


"""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


__snake_case    					=					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)

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

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							Union[str, 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
 lowercase__       :       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
__snake_case    					=					list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   @parameterized.expand(lowerCamelCase__ ,     name_func=lowerCamelCase__						)
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Dict:
    self.run_and_check(
        stage=lowerCamelCase__ ,     model=lowerCamelCase__ ,     distributed=lowerCamelCase__ ,     fpaa=lowerCamelCase__ ,     )


   @require_torch_multi_gpu
   @parameterized.expand(lowerCamelCase__ ,     name_func=lowerCamelCase__						)
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[int]:
    self.run_and_check(
        stage=lowerCamelCase__ ,     model=lowerCamelCase__ ,     distributed=lowerCamelCase__ ,     fpaa=lowerCamelCase__ ,     )


   @parameterized.expand(lowerCamelCase__ ,     name_func=lowerCamelCase__						)
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Union[str, Any]:
    self.run_and_check(
        stage=lowerCamelCase__ ,     model=lowerCamelCase__ ,     distributed=lowerCamelCase__ ,     fpaa=lowerCamelCase__ ,     )


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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ = 10 ,     lowerCamelCase__ = True ,     lowerCamelCase__ = True ,     lowerCamelCase__ = True ,     )      ->							Dict:
    lowercase__       :       int				=						models[model]

    lowercase__       :       Any				=						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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ = 10 ,     lowerCamelCase__ = 1 ,     lowerCamelCase__ = True ,     lowerCamelCase__ = True ,     )      ->							int:
    lowercase__       :       Tuple				=						self.get_auto_remove_tmp_dir("""./xxx""" ,     after=lowerCamelCase__						)
    lowercase__       :       List[Any]				=						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
    lowercase__       :       str				=						F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
    lowercase__       :       Optional[Any]				=						[F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
    lowercase__       :       Dict				=						self.get_launcher(lowerCamelCase__						)

    lowercase__       :       Optional[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 					UpperCAmelCase__(						self ,     lowerCamelCase__=False						)      ->							Union[str, 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)
    lowercase__       :       Any				=						min(2 ,     get_gpu_count()						) if distributed else 1
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()



"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)