
'''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


UpperCAmelCase__   : Dict					=		logging.get_logger(__name__)






class    A     (	SCREAMING_SNAKE_CASE__    ):
	snake_case__      :PyTorchBenchmarkArguments
	snake_case__      :PretrainedConfig
	snake_case__      :str        =     "PyTorch"





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				return torch.__version__





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self._prepare_inference_func(__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				return self._measure_speed(_inference							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self._prepare_inference_func(__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				return self._measure_memory(_inference							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self._prepare_train_func(__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				return self._measure_speed(_train							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self._prepare_train_func(__magic_name__      ,							__magic_name__      ,							__magic_name__							)
				return self._measure_memory(_train							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.config_dict[model_name]

				if self.args.torchscript:
							lowerCAmelCase__								=							True

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

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

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

				def encoder_decoder_forward():
							with torch.no_grad():
										lowerCAmelCase__								=							inference_model(__magic_name__      ,							decoder_input_ids=__magic_name__							)
							return outputs

				def encoder_forward():
							with torch.no_grad():
										lowerCAmelCase__								=							inference_model(__magic_name__							)
							return outputs

				lowerCAmelCase__								=							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
				return _forward





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.config_dict[model_name]

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

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

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

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

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

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]      ,							__magic_name__			:       Tuple							):


				"""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(
										    __magic_name__      ,							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__								=							timeit.repeat(
							    __magic_name__      ,							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(__magic_name__							) / 10.0
				except RuntimeError as e:
							self.print_fn(f"""Doesn't fit on GPU. {e}"""							)
							return "N/A"







	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       Callable[[], None]							):


				"""simple docstring"""

				try:
							if self.args.trace_memory_line_by_line:
										lowerCAmelCase__								=							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."							)
													lowerCAmelCase__								=							"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__								=							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
													lowerCAmelCase__								=							nvml.nvmlDeviceGetMemoryInfo(__magic_name__							)
													lowerCAmelCase__								=							meminfo.used
													lowerCAmelCase__								=							Memory(__magic_name__							)
													# shutdown nvml
													nvml.nvmlShutdown()
							else:
										# cpu
										lowerCAmelCase__								=							measure_peak_memory_cpu(__magic_name__							)
										lowerCAmelCase__								=							Memory(__magic_name__							) if isinstance(__magic_name__      ,							__magic_name__							) else memory_bytes

							if self.args.trace_memory_line_by_line:
										lowerCAmelCase__								=							stop_memory_tracing(__magic_name__							)
							else:
										lowerCAmelCase__								=							None

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   : Any					=		{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__   : str					=		["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		UpperCAmelCase__   : int					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





