


'''simple docstring'''






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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




@require_sentencepiece
@require_tokenizers
class 						a__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[Any]      		= XLMRobertaTokenizer
							lowercase__     :  Optional[Any]      		= XLMRobertaTokenizerFast
							lowercase__     :  Union[str, Any]      		= True
							lowercase__     :  List[str]      		= True







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														super().setUp()

														# We have a SentencePiece fixture for testing
														lowerCAmelCase__			  =      XLMRobertaTokenizer(lowerCamelCase_   ,       keep_accents=lowerCamelCase_  )
														tokenizer.save_pretrained(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      '''<pad>'''
														lowerCAmelCase__			  =      1

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]   ,       '''<s>'''  )
														self.assertEqual(vocab_keys[1]   ,       '''<pad>'''  )
														self.assertEqual(vocab_keys[-1]   ,       '''<mask>'''  )
														self.assertEqual(len(lowerCamelCase_  )   ,       10_02  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														self.assertEqual(self.get_tokenizer().vocab_size   ,       10_02  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      XLMRobertaTokenizer(lowerCamelCase_   ,       keep_accents=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer.tokenize('''This is a test'''  )
														self.assertListEqual(lowerCamelCase_   ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCamelCase_  )   ,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,       )

														lowerCAmelCase__			  =      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        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''',
														        '''.''',
														    ]   ,       )
														lowerCAmelCase__			  =      tokenizer.convert_tokens_to_ids(lowerCamelCase_  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        value + tokenizer.fairseq_offset
														        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
														        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
														    ]   ,       )

														lowerCAmelCase__			  =      tokenizer.convert_ids_to_tokens(lowerCamelCase_  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        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>''',
														        '''.''',
														    ]   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														if not self.test_slow_tokenizer:
																					# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																					return

														lowerCAmelCase__			  =      (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
														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(lowerCamelCase_   ,       **lowerCamelCase_  )
																												lowerCAmelCase__			  =      self.tokenizer_class.from_pretrained(lowerCamelCase_   ,       **lowerCamelCase_  )

																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it save with the same files + the tokenizer.json file for the fast one
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )
																												lowerCAmelCase__			  =      tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
																												self.assertSequenceEqual(lowerCamelCase_   ,       lowerCamelCase_  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )
																																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																												shutil.rmtree(lowerCamelCase_  )

																												# Save tokenizer rust, legacy_format=True
																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_   ,       legacy_format=lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it save with the same files
																												self.assertSequenceEqual(lowerCamelCase_   ,       lowerCamelCase_  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )

																												shutil.rmtree(lowerCamelCase_  )

																												# Save tokenizer rust, legacy_format=False
																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_   ,       legacy_format=lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it saved the tokenizer.json file
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )

																												shutil.rmtree(lowerCamelCase_  )







							@cached_property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														with tempfile.NamedTemporaryFile() as f:
																					shutil.copyfile(lowerCamelCase_   ,       f.name  )
																					lowerCAmelCase__			  =      XLMRobertaTokenizer(f.name   ,       keep_accents=lowerCamelCase_  )
																					lowerCAmelCase__			  =      pickle.dumps(lowerCamelCase_  )
														pickle.loads(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														if not self.test_rust_tokenizer:
																					return

														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_rust_tokenizer()

														lowerCAmelCase__			  =      '''I was born in 92000, and this is falsé.'''

														lowerCAmelCase__			  =      tokenizer.tokenize(lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.tokenize(lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer.encode(lowerCamelCase_   ,       add_special_tokens=lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.encode(lowerCamelCase_   ,       add_special_tokens=lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_rust_tokenizer()
														lowerCAmelCase__			  =      tokenizer.encode(lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.encode(lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      '''Hello World!'''
														lowerCAmelCase__			  =      [0, 3_53_78, 66_61, 38, 2]
														# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
														# xlmr.eval()
														# xlmr.encode(symbols)

														self.assertListEqual(lowerCamelCase_   ,       self.big_tokenizer.encode(lowerCamelCase_  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      (
														    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
														    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
														)
														lowerCAmelCase__			  =      [
														    0,
														    32_93,
														    83,
														    10,
														    45_52,
														    49_89,
														    79_86,
														    6_78,
														    10,
														    59_15,
														    1_11,
														    17_94_59,
														    12_48_50,
														    4,
														    60_44,
														    2_37,
														    12,
														    6,
														    5,
														    6,
														    4,
														    67_80,
														    7_05,
														    15,
														    13_88,
														    44,
														    3_78,
														    1_01_14,
														    7_11,
														    1_52,
														    20,
														    6,
														    5,
														    2_23_76,
														    6_42,
														    12_21,
														    1_51_90,
														    3_41_53,
														    4_50,
														    56_08,
														    9_59,
														    11_19,
														    5_77_02,
														    1_36,
														    1_86,
														    47,
														    10_98,
														    2_93_67,
														    47,
														    # 4426, # What fairseq tokenizes from "<unk>": "_<"
														    # 3678, # What fairseq tokenizes from "<unk>": "unk"
														    # 2740, # What fairseq tokenizes from "<unk>": ">"
														    3,  # What we tokenize from "<unk>": "<unk>"
														    6,  # Residue from the tokenization: an extra sentencepiece underline
														    4,
														    60_44,
														    2_37,
														    62_84,
														    5_09_01,
														    5_28,
														    31,
														    90,
														    34,
														    9_27,
														    2,
														]
														# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
														# xlmr.eval()
														# xlmr.encode(symbols)

														self.assertListEqual(lowerCamelCase_   ,       self.big_tokenizer.encode(lowerCamelCase_  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														# fmt: off
														lowerCAmelCase__			  =      {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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]], '''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, 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, 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=lowerCamelCase_   ,       model_name='''xlm-roberta-base'''   ,       revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''   ,       )



"""simple docstring"""


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
	A	:  List[Any]            =     True
	from torch.cuda.amp import autocast


A	:  Any            =     logging.getLogger(__name__)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to log verbose messages or not."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=2.0			,metadata={"""help""": """Maximum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.5			,metadata={"""help""": """Minimum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.999_995			,metadata={"""help""": """Decay of gumbel temperature during training."""}						)


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,					datefmt="%m/%d/%Y %H:%M:%S"       ,					handlers=[logging.StreamHandler(sys.stdout )]       ,					)
			__lowerCAmelCase							       =     logging.WARNING
			if model_args.verbose_logging:
						__lowerCAmelCase							       =     logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank ):
						__lowerCAmelCase							       =     logging.INFO
			logger.setLevel(_UpperCamelCase )
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""train"""			,metadata={
							        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""validation"""			,metadata={
							        """help""": (
							            """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
							        )
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""file"""			,metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}			,)
							__UpperCAmelCase		:       bool							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=1			,metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }			,)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The number of processes to use for the preprocessing."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=20.0			,metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}						)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       WavaVecaForPreTraining
							__UpperCAmelCase		:       WavaVecaFeatureExtractor
							__UpperCAmelCase		:       Union[bool, str]							 ="longest"
							__UpperCAmelCase		:       Optional[int]							 =None
							__UpperCAmelCase		:       Optional[int]							 =None

							def __call__(	self       ,  __a      ):
										# reformat list to dict and set to pytorch format
										__lowerCAmelCase							       =     self.feature_extractor.pad(
										    __a       ,  max_length=self.max_length       ,  padding=self.padding       ,  pad_to_multiple_of=self.pad_to_multiple_of       ,  return_tensors="pt"       ,  )
										__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]      )

										__lowerCAmelCase							       =     batch["input_values"].shape[0]

										# make sure that no loss is computed on padded inputs
										if batch["attention_mask"] is not None:
													# compute real output lengths according to convolution formula
													__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1      )      ).to(
													    torch.long      )

													__lowerCAmelCase							       =     torch.zeros(
													    (batch_size, mask_indices_seq_length)       ,  dtype=torch.long       ,  device=batch["input_values"].device      )

													# these two operations makes sure that all values
													# before the output lengths indices are attended to
													__lowerCAmelCase							       =     1
													__lowerCAmelCase							       =     attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

										# sample randomly masked indices
										__lowerCAmelCase							       =     _compute_mask_indices(
										    (batch_size, mask_indices_seq_length)       ,  self.model.config.mask_time_prob       ,  self.model.config.mask_time_length       ,  attention_mask=__a       ,  min_masks=2       ,  )

										return batch
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  __a=1       ,  __a=0       ,  __a=1.0       ,  **__a      ):
										super().__init__(*__a       ,  **__a      )
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     max_gumbel_temp
										__lowerCAmelCase							       =     min_gumbel_temp
										__lowerCAmelCase							       =     gumbel_temp_decay



							def      snake_case							(	self       ,  __a       ,  __a      ):
										model.train()
										__lowerCAmelCase							       =     self._prepare_inputs(__a      )

										if self.use_amp:
													with autocast():
																__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )
										else:
													__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )

										if self.args.n_gpu > 1 or self.deepspeed:
													if model.module.config.ctc_loss_reduction == "mean":
																__lowerCAmelCase							       =     loss.mean()
													elif model.module.config.ctc_loss_reduction == "sum":
																__lowerCAmelCase							       =     loss.sum() / (inputs["mask_time_indices"]).sum()
													else:
																raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

										if self.args.gradient_accumulation_steps > 1:
													__lowerCAmelCase							       =     loss / self.args.gradient_accumulation_steps

										if self.use_amp:
													self.scaler.scale(__a      ).backward()
										elif self.use_apex:
													with amp.scale_loss(__a       ,  self.optimizer      ) as scaled_loss:
																scaled_loss.backward()
										elif self.deepspeed:
													self.deepspeed.backward(__a      )
										else:
													loss.backward()

										self.num_update_step += 1
										# make sure gumbel softmax temperature is decayed
										if self.args.n_gpu > 1 or self.deepspeed:
													model.module.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )
										else:
													model.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )

										return loss.detach()


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

			__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     parser.parse_args_into_dataclasses()
			configure_logger(_UpperCamelCase       ,					_UpperCamelCase )

			# Downloading and loading a dataset from the hub.
			__lowerCAmelCase							       =     load_dataset(data_args.dataset_name       ,					data_args.dataset_config_name       ,					cache_dir=model_args.cache_dir )

			if "validation" not in datasets.keys():
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"       ,					cache_dir=model_args.cache_dir       ,					)
			else:
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split="validation"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}"       ,					cache_dir=model_args.cache_dir       ,					)

			# only normalized-inputs-training is supported
			__lowerCAmelCase							       =     WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					do_normalize=_UpperCamelCase )

			def prepare_dataset(_UpperCamelCase ):
						# check that all files have the correct sampling rate
						__lowerCAmelCase       ,      __lowerCAmelCase							       =     librosa.load(batch[data_args.speech_file_column]       ,					sr=feature_extractor.sampling_rate )
						return batch

			# load audio files into numpy arrays
			__lowerCAmelCase							       =     datasets.map(
			    _UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					remove_columns=datasets["train"].column_names )

			# filter audio files that are too long
			__lowerCAmelCase							       =     vectorized_datasets.filter(
			    lambda _UpperCamelCase : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )

			def normalize(_UpperCamelCase ):
						return feature_extractor(batch["speech"]       ,					sampling_rate=feature_extractor.sampling_rate )

			# normalize and transform to `BatchFeatures`
			__lowerCAmelCase							       =     vectorized_datasets.map(
			    _UpperCamelCase       ,					batched=_UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					load_from_cache_file=not data_args.overwrite_cache       ,					remove_columns=vectorized_datasets["train"].column_names       ,					)

			# pretraining is only supported for "newer" stable layer norm architecture
			# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
			__lowerCAmelCase							       =     WavaVecaConfig.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					gradient_checkpointing=training_args.gradient_checkpointing       ,					)

			if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
						raise ValueError(
						    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
						    " ``config.feat_extract_norm='layer'" )

			__lowerCAmelCase							       =     WavaVecaForPreTraining(_UpperCamelCase )

			__lowerCAmelCase							       =     DataCollatorForWavaVecaPretraining(model=_UpperCamelCase       ,					feature_extractor=_UpperCamelCase )

			__lowerCAmelCase							       =     WavaVecaPreTrainer(
			    model=_UpperCamelCase       ,					data_collator=_UpperCamelCase       ,					args=_UpperCamelCase       ,					train_dataset=vectorized_datasets["train"]       ,					eval_dataset=vectorized_datasets["validation"]       ,					tokenizer=_UpperCamelCase       ,					max_gumbel_temp=model_args.max_gumbel_temperature       ,					min_gumbel_temp=model_args.min_gumbel_temperature       ,					gumbel_temp_decay=model_args.gumbel_temperature_decay       ,					)
			trainer.train()


if __name__ == "__main__":
	main()




