
# 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_						=						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)

UpperCamelCase_						=						{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCamelCase_						=						"""zero2"""
UpperCamelCase_						=						"""zero3"""
UpperCamelCase_						=						[ZEROa, ZEROa]





def 				_lowerCamelCase      (       lowerCamelCase_:	int					,						lowerCamelCase_:	Optional[Any]					,						lowerCamelCase_:	Optional[int]		):



				'''simple docstring'''







				A  :	Optional[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(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class      _SCREAMING_SNAKE_CASE				(		_UpperCamelCase				):





				@parameterized.expand(snake_case_       ,   name_func=snake_case_				)
				def 							_UpperCAmelCase    (		self			:		List[Any]       ,   snake_case_			:		Union[str, Any]       ,   snake_case_			:		List[Any]				):


								"""simple docstring"""

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





				@require_torch_multi_gpu
				@parameterized.expand(snake_case_       ,   name_func=snake_case_				)
				def 							_UpperCAmelCase    (		self			:		Dict       ,   snake_case_			:		Optional[int]       ,   snake_case_			:		Optional[Any]				):


								"""simple docstring"""

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





				@parameterized.expand(snake_case_       ,   name_func=snake_case_				)
				def 							_UpperCAmelCase    (		self			:		List[Any]       ,   snake_case_			:		Dict       ,   snake_case_			:		str				):


								"""simple docstring"""

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





				@require_torch_multi_gpu
				@parameterized.expand(snake_case_       ,   name_func=snake_case_				)
				def 							_UpperCAmelCase    (		self			:		List[str]       ,   snake_case_			:		Tuple       ,   snake_case_			:		str				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		Any				):


								"""simple docstring"""

								pass





				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		Any       ,   snake_case_			:		Optional[int]       ,   snake_case_			:		Dict = 10       ,   snake_case_			:		List[Any] = True       ,   snake_case_			:		str = True       ,   snake_case_			:		Tuple = True       ,   ):


								"""simple docstring"""

								A  :	Tuple				=					models[model]

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

								self.do_checks(snake_case_				)

								return output_dir





				def 							_UpperCAmelCase    (		self			:		List[Any]       ,   snake_case_			:		List[str]       ,   snake_case_			:		Any       ,   snake_case_			:		Union[str, Any] = 10       ,   snake_case_			:		str = 1       ,   snake_case_			:		Optional[Any] = True       ,   snake_case_			:		Tuple = True       ,   ):


								"""simple docstring"""

								A  :	str				=					self.get_auto_remove_tmp_dir('''./xxx'''       ,   after=snake_case_				)
								A  :	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(snake_case_				)}
            --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  :	str				=					f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
								A  :	Dict				=					[f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
								A  :	Optional[int]				=					self.get_launcher(snake_case_				)

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

								return output_dir






				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		List[Any]=False				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					min(2       ,   get_gpu_count()				) if distributed else 1
								return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__							:      Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

