-1]    ,					self.tokenizer.eos_token_id					)




 def    __magic_name__		(					self		:							List[str]					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													="fr"
       self.assertListEqual(self.tokenizer.prefix_tokens    ,					[FR_CODE]					)

       SCREAMING_SNAKE_CASE__   : Tuple													="es"
       self.assertListEqual(self.tokenizer.prefix_tokens    ,					[ES_CODE]					)




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       max(self.process.memory_info().rss       ,			self.cpu_memory_peak)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							if not self.peak_monitoring:
										break

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  Optional[Any]												=       psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       torch.cuda.memory_allocated(__snake_case	)
			torch.cuda.reset_peak_memory_stats()

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  str												=       (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase      :  List[str]												=       (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			print(F"""{description}:"""	)
			print(F"""- Time: {measures['time']:.2f}s"""	)
			for i in range(torch.cuda.device_count()	):
						print(F"""- GPU {i} allocated: {measures[str(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[Any]												=       measures[F"""{i}-peak"""]
						print(F"""- GPU {i} peak: {peak:.2f}MiB"""	)
			print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""	)
			print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""	)