





# 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(42)

UpperCamelCase__   : List[Any]					  = {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCamelCase__   : Tuple					  = """zero2"""
UpperCamelCase__   : Optional[int]					  = """zero3"""
UpperCamelCase__   : str					  = [ZEROa, ZEROa]




def    A_(			A				,      A				,      A   ):
	# 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
	UpperCAmelCase_								=     parameterized.to_safe_name("""_""".join(str(A   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


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







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







					'''simple docstring'''

					@parameterized.expand(__lowercase ,					name_func=__lowercase		)
					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  List[str]		):






						'''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        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







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





					@parameterized.expand(__lowercase ,					name_func=__lowercase		)
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  List[str] ,					__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        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Tuple		):






						'''simple docstring'''







						pass





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  int = 10 ,					__lowercase    :  bool = True ,					__lowercase    :  bool = True ,					__lowercase    :  bool = True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     models[model]

						UpperCAmelCase_								=     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        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  int = 10 ,					__lowercase    :  int = 1 ,					__lowercase    :  bool = True ,					__lowercase    :  bool = True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_auto_remove_tmp_dir("""./xxx""" ,					after=__lowercase		)
						UpperCAmelCase_								=     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
						UpperCAmelCase_								=     F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
						UpperCAmelCase_								=     [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
						UpperCAmelCase_								=     self.get_launcher(__lowercase		)

						UpperCAmelCase_								=     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        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Tuple=False		):






						'''simple docstring'''







						UpperCAmelCase_								=     min(2 ,					get_gpu_count()		) if distributed else 1
						return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


