
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


SCREAMING_SNAKE_CASE							:Optional[Any]											=       logging.get_logger(__name__)





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  42
	snake_case_      					=  42
	snake_case_      					=  "PyTorch"






	@property
	def        UpperCamelCase_						( self      :				Optional[int]				):
					return torch.__version__






	def        UpperCamelCase_						( self      :				str							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self._prepare_inference_func(A							,A							,A				)
					return self._measure_speed(_inference				)






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self._prepare_inference_func(A							,A							,A				)
					return self._measure_memory(_inference				)






	def        UpperCamelCase_						( self      :				Any							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self._prepare_train_func(A							,A							,A				)
					return self._measure_speed(_train				)






	def        UpperCamelCase_						( self      :				Any							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self._prepare_train_func(A							,A							,A				)
					return self._measure_memory(_train				)






	def        UpperCamelCase_						( self      :				Tuple							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self.config_dict[model_name]

					if self.args.torchscript:
									__A         =				True

					__A         =				(
					    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:
													__A         =				config.architectures[0]
													__A         =				__import__("transformers"							,fromlist=[model_class]				)
													__A         =				getattr(A							,A				)
													__A         =				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:
									__A         =				MODEL_MAPPING[config.__class__](A				)

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

					# encoder-decoder has vocab size saved differently
					__A         =				config.vocab_size if hasattr(A							,"vocab_size"				) else config.encoder.vocab_size
					__A         =				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():
													__A         =				torch.jit.trace(A							,A				)
					else:
									__A         =				model

					def encoder_decoder_forward():
									with torch.no_grad():
													__A         =				inference_model(A							,decoder_input_ids=A				)
									return outputs

					def encoder_forward():
									with torch.no_grad():
													__A         =				inference_model(A				)
									return outputs

					__A         =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
					return _forward






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				str							,A      :				int							,A      :				int				):
					__A         =				self.config_dict[model_name]

					__A         =				(
					    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:
													__A         =				config.architectures[0]
													__A         =				__import__("transformers"							,fromlist=[model_class]				)
													__A         =				getattr(A							,A				)
													__A         =				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:
									__A         =				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A				)

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

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

					# encoder-decoder has vocab size saved differently
					__A         =				config.vocab_size if hasattr(A							,"vocab_size"				) else config.encoder.vocab_size
					__A         =				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():
									__A         =				train_model(A							,labels=A				)[0]
									loss.backward()
									return loss

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

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






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				Optional[Any]				):
					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
									__A         =				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        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Callable[[], None]				):
					try:
									if self.args.trace_memory_line_by_line:
													__A         =				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."				)
																	__A         =				"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()
																	__A         =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
																	__A         =				nvml.nvmlDeviceGetMemoryInfo(A				)
																	__A         =				meminfo.used
																	__A         =				Memory(A				)
																	# shutdown nvml
																	nvml.nvmlShutdown()
									else:
													# cpu
													__A         =				measure_peak_memory_cpu(A				)
													__A         =				Memory(A				) if isinstance(A							,A				) else memory_bytes

									if self.args.trace_memory_line_by_line:
													__A         =				stop_memory_tracing(A				)
									else:
													__A         =				None

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
