




'''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


A				: Union[str, Any]					      =			logging.get_logger(__name__)






class 			SCREAMING_SNAKE_CASE(							__A	):
       snake_case_					:	PyTorchBenchmarkArguments
       snake_case_					:	PretrainedConfig
       snake_case_					:	str       =   "PyTorch"

       @property
       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           return torch.__version__

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   float:

           """simple docstring"""



           __lowercase     							=		self._prepare_inference_func(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)
           return self._measure_speed(_inference	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   [Memory, Optional[MemorySummary]]:

           """simple docstring"""



           __lowercase     							=		self._prepare_inference_func(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)
           return self._measure_memory(_inference	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   float:

           """simple docstring"""



           __lowercase     							=		self._prepare_train_func(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)
           return self._measure_speed(_train	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   [Memory, Optional[MemorySummary]]:

           """simple docstring"""



           __lowercase     							=		self._prepare_train_func(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)
           return self._measure_memory(_train	)

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Callable[[], None]:

           """simple docstring"""



           __lowercase     							=		self.config_dict[model_name]

           if self.args.torchscript:
               __lowercase     							=		True

           __lowercase     							=		(
               hasattr(lowerCamelCase__   ,						"""architectures"""	)
               and isinstance(config.architectures   ,						lowerCamelCase__	)
               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(lowerCamelCase__   ,						lowerCamelCase__	)
                   __lowercase     							=		model_cls(lowerCamelCase__	)
               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__](lowerCamelCase__	)

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

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

           def encoder_decoder_forward():
               with torch.no_grad():
                   __lowercase     							=		inference_model(lowerCamelCase__   ,						decoder_input_ids=lowerCamelCase__	)
               return outputs

           def encoder_forward():
               with torch.no_grad():
                   __lowercase     							=		inference_model(lowerCamelCase__	)
               return outputs

           __lowercase     							=		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
           return _forward

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Callable[[], None]:

           """simple docstring"""



           __lowercase     							=		self.config_dict[model_name]

           __lowercase     							=		(
               hasattr(lowerCamelCase__   ,						"""architectures"""	)
               and isinstance(config.architectures   ,						lowerCamelCase__	)
               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(lowerCamelCase__   ,						lowerCamelCase__	)
                   __lowercase     							=		model_cls(lowerCamelCase__	)
               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__](lowerCamelCase__	)

           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(lowerCamelCase__   ,						"""vocab_size"""	) else config.encoder.vocab_size
           __lowercase     							=		torch.randint(lowerCamelCase__   ,						(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(lowerCamelCase__   ,						labels=lowerCamelCase__	)[0]
               loss.backward()
               return loss

           def compute_loss_and_backprob_encoder_decoder():
               __lowercase     							=		train_model(lowerCamelCase__   ,						decoder_input_ids=lowerCamelCase__   ,						labels=lowerCamelCase__	)[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        snake_case__     (       self   ,						lowerCamelCase__	)       ->   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(
                       lowerCamelCase__   ,						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(
                   lowerCamelCase__   ,						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(lowerCamelCase__	) / 10.0
           except RuntimeError as e:
               self.print_fn(F'Doesn\'t fit on GPU. {e}'	)
               return "N/A"




       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   [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(lowerCamelCase__	)
                       __lowercase     							=		meminfo.used
                       __lowercase     							=		Memory(lowerCamelCase__	)
                       # shutdown nvml
                       nvml.nvmlShutdown()
               else:
                   # cpu
                   __lowercase     							=		measure_peak_memory_cpu(lowerCamelCase__	)
                   __lowercase     							=		Memory(lowerCamelCase__	) if isinstance(lowerCamelCase__   ,						lowerCamelCase__	) else memory_bytes

               if self.args.trace_memory_line_by_line:
                   __lowercase     							=		stop_memory_tracing(lowerCamelCase__	)
               else:
                   __lowercase     							=		None

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










'''simple docstring'''
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		(boundary[1] - boundary[0]) / steps
    __lowercase     							=		boundary[0]
    __lowercase     							=		boundary[1]
    __lowercase     							=		make_points(a__       ,a__       ,a__     )
    __lowercase     							=		0.0
    y += (h / 3.0) * f(a__     )
    __lowercase     							=		2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a__     )
        cnt += 1
    y += (h / 3.0) * f(a__     )
    return y
def 							snake_case_    (  a__						:      Optional[Any]       ,a__						:      List[str]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		a + h
    while x < (b - h):
        yield x
        __lowercase     							=		x + h
def 							snake_case_    (  a__						:      List[Any]     ):  # enter your function here



    """simple docstring"""




    __lowercase     							=		(x - 0) * (x - 0)
    return y
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		0.0  # Lower bound of integration
    __lowercase     							=		1.0  # Upper bound of integration
    __lowercase     							=		1_0.0  # define number of steps or resolution
    __lowercase     							=		[a, b]  # define boundary of integration
    __lowercase     							=		method_a(a__       ,a__     )
    print(f'y = {y}'     )


if __name__ == "__main__":
  main()




