

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


a_         =      logging.get_logger(__name__)






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =42
			lowerCamelCase__          =42
			lowerCamelCase__          ="PyTorch"
			@property
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Any:

				"""simple docstring"""

				return torch.__version__
			def 							__UpperCamelCase	(     self							:	Any , a							:	str , a							:	int , a							:	int		)							->    float:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    self._prepare_inference_func(a , a , a		)
				return self._measure_speed(_inference		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	str , a							:	int , a							:	int		)							->    [Memory, Optional[MemorySummary]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self._prepare_inference_func(a , a , a		)
				return self._measure_memory(_inference		)
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	str , a							:	int , a							:	int		)							->    float:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    self._prepare_train_func(a , a , a		)
				return self._measure_speed(_train		)
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	str , a							:	int , a							:	int		)							->    [Memory, Optional[MemorySummary]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self._prepare_train_func(a , a , a		)
				return self._measure_memory(_train		)
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	str , a							:	int , a							:	int		)							->    Callable[[], None]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.config_dict[model_name]

				if self.args.torchscript:
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    (
				    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:
						SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    config.architectures[0]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    __import__("transformers" , fromlist=[model_class]		)
						SCREAMING_SNAKE_CASE				: Any			       =    getattr(a , a		)
						SCREAMING_SNAKE_CASE				: Tuple			       =    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:
					SCREAMING_SNAKE_CASE				: List[Any]			       =    MODEL_MAPPING[config.__class__](a		)

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

				# encoder-decoder has vocab size saved differently
				SCREAMING_SNAKE_CASE				: List[str]			       =    config.vocab_size if hasattr(a , "vocab_size"		) else config.encoder.vocab_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    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():
						SCREAMING_SNAKE_CASE				: Any			       =    torch.jit.trace(a , a		)
				else:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    model

				def encoder_decoder_forward():
					with torch.no_grad():
						SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    inference_model(a , decoder_input_ids=a		)
					return outputs

				def encoder_forward():
					with torch.no_grad():
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    inference_model(a		)
					return outputs

				SCREAMING_SNAKE_CASE				: Dict			       =    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
				return _forward
			def 							__UpperCamelCase	(     self							:	Dict , a							:	str , a							:	int , a							:	int		)							->    Callable[[], None]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.config_dict[model_name]

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    (
				    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:
						SCREAMING_SNAKE_CASE				: List[Any]			       =    config.architectures[0]
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    __import__("transformers" , fromlist=[model_class]		)
						SCREAMING_SNAKE_CASE				: str			       =    getattr(a , a		)
						SCREAMING_SNAKE_CASE				: str			       =    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:
					SCREAMING_SNAKE_CASE				: Any			       =    MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a		)

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

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

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

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

				SCREAMING_SNAKE_CASE				: List[str]			       =    (
				    compute_loss_and_backprob_encoder_decoder
				    if config.is_encoder_decoder
				    else compute_loss_and_backprob_encoder
				)
				return _train
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , 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
					SCREAMING_SNAKE_CASE				: Any			       =    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]		)							->    [Memory, MemorySummary]:

				"""simple docstring"""

				try:
					if self.args.trace_memory_line_by_line:
						SCREAMING_SNAKE_CASE				: Union[str, 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"
						    " `--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."		)
							SCREAMING_SNAKE_CASE				: 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()
							SCREAMING_SNAKE_CASE				: Optional[int]			       =    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
							SCREAMING_SNAKE_CASE				: Dict			       =    nvml.nvmlDeviceGetMemoryInfo(a		)
							SCREAMING_SNAKE_CASE				: List[Any]			       =    meminfo.used
							SCREAMING_SNAKE_CASE				: Tuple			       =    Memory(a		)
							# shutdown nvml
							nvml.nvmlShutdown()
					else:
						# cpu
						SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    measure_peak_memory_cpu(a		)
						SCREAMING_SNAKE_CASE				: Any			       =    Memory(a		) if isinstance(a , a		) else memory_bytes

					if self.args.trace_memory_line_by_line:
						SCREAMING_SNAKE_CASE				: Tuple			       =    stop_memory_tracing(a		)
					else:
						SCREAMING_SNAKE_CASE				: List[str]			       =    None

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


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)