'''simple docstring'''
import sys
from pathlib import Path


a=  Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

a=  {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

a=  '''zero2'''
a=  '''zero3'''
a=  [ZEROa, ZEROa]







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	Optional[Any]				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Tuple		    =  parameterized.to_safe_name('_'.join(str(_a			) for x in param.args			)			)
    return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
a=  list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 			__lowercase		(  _lowerCamelCase						):







     """simple docstring"""






     @parameterized.expand(_lowerCamelCase     ,			name_func=_lowerCamelCase    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.run_and_check(
             stage=_lowerCamelCase     ,			model=_lowerCamelCase     ,			distributed=_lowerCamelCase     ,			fpaa=_lowerCamelCase     ,			)







     @require_torch_multi_gpu
     @parameterized.expand(_lowerCamelCase     ,			name_func=_lowerCamelCase    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.run_and_check(
             stage=_lowerCamelCase     ,			model=_lowerCamelCase     ,			distributed=_lowerCamelCase     ,			fpaa=_lowerCamelCase     ,			)







     @parameterized.expand(_lowerCamelCase     ,			name_func=_lowerCamelCase    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.run_and_check(
             stage=_lowerCamelCase     ,			model=_lowerCamelCase     ,			distributed=_lowerCamelCase     ,			fpaa=_lowerCamelCase     ,			)







     @require_torch_multi_gpu
     @parameterized.expand(_lowerCamelCase     ,			name_func=_lowerCamelCase    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.run_and_check(
             stage=_lowerCamelCase     ,			model=_lowerCamelCase     ,			distributed=_lowerCamelCase     ,			fpaa=_lowerCamelCase     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # XXX: run_asr is premature and doesn't save any results
         # so all we check for now is that the process didn't fail
         pass







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 1_0     ,			_lowerCamelCase = True     ,			_lowerCamelCase = True     ,			_lowerCamelCase = True     ,			):
         __UpperCamelCase	:	int		    =  models[model]

         __UpperCamelCase	:	str		    =  self.run_trainer(
             stage=_lowerCamelCase     ,			model_name=_lowerCamelCase     ,			eval_steps=_lowerCamelCase     ,			num_train_epochs=1     ,			distributed=_lowerCamelCase     ,			fpaa=_lowerCamelCase     ,			)

         self.do_checks(_lowerCamelCase    )

         return output_dir







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 1_0     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = True     ,			_lowerCamelCase = True     ,			):
         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir('./xxx'     ,			after=_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  f"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_lowerCamelCase    )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

         if fpaa:
             args.extend(['--fp16']    )

         # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
         # hence the separate config files
         __UpperCamelCase	:	Optional[int]		    =  f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
         __UpperCamelCase	:	Optional[int]		    =  [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
         __UpperCamelCase	:	Optional[int]		    =  self.get_launcher(_lowerCamelCase    )

         __UpperCamelCase	:	Any		    =  launcher + script + args + ds_args
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         execute_subprocess_async(_lowerCamelCase     ,			env=self.get_env()    )

         return output_dir







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False    ):
         # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
         # - it won't be able to handle that
         # 2. for now testing with just 2 gpus max (since some quality tests may give different
         # results with mode gpus because we use very little data)
         __UpperCamelCase	:	List[Any]		    =  min(2     ,			get_gpu_count()    ) if distributed else 1
         return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()




'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a=  logging.get_logger(__name__)

a=  '''▁'''

a=  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a=  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a=  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a=  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			do_upper_case=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase     ,			tgt_lang=_lowerCamelCase     ,			lang_codes=_lowerCamelCase     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_lowerCamelCase     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

         if lang_codes is not None:
             __UpperCamelCase	:	Any		    =  lang_codes
             __UpperCamelCase	:	Any		    =  LANGUAGES[lang_codes]
             __UpperCamelCase	:	str		    =  [f"""<lang:{lang}>""" for lang in self.langs]
             __UpperCamelCase	:	List[str]		    =  {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.sp_model.decode(_lowerCamelCase    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=None    ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False    ):

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_lowerCamelCase     ,			token_ids_a=_lowerCamelCase     ,			already_has_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [1]
         if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCamelCase    )) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCamelCase    )) + ([0] * len(_lowerCamelCase    )) + suffix_ones







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







     def __getstate__(		self    ):
         __UpperCamelCase	:	int		    =  self.__dict__.copy()
         __UpperCamelCase	:	Dict		    =  None
         return state







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

         # for backward compatibility
         if not hasattr(self     ,			'sp_model_kwargs'    ):
             __UpperCamelCase	:	Optional[int]		    =  {}

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,			_lowerCamelCase    )

         if os.path.abspath(self.spm_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.spm_file    ):
             copyfile(self.spm_file     ,			_lowerCamelCase    )
         elif not os.path.isfile(self.spm_file    ):
             with open(_lowerCamelCase     ,			'wb'    ) as fi:
                 __UpperCamelCase	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'r'			) as f:
        return json.load(_a			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=2			)



