





'''simple docstring'''

# 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


UpperCAmelCase					:		Dict		=							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)

UpperCAmelCase					:		Optional[int]		=							{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

UpperCAmelCase					:		Union[str, Any]		=							'zero2'
UpperCAmelCase					:		Union[str, Any]		=							'zero3'
UpperCAmelCase					:		str		=							[ZEROa, ZEROa]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	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
UpperCAmelCase					:		List[str]		=							list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class lowerCamelCase						(a__					):







  @parameterized.expand(lowercase__     ,     name_func=lowercase__		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        self.run_and_check(
            stage=lowercase__     ,     model=lowercase__     ,     distributed=lowercase__     ,     fpaa=lowercase__     ,     )







  @require_torch_multi_gpu
  @parameterized.expand(lowercase__     ,     name_func=lowercase__		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        self.run_and_check(
            stage=lowercase__     ,     model=lowercase__     ,     distributed=lowercase__     ,     fpaa=lowercase__     ,     )







  @parameterized.expand(lowercase__     ,     name_func=lowercase__		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        self.run_and_check(
            stage=lowercase__     ,     model=lowercase__     ,     distributed=lowercase__     ,     fpaa=lowercase__     ,     )







  @require_torch_multi_gpu
  @parameterized.expand(lowercase__     ,     name_func=lowercase__		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						str:






        """simple docstring"""





        self.run_and_check(
            stage=lowercase__     ,     model=lowercase__     ,     distributed=lowercase__     ,     fpaa=lowercase__     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        pass







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = 10     ,     lowercase__ = True     ,     lowercase__ = True     ,     lowercase__ = True     ,     )		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Dict					     =     models[model]

        _snake_case		:	int					     =     self.run_trainer(
            stage=lowercase__     ,     model_name=lowercase__     ,     eval_steps=lowercase__     ,     num_train_epochs=1     ,     distributed=lowercase__     ,     fpaa=lowercase__     ,     )

        self.do_checks(lowercase__		)

        return output_dir







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = 10     ,     lowercase__ = 1     ,     lowercase__ = True     ,     lowercase__ = True     ,     )		->						int:






        """simple docstring"""





        _snake_case		:	Any					     =     self.get_auto_remove_tmp_dir('''./xxx'''     ,     after=lowercase__		)
        _snake_case		:	str					     =     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(lowercase__		)}
            --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
        _snake_case		:	Dict					     =     F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
        _snake_case		:	Any					     =     [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
        _snake_case		:	Tuple					     =     self.get_launcher(lowercase__		)

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

        return output_dir







  def 				UpperCAmelCase_				(		self     ,     lowercase__=False		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     min(2     ,     get_gpu_count()		) if distributed else 1
        return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


