
# 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 :			Union[str, Any]              =	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 :			Dict              =	{"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

_UpperCAmelCase :			List[Any]              =	"zero2"
_UpperCAmelCase :			Optional[Any]              =	"zero3"
_UpperCAmelCase :			int              =	[ZEROa, ZEROa]


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					parameterized.to_safe_name('_'.join(str(lowercase			) for x in param.args			)			)
		return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_UpperCAmelCase :			List[str]              =	list(itertools.product(stages, models.keys()))


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



		@parameterized.expand(A_    ,				name_func=A_	)
		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> List[str]:
				"""simple docstring"""
				self.run_and_check(
				    stage=A_    ,				model=A_    ,				distributed=A_    ,				fpaa=A_    ,				)



		@require_torch_multi_gpu
		@parameterized.expand(A_    ,				name_func=A_	)
		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> str:
				"""simple docstring"""
				self.run_and_check(
				    stage=A_    ,				model=A_    ,				distributed=A_    ,				fpaa=A_    ,				)



		@parameterized.expand(A_    ,				name_func=A_	)
		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				self.run_and_check(
				    stage=A_    ,				model=A_    ,				distributed=A_    ,				fpaa=A_    ,				)



		@require_torch_multi_gpu
		@parameterized.expand(A_    ,				name_func=A_	)
		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				self.run_and_check(
				    stage=A_    ,				model=A_    ,				distributed=A_    ,				fpaa=A_    ,				)



		def __UpperCamelCase			(			self    ,				A_	)  -> str:
				"""simple docstring"""
				# 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    ,				A_    ,				A_    ,				A_ = 10    ,				A_ = True    ,				A_ = True    ,				A_ = True    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					models[model]

				UpperCamelCase          =					self.run_trainer(
				    stage=A_    ,				model_name=A_    ,				eval_steps=A_    ,				num_train_epochs=1    ,				distributed=A_    ,				fpaa=A_    ,				)

				self.do_checks(A_	)

				return output_dir



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = 10    ,				A_ = 1    ,				A_ = True    ,				A_ = True    ,				)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_auto_remove_tmp_dir('./xxx'    ,				after=A_	)
				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(A_	)}
            --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(A_	)

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

				return output_dir



		def __UpperCamelCase			(			self    ,				A_=False	)  -> Dict:
				"""simple docstring"""
				# 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()


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
