


'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 lowerCamelCase							:   Any  						=	None


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Optional[int]  						=	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase							:   List[str]  						=	{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:   Any  						=	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							VOCAB_FILES_NAMES
      lowerCAmelCase__					:     str  					=							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__					:     Optional[Any]  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__					:     int  					=							["""input_ids""", """attention_mask"""]
      lowerCAmelCase__					:     Optional[int]  					=							TaTokenizer

      lowerCAmelCase__					:     List[int]  					=							[]



      def __init__(self    :    Dict   ,						UpperCamelCase    :    str=None   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    Any="</s>"   ,						UpperCamelCase    :    str="<unk>"   ,						UpperCamelCase    :    List[str]="<pad>"   ,						UpperCamelCase    :    List[str]=100   ,						UpperCamelCase    :    Tuple=None   ,						**UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             if extra_ids > 0 and additional_special_tokens is None:
                    lowercase__      			= [f"<extra_id_{i}>" for i in range(UpperCamelCase )]
             elif extra_ids > 0 and additional_special_tokens is not None:
                    # Check that we have the right number of extra special tokens
                    lowercase__      			= len(set(filter(lambda UpperCamelCase : bool('''extra_id_''' in str(UpperCamelCase ) )   ,						UpperCamelCase ) ) )
                    if extra_tokens != extra_ids:
                           raise ValueError(
                               f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                               ''' tokens''' )

             super().__init__(
                 UpperCamelCase   ,						tokenizer_file=UpperCamelCase   ,						eos_token=UpperCamelCase   ,						unk_token=UpperCamelCase   ,						pad_token=UpperCamelCase   ,						extra_ids=UpperCamelCase   ,						additional_special_tokens=UpperCamelCase   ,						**UpperCamelCase   ,						)

             lowercase__      			= vocab_file
             lowercase__      			= False if not self.vocab_file else True
             lowercase__      			= extra_ids



      @staticmethod
      def       UpperCamelCase__		(UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                    lowercase__      			= TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                    if init_max_model_length is not None and init_max_model_length != max_model_length:
                           return init_max_model_length
                    elif init_max_model_length is None:
                           warnings.warn(
                               '''This tokenizer was incorrectly instantiated with a model max length of'''
                               f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                               f" {pretrained_model_name_or_path} automatically truncating your input to"
                               f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                               f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''   ,						UpperCamelCase   ,						)

             return max_model_length



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[str] = None ):

             '''simple docstring'''







             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                        '''tokenizer.''' )

             if not os.path.isdir(UpperCamelCase ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
                    return
             lowercase__      			= os.path.join(
                 UpperCamelCase   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

             if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ):
                    copyfile(self.vocab_file   ,						UpperCamelCase )
                    logger.info(f"Copy vocab file to {out_vocab_file}" )

             return (out_vocab_file,)



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a
             else:
                    lowercase__      			= token_ids_a + [self.eos_token_id]
                    return self.prefix_tokens + token_ids_a + token_ids_a



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    List[int]   ,						UpperCamelCase    :    Optional[List[int]] = None ):

             '''simple docstring'''







             lowercase__      			= [self.eos_token_id]

             if token_ids_a is None:
                    return len(token_ids_a + eos ) * [0]
             return len(token_ids_a + eos + token_ids_a + eos ) * [0]



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return list(
                 set(filter(lambda UpperCamelCase : bool(re.search(R'''<extra_id_\d+>'''   ,						UpperCamelCase ) ) is not None   ,						self.additional_special_tokens ) ) )






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return [self.convert_tokens_to_ids(UpperCamelCase ) for token in self.get_sentinel_tokens()]






# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       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(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




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




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# 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		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.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	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

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

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 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()