


import sys
from pathlib import Path


_snake_case  :	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)

_snake_case  :	Optional[int]			      =       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

_snake_case  :	Union[str, Any]			      =       'zero2'
_snake_case  :	Union[str, Any]			      =       'zero3'
_snake_case  :	str			      =       [ZEROa, ZEROa]







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
       __snake_case	:    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[str]			      =       list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class        a	(a__   ):




       """simple docstring"""


       @parameterized.expand(lowercase__					,			name_func=lowercase__       )
       def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						List[str]					,			lowerCamelCase :						Any       )			->					Optional[Any]:
              self.run_and_check(
                  stage=lowercase__					,			model=lowercase__					,			distributed=lowercase__					,			fpaa=lowercase__					,			)

       @require_torch_multi_gpu
       @parameterized.expand(lowercase__					,			name_func=lowercase__       )
       def 					__snake_case      (							self :						str					,			lowerCamelCase :						Any					,			lowerCamelCase :						str       )			->					List[str]:
              self.run_and_check(
                  stage=lowercase__					,			model=lowercase__					,			distributed=lowercase__					,			fpaa=lowercase__					,			)

       @parameterized.expand(lowercase__					,			name_func=lowercase__       )
       def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
              self.run_and_check(
                  stage=lowercase__					,			model=lowercase__					,			distributed=lowercase__					,			fpaa=lowercase__					,			)

       @require_torch_multi_gpu
       @parameterized.expand(lowercase__					,			name_func=lowercase__       )
       def 					__snake_case      (							self :						int					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[int]       )			->					str:
              self.run_and_check(
                  stage=lowercase__					,			model=lowercase__					,			distributed=lowercase__					,			fpaa=lowercase__					,			)

       def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					List[str]:
              pass

       def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						int					,			lowerCamelCase :						Any					,			lowerCamelCase :						Union[str, Any] = 10					,			lowerCamelCase :						str = True					,			lowerCamelCase :						Optional[int] = True					,			lowerCamelCase :						Union[str, Any] = True					,			)			->					Optional[Any]:
              __snake_case	:    Dict											=     models[model]

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

              __snake_case	:    List[Any]											=     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 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						Optional[int]=False       )			->					Optional[Any]:
              __snake_case	:    Tuple											=     min(2					,			get_gpu_count()       ) if distributed else 1
              return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()





def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    Dict											=     str(bin(__lowerCamelCase			)			)
							binary_number += "0" * shift_amount
							return binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    List[str]											=     str(bin(__lowerCamelCase			)			)[2:]
							if shift_amount >= len(__lowerCamelCase			):
														return "0b0"
							__snake_case	:    Union[str, Any]											=     binary_number[: len(__lowerCamelCase			) - shift_amount]
							return "0b" + shifted_binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number >= 0:  # Get binary representation of positive number
														__snake_case	:    Optional[int]											=     "0" + str(bin(__lowerCamelCase			)			).strip("-"			)[2:]
							else:  # Get binary (2's complement) representation of negative number
														__snake_case	:    Tuple											=     len(bin(__lowerCamelCase			)[3:]			)  # Find 2's complement of number
														__snake_case	:    Any											=     bin(abs(__lowerCamelCase			) - (1 << binary_number_length)			)[3:]
														__snake_case	:    Optional[Any]											=     (
														    "1" + "0" * (binary_number_length - len(__lowerCamelCase			)) + binary_number
														)

							if shift_amount >= len(__lowerCamelCase			):
														return "0b" + binary_number[0] * len(__lowerCamelCase			)
							return (
							    "0b"
							    + binary_number[0] * shift_amount
							    + binary_number[: len(__lowerCamelCase			) - shift_amount]
							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

