

"""simple docstring"""






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 	_lowerCAmelCase    (      __snake_case       ):

							def __init__( self       :    Tuple ,		a       :    NestedDataStructureLike[PathLike] ,		a       :    Optional[NamedSplit] = None ,		a       :    Optional[Features] = None ,		a       :    str = None ,		a       :    bool = False ,		a       :    bool = False ,		a       :    Optional[int] = None ,		**a       :    List[str] ,		)   ->    Union[str, Any]:

									"""simple docstring"""
									super().__init__(
									    a ,		split=a ,		features=a ,		cache_dir=a ,		keep_in_memory=a ,		streaming=a ,		num_proc=a ,		**a ,		)
									lowercase										=							path_or_paths if isinstance(a ,		a    ) else {self.split: path_or_paths}
									lowercase										=							Csv(
									    cache_dir=a ,		data_files=a ,		features=a ,		**a ,		)

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    List[Any]:

									"""simple docstring"""
									# Build iterable dataset
									if self.streaming:
											lowercase										=							self.builder.as_streaming_dataset(split=self.split    )
									# Build regular (map-style) dataset
									else:
											lowercase										=							None
											lowercase										=							None
											lowercase										=							None
											lowercase										=							None

											self.builder.download_and_prepare(
											    download_config=a ,		download_mode=a ,		verification_mode=a ,		base_path=a ,		num_proc=self.num_proc ,		)
											lowercase										=							self.builder.as_dataset(
											    split=self.split ,		verification_mode=a ,		in_memory=self.keep_in_memory    )
									return dataset




class 	_lowerCAmelCase    :

							def __init__( self       :    int ,		a       :    Dataset ,		a       :    Union[PathLike, BinaryIO] ,		a       :    Optional[int] = None ,		a       :    Optional[int] = None ,		**a       :    Union[str, Any] ,		)   ->    Optional[Any]:

									"""simple docstring"""
									if num_proc is not None and num_proc <= 0:
											raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""    )

									lowercase										=							dataset
									lowercase										=							path_or_buf
									lowercase										=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									lowercase										=							num_proc
									lowercase										=							'''utf-8'''
									lowercase										=							to_csv_kwargs

							def 					_lowerCAmelCase  ( self       :    str    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.to_csv_kwargs.pop('''path_or_buf''' ,		a    )
									lowercase										=							self.to_csv_kwargs.pop('''header''' ,		a    )
									lowercase										=							self.to_csv_kwargs.pop('''index''' ,		a    )

									if isinstance(self.path_or_buf ,		(str, bytes, os.PathLike)    ):
											with open(self.path_or_buf ,		'''wb+'''    ) as buffer:
													lowercase										=							self._write(file_obj=a ,		header=a ,		index=a ,		**self.to_csv_kwargs    )
									else:
											lowercase										=							self._write(file_obj=self.path_or_buf ,		header=a ,		index=a ,		**self.to_csv_kwargs    )
									return written

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Union[str, Any]    )   ->    Dict:

									"""simple docstring"""
									lowercase					,  lowercase					,  lowercase					,  lowercase										=							args

									lowercase										=							query_table(
									    table=self.dataset.data ,		key=slice(a ,		offset + self.batch_size    ) ,		indices=self.dataset._indices ,		)
									lowercase										=							batch.to_pandas().to_csv(
									    path_or_buf=a ,		header=header if (offset == 0) else False ,		index=a ,		**a    )
									return csv_str.encode(self.encoding    )

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    BinaryIO ,		a       :    List[Any] ,		a       :    Optional[Any] ,		**a       :    Optional[int]    )   ->    int:

									"""simple docstring"""
									lowercase										=							0

									if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0 ,		len(self.dataset    ) ,		self.batch_size    ) ,		unit='''ba''' ,		disable=not logging.is_progress_bar_enabled() ,		desc='''Creating CSV from Arrow format''' ,		):
													lowercase										=							self._batch_csv((offset, header, index, to_csv_kwargs)    )
													written += file_obj.write(a    )

									else:
											lowercase					,  lowercase										=							len(self.dataset    ), self.batch_size
											with multiprocessing.Pool(self.num_proc    ) as pool:
													for csv_str in logging.tqdm(
													    pool.imap(
													        self._batch_csv ,		[(offset, header, index, to_csv_kwargs) for offset in range(0 ,		a ,		a    )] ,		) ,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,		unit='''ba''' ,		disable=not logging.is_progress_bar_enabled() ,		desc='''Creating CSV from Arrow format''' ,		):
															written += file_obj.write(a    )

									return written


"""simple docstring"""






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
							import torch

							from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
							import pyanvml.pyanvml as nvml


__lowerCAmelCase				      =   logging.get_logger(__name__)
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    PyTorchBenchmarkArguments
							__lowerCAmelCase					:    PretrainedConfig
							__lowerCAmelCase					:    str     =  "PyTorch"

							@property
							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Dict:

									"""simple docstring"""
									return torch.__version__

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_speed(_inference    )

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_memory(_inference    )

							def 					_lowerCAmelCase  ( self       :    Union[str, Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_speed(_train    )

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_memory(_train    )

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									if self.args.torchscript:
											lowercase										=							True

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											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:
											lowercase										=							MODEL_MAPPING[config.__class__](a    )

									model.eval()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									if self.args.torchscript:
											with torch.no_grad():
													lowercase										=							torch.jit.trace(a ,		a    )
									else:
											lowercase										=							model

									def encoder_decoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a ,		decoder_input_ids=a    )
											return outputs

									def encoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a    )
											return outputs

									lowercase										=							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											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:
											lowercase										=							MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a    )

									if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''    )
									else:
											lowercase										=							model

									model.train()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									def compute_loss_and_backprob_encoder():
											lowercase										=							train_model(a ,		labels=a    )[0]
											loss.backward()
											return loss

									def compute_loss_and_backprob_encoder_decoder():
											lowercase										=							train_model(a ,		decoder_input_ids=a ,		labels=a    )[0]
											loss.backward()
											return loss

									lowercase										=							(
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    Optional[Any]    )   ->    float:

									"""simple docstring"""
									try:
											if self.args.is_tpu or self.args.torchscript:
													# run additional 10 times to stabilize compilation for tpu and torchscript
													logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''    )
													timeit.repeat(
													    a ,		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
											lowercase										=							timeit.repeat(
											    a ,		repeat=self.args.repeat ,		number=10 ,		)

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
													import torch_xla.debug.metrics as met

													self.print_fn(met.metrics_report()    )

											return min(a    ) / 10.0
									except RuntimeError as e:
											self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
											return "N/A"

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    Callable[[], None]    )   ->    [Memory, MemorySummary]:

									"""simple docstring"""
									try:
											if self.args.trace_memory_line_by_line:
													lowercase										=							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'''
													    ''' `--no-memory` or `args.memory=False`'''    )
											elif self.args.is_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.'''    )
															lowercase										=							'''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()
															lowercase										=							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
															lowercase										=							nvml.nvmlDeviceGetMemoryInfo(a    )
															lowercase										=							meminfo.used
															lowercase										=							Memory(a    )
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													lowercase										=							measure_peak_memory_cpu(a    )
													lowercase										=							Memory(a    ) if isinstance(a ,		a    ) else memory_bytes

											if self.args.trace_memory_line_by_line:
													lowercase										=							stop_memory_tracing(a    )
											else:
													lowercase										=							None

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