






'''simple docstring'''







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
			import tensorflow as tf
			from tensorflow.python.framework.errors_impl import ResourceExhaustedError

			from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
			import pyanvml.pyanvml as nvml

_A	:			Optional[int]         =logging.get_logger(__name__)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							def run_func(UpperCamelCase      ):
														@wraps(UpperCamelCase      )
														def run_in_eager_mode(*UpperCamelCase				,			**UpperCamelCase      ):
																					return func(*UpperCamelCase				,			**UpperCamelCase      )

														@wraps(UpperCamelCase      )
														@tf.function(experimental_compile=UpperCamelCase      )
														def run_in_graph_mode(*UpperCamelCase				,			**UpperCamelCase      ):
																					return func(*UpperCamelCase				,			**UpperCamelCase      )

														if do_eager_mode is True:
																					if use_xla is not False:
																												raise ValueError(
																												    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""      )
																					return run_in_eager_mode
														else:
																					return run_in_graph_mode

							return run_func





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       ["tf.Tensor"]:
							lowerCamelCase__					:	str         =       random.Random()
							lowerCamelCase__					:	Any         =       [rng.randint(0				,			vocab_size - 1      ) for i in range(batch_size * sequence_length      )]
							return tf.constant(UpperCamelCase				,			shape=(batch_size, sequence_length)				,			dtype=tf.intaa      )



class _lowercase	(				_lowercase		):
							a				      =       42
							a				      =       42
							a				      =       "TensorFlow"




							@property
							def 		lowerCamelCase_				(							self:				Dict     ):
														return tf.__version__




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														# initialize GPU on separate process
														lowerCamelCase__					:	str         =       self.args.strategy
														if strategy is None:
																					raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
														lowerCamelCase__					:	Tuple         =       self._prepare_inference_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )
														return self._measure_speed(_inference     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.args.strategy
														if strategy is None:
																					raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
														lowerCamelCase__					:	Dict         =       self._prepare_train_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )
														return self._measure_speed(_train     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														# initialize GPU on separate process
														if self.args.is_gpu:
																					tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,							UpperCamelCase__     )
														lowerCamelCase__					:	int         =       self.args.strategy
														if strategy is None:
																					raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )
														lowerCamelCase__					:	Tuple         =       self._prepare_inference_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )
														return self._measure_memory(_inference     )




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if self.args.is_gpu:
																					tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,							UpperCamelCase__     )
														lowerCamelCase__					:	Tuple         =       self.args.strategy
														if strategy is None:
																					raise ValueError("""A device strategy has to be initialized before using TensorFlow."""     )

														lowerCamelCase__					:	List[Any]         =       self._prepare_train_func(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )
														return self._measure_memory(_train     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	int         =       self.config_dict[model_name]

														if self.args.fpaa:
																					raise NotImplementedError("""Mixed precision is currently not supported."""     )

														lowerCamelCase__					:	List[str]         =       (
														    hasattr(UpperCamelCase__			,							"""architectures"""     )
														    and isinstance(config.architectures			,							UpperCamelCase__     )
														    and len(config.architectures     ) > 0
														)
														if not self.args.only_pretrain_model and has_model_class_in_config:
																					try:
																												lowerCamelCase__					:	Optional[int]         =       """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																												lowerCamelCase__					:	Optional[Any]         =       __import__("""transformers"""			,							fromlist=[model_class]     )
																												lowerCamelCase__					:	List[Any]         =       getattr(UpperCamelCase__			,							UpperCamelCase__     )
																												lowerCamelCase__					:	Optional[int]         =       model_cls(UpperCamelCase__     )
																					except ImportError:
																												raise ImportError(
																												    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																												    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
														else:
																					lowerCamelCase__					:	Union[str, Any]         =       TF_MODEL_MAPPING[config.__class__](UpperCamelCase__     )

														# encoder-decoder has vocab size saved differently
														lowerCamelCase__					:	int         =       config.vocab_size if hasattr(UpperCamelCase__			,							"""vocab_size"""     ) else config.encoder.vocab_size
														lowerCamelCase__					:	int         =       random_input_ids(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

														@run_with_tf_optimizations(self.args.eager_mode			,							self.args.use_xla     )
														def encoder_decoder_forward():
																					return model(UpperCamelCase__			,							decoder_input_ids=UpperCamelCase__			,							training=UpperCamelCase__     )

														@run_with_tf_optimizations(self.args.eager_mode			,							self.args.use_xla     )
														def encoder_forward():
																					return model(UpperCamelCase__			,							training=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

														return _inference




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Tuple         =       self.config_dict[model_name]

														if self.args.eager_mode is not False:
																					raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""     )

														if self.args.fpaa:
																					raise NotImplementedError("""Mixed precision is currently not supported."""     )

														lowerCamelCase__					:	Any         =       (
														    hasattr(UpperCamelCase__			,							"""architectures"""     )
														    and isinstance(config.architectures			,							UpperCamelCase__     )
														    and len(config.architectures     ) > 0
														)
														if not self.args.only_pretrain_model and has_model_class_in_config:
																					try:
																												lowerCamelCase__					:	List[str]         =       """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																												lowerCamelCase__					:	Tuple         =       __import__("""transformers"""			,							fromlist=[model_class]     )
																												lowerCamelCase__					:	Optional[Any]         =       getattr(UpperCamelCase__			,							UpperCamelCase__     )
																												lowerCamelCase__					:	str         =       model_cls(UpperCamelCase__     )
																					except ImportError:
																												raise ImportError(
																												    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																												    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""     )
														else:
																					lowerCamelCase__					:	List[str]         =       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__     )

														# encoder-decoder has vocab size saved differently
														lowerCamelCase__					:	Tuple         =       config.vocab_size if hasattr(UpperCamelCase__			,							"""vocab_size"""     ) else config.encoder.vocab_size
														lowerCamelCase__					:	int         =       random_input_ids(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

														@run_with_tf_optimizations(self.args.eager_mode			,							self.args.use_xla     )
														def encoder_decoder_train():
																					lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							decoder_input_ids=UpperCamelCase__			,							labels=UpperCamelCase__			,							training=UpperCamelCase__     )[0]
																					lowerCamelCase__					:	Any         =       tf.gradients(UpperCamelCase__			,							model.trainable_variables     )
																					return gradients

														@run_with_tf_optimizations(self.args.eager_mode			,							self.args.use_xla     )
														def encoder_train():
																					lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							labels=UpperCamelCase__			,							training=UpperCamelCase__     )[0]
																					lowerCamelCase__					:	Union[str, Any]         =       tf.gradients(UpperCamelCase__			,							model.trainable_variables     )
																					return gradients

														lowerCamelCase__					:	List[Any]         =       encoder_decoder_train if config.is_encoder_decoder else encoder_train

														return _train




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				int     ):
														with self.args.strategy.scope():
																					try:
																												if self.args.is_tpu or self.args.use_xla:
																																			# run additional 10 times to stabilize compilation for tpu
																																			logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""     )
																																			timeit.repeat(UpperCamelCase__			,							repeat=1			,							number=5     )

																												# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																												lowerCamelCase__					:	Tuple         =       timeit.repeat(
																												    UpperCamelCase__			,							repeat=self.args.repeat			,							number=10			,							)

																												return min(UpperCamelCase__     ) / 10.0
																					except ResourceExhaustedError as e:
																												self.print_fn(F'''Doesn\'t fit on GPU. {e}'''     )




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Callable[[], None]     ):
														logger.info(
														    """Note that TensorFlow allocates more memory than """
														    """it might need to speed up computation. """
														    """The memory reported here corresponds to the memory """
														    """reported by `nvidia-smi`, which can vary depending """
														    """on total available memory on the GPU that is used."""     )
														with self.args.strategy.scope():
																					try:
																												if self.args.trace_memory_line_by_line:
																																			if not self.args.eager_mode:
																																										raise ValueError(
																																										    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																																										    """ consumption line by line."""     )
																																			lowerCamelCase__					:	List[Any]         =       start_memory_tracing("""transformers"""     )

																												if self.args.is_tpu:
																																			# tpu
																																			raise NotImplementedError(
																																			    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																																			    """ with `args.memory=False`"""     )
																												elif self.args.is_gpu:
																																			# gpu
																																			if not is_pyanvml_available():
																																										logger.warning(
																																										    """py3nvml not installed, we won't log GPU memory usage. """
																																										    """Install py3nvml (pip install py3nvml) to log information about GPU."""     )
																																										lowerCamelCase__					:	str         =       """N/A"""
																																			else:
																																										logger.info(
																																										    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																																										    """ running on the same GPU."""     )
																																										# init nvml
																																										nvml.nvmlInit()
																																										func()
																																										lowerCamelCase__					:	List[str]         =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
																																										lowerCamelCase__					:	int         =       nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__     )
																																										lowerCamelCase__					:	int         =       meminfo.used
																																										lowerCamelCase__					:	int         =       Memory(UpperCamelCase__     )
																																										# shutdown nvml
																																										nvml.nvmlShutdown()
																												else:
																																			# cpu
																																			if self.args.trace_memory_line_by_line:
																																										logger.info(
																																										    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																																										    """ TensorFlow."""     )
																																										lowerCamelCase__					:	List[Any]         =       None
																																			else:
																																										lowerCamelCase__					:	List[str]         =       measure_peak_memory_cpu(UpperCamelCase__     )
																																										lowerCamelCase__					:	Union[str, Any]         =       Memory(UpperCamelCase__     ) if isinstance(UpperCamelCase__			,							UpperCamelCase__     ) else memory_bytes
																												if self.args.trace_memory_line_by_line:
																																			lowerCamelCase__					:	Dict         =       stop_memory_tracing(UpperCamelCase__     )
																																			if memory is None:
																																										lowerCamelCase__					:	Union[str, Any]         =       summary.total
																												else:
																																			lowerCamelCase__					:	List[str]         =       None

																												return memory, summary
																					except ResourceExhaustedError as e:
																												self.print_fn(F'''Doesn\'t fit on GPU. {e}'''     )
																												return "N/A", None



# 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


A     :	Optional[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(4_2)

A     :	List[str]    = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A     :	Optional[int]    = 'zero2'
A     :	str    = 'zero3'
A     :	Tuple    = [ZEROa, ZEROa]


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Tuple:
				# 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
				__a										=    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
A     :	Union[str, Any]    = list(itertools.product(stages, models.keys()))







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




				@parameterized.expand(_snake_case		,     name_func=_snake_case				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      int:


								'''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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    models[model]

								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = 10		,     _snake_case = 1		,     _snake_case = True		,     _snake_case = True		,     )    ->      Union[str, Any]:


								'''simple docstring'''




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

								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False				)    ->      List[str]:


								'''simple docstring'''




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