

'''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


__lowerCamelCase									=     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)

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

__lowerCamelCase									=     '''zero2'''
__lowerCamelCase									=     '''zero3'''
__lowerCamelCase									=     [ZEROa, ZEROa]
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       # customize the test name generator function as we want both params to appear in the sub-test
       # name, as by default it shows only the first param
       A_						       =   parameterized.to_safe_name("""_""".join(str(UpperCAmelCase__					) for x in param.args					)					)
       return F'''{func.__name__}_{param_based_name}'''


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







@slow
@require_deepspeed
@require_torch_gpu
class        A__   (   _snake_case      ):



    @parameterized.expand(UpperCamelCase__		,					name_func=UpperCamelCase__							)
    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           self.run_and_check(
               stage=UpperCamelCase__		,					model=UpperCamelCase__		,					distributed=UpperCamelCase__		,					fpaa=UpperCamelCase__		,					)



    @require_torch_multi_gpu
    @parameterized.expand(UpperCamelCase__		,					name_func=UpperCamelCase__							)
    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           self.run_and_check(
               stage=UpperCamelCase__		,					model=UpperCamelCase__		,					distributed=UpperCamelCase__		,					fpaa=UpperCamelCase__		,					)



    @parameterized.expand(UpperCamelCase__		,					name_func=UpperCamelCase__							)
    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           self.run_and_check(
               stage=UpperCamelCase__		,					model=UpperCamelCase__		,					distributed=UpperCamelCase__		,					fpaa=UpperCamelCase__		,					)



    @require_torch_multi_gpu
    @parameterized.expand(UpperCamelCase__		,					name_func=UpperCamelCase__							)
    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[Any]:





           '''simple docstring'''
           self.run_and_check(
               stage=UpperCamelCase__		,					model=UpperCamelCase__		,					distributed=UpperCamelCase__		,					fpaa=UpperCamelCase__		,					)



    def        snake_case_      (       self		,					UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           # 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        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = 10		,					UpperCamelCase__ = True		,					UpperCamelCase__ = True		,					UpperCamelCase__ = True		,					)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   models[model]

           A_						       =   self.run_trainer(
               stage=UpperCamelCase__		,					model_name=UpperCamelCase__		,					eval_steps=UpperCamelCase__		,					num_train_epochs=1		,					distributed=UpperCamelCase__		,					fpaa=UpperCamelCase__		,					)

           self.do_checks(UpperCamelCase__							)

           return output_dir



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = 10		,					UpperCamelCase__ = 1		,					UpperCamelCase__ = True		,					UpperCamelCase__ = True		,					)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   self.get_auto_remove_tmp_dir("""./xxx"""		,					after=UpperCamelCase__							)
           A_						       =   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(UpperCamelCase__							)}
            --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
           A_						       =   f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
           A_						       =   [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
           A_						       =   self.get_launcher(UpperCamelCase__							)

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

           return output_dir







    def        snake_case_      (       self		,					UpperCamelCase__=False							)    ->   int:





           '''simple docstring'''
           # 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)
           A_						       =   min(2		,					get_gpu_count()							) if distributed else 1
           return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()



'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
