

# 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


SCREAMING_SNAKE_CASE		:Optional[int]						=     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)

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

SCREAMING_SNAKE_CASE		:Any						=     """zero2"""
SCREAMING_SNAKE_CASE		:List[Any]						=     """zero3"""
SCREAMING_SNAKE_CASE		:Tuple						=     [ZEROa, ZEROa]




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE_			) for x in param.args			)			)
    return f"{func.__name__}_{param_based_name}"


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






@slow
@require_deepspeed
@require_torch_gpu
class 						__magic_name__					(							snake_case				):






     @parameterized.expand(_lowercase		,						name_func=_lowercase	)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Any:
         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	)->     Optional[Any]:
         self.run_and_check(
             stage=_lowercase		,						model=_lowercase		,						distributed=_lowercase		,						fpaa=_lowercase		,						)






     @parameterized.expand(_lowercase		,						name_func=_lowercase	)
     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Dict:
         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	)->     Optional[int]:
         self.run_and_check(
             stage=_lowercase		,						model=_lowercase		,						distributed=_lowercase		,						fpaa=_lowercase		,						)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Tuple:
         # 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		,						_lowercase		,						_lowercase		,						_lowercase = 10		,						_lowercase = True		,						_lowercase = True		,						_lowercase = True		,						)->     Optional[int]:
         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 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase = 10		,						_lowercase = 1		,						_lowercase = True		,						_lowercase = True		,						)->     Optional[Any]:
         UpperCamelCase_			  =						self.get_auto_remove_tmp_dir("./xxx"		,						after=_lowercase	)
         UpperCamelCase_			  =						F"\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(_lowercase	)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        ".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 	UpperCAmelCase_				(				self		,						_lowercase=False	)->     List[str]:
         # 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)
         UpperCamelCase_			  =						min(2		,						get_gpu_count()	) if distributed else 1
         return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[0, 1]
    UpperCamelCase_			  =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]			)
        if fib[i + 2] > n:
            break
        i += 1
    UpperCamelCase_			  =						0
    for j in range(len(SCREAMING_SNAKE_CASE_			) - 1			):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(F'''{solution() = }''')
