






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											=      logging.get_logger(__name__)




class 	lowerCamelCase__				(							UpperCAmelCase ):
      lowerCamelCase_							:   PyTorchBenchmarkArguments
      lowerCamelCase_							:   PretrainedConfig
      lowerCamelCase_							:   str											=	"PyTorch"






      @property
      def      UpperCAmelCase_     (self					:    int		)       ->   List[Any]:


       """simple docstring"""






       return torch.__version__






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   float:


       """simple docstring"""






       lowerCamelCase_							:    int							=     self._prepare_inference_func(_snake_case		,		_snake_case		,		_snake_case		)
       return self._measure_speed(_inference		)






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   [Memory, Optional[MemorySummary]]:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     self._prepare_inference_func(_snake_case		,		_snake_case		,		_snake_case		)
       return self._measure_memory(_inference		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   float:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     self._prepare_train_func(_snake_case		,		_snake_case		,		_snake_case		)
       return self._measure_speed(_train		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   [Memory, Optional[MemorySummary]]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     self._prepare_train_func(_snake_case		,		_snake_case		,		_snake_case		)
       return self._measure_memory(_train		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   Callable[[], None]:


       """simple docstring"""






       lowerCamelCase_							:    int							=     self.config_dict[model_name]

       if self.args.torchscript:
        lowerCamelCase_							:    Dict							=     True

       lowerCamelCase_							:    List[str]							=     (
           hasattr(_snake_case		,		'architectures'		)
           and isinstance(config.architectures		,		_snake_case		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
         lowerCamelCase_							:    Any							=     config.architectures[0]
         lowerCamelCase_							:    str							=     __import__('transformers'		,		fromlist=[model_class]		)
         lowerCamelCase_							:    Optional[Any]							=     getattr(_snake_case		,		_snake_case		)
         lowerCamelCase_							:    int							=     model_cls(_snake_case		)
        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_							:    Union[str, Any]							=     MODEL_MAPPING[config.__class__](_snake_case		)

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

       # encoder-decoder has vocab size saved differently
       lowerCamelCase_							:    Union[str, Any]							=     config.vocab_size if hasattr(_snake_case		,		'vocab_size'		) else config.encoder.vocab_size
       lowerCamelCase_							:    List[str]							=     torch.randint(_snake_case		,		(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_							:    str							=     torch.jit.trace(_snake_case		,		_snake_case		)
       else:
        lowerCamelCase_							:    List[str]							=     model

       def encoder_decoder_forward():
        with torch.no_grad():
         lowerCamelCase_							:    List[Any]							=     inference_model(_snake_case		,		decoder_input_ids=_snake_case		)
        return outputs

       def encoder_forward():
        with torch.no_grad():
         lowerCamelCase_							:    Any							=     inference_model(_snake_case		)
        return outputs

       lowerCamelCase_							:    str							=     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
       return _forward






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   Callable[[], None]:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.config_dict[model_name]

       lowerCamelCase_							:    Optional[int]							=     (
           hasattr(_snake_case		,		'architectures'		)
           and isinstance(config.architectures		,		_snake_case		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
         lowerCamelCase_							:    Union[str, Any]							=     config.architectures[0]
         lowerCamelCase_							:    Optional[Any]							=     __import__('transformers'		,		fromlist=[model_class]		)
         lowerCamelCase_							:    Any							=     getattr(_snake_case		,		_snake_case		)
         lowerCamelCase_							:    Tuple							=     model_cls(_snake_case		)
        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_							:    List[Any]							=     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_snake_case		)

       if self.args.torchscript:
        raise NotImplementedError('Training for torchscript is currently not implemented'		)
       else:
        lowerCamelCase_							:    List[str]							=     model

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

       # encoder-decoder has vocab size saved differently
       lowerCamelCase_							:    Any							=     config.vocab_size if hasattr(_snake_case		,		'vocab_size'		) else config.encoder.vocab_size
       lowerCamelCase_							:    str							=     torch.randint(_snake_case		,		(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_							:    List[str]							=     train_model(_snake_case		,		labels=_snake_case		)[0]
        loss.backward()
        return loss

       def compute_loss_and_backprob_encoder_decoder():
        lowerCamelCase_							:    Any							=     train_model(_snake_case		,		decoder_input_ids=_snake_case		,		labels=_snake_case		)[0]
        loss.backward()
        return loss

       lowerCamelCase_							:    Tuple							=     (
           compute_loss_and_backprob_encoder_decoder
           if config.is_encoder_decoder
           else compute_loss_and_backprob_encoder
       )
       return _train






      def      UpperCAmelCase_     (self					:    Optional[int]		,		_snake_case					:    Tuple		)       ->   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(
             _snake_case		,		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_							:    Optional[Any]							=     timeit.repeat(
            _snake_case		,		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(_snake_case		) / 10.0
       except RuntimeError as e:
        self.print_fn(f'Doesn\'t fit on GPU. {e}'		)
        return "N/A"






      def      UpperCAmelCase_     (self					:    int		,		_snake_case					:    Callable[[], None]		)       ->   [Memory, MemorySummary]:


       """simple docstring"""






       try:
        if self.args.trace_memory_line_by_line:
         lowerCamelCase_							:    Dict							=     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_							:    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()
          lowerCamelCase_							:    Optional[int]							=     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
          lowerCamelCase_							:    Dict							=     nvml.nvmlDeviceGetMemoryInfo(_snake_case		)
          lowerCamelCase_							:    Union[str, Any]							=     meminfo.used
          lowerCamelCase_							:    str							=     Memory(_snake_case		)
          # shutdown nvml
          nvml.nvmlShutdown()
        else:
         # cpu
         lowerCamelCase_							:    Optional[Any]							=     measure_peak_memory_cpu(_snake_case		)
         lowerCamelCase_							:    Any							=     Memory(_snake_case		) if isinstance(_snake_case		,		_snake_case		) else memory_bytes

        if self.args.trace_memory_line_by_line:
         lowerCamelCase_							:    Dict							=     stop_memory_tracing(_snake_case		)
        else:
         lowerCamelCase_							:    Union[str, Any]							=     None

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








import math




class 	lowerCamelCase__				:






      def __init__(self					:    int		,		_snake_case					:    Dict		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     len(_snake_case		)
       lowerCamelCase_							:    Optional[Any]							=     [0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1		,		0		,		self.N - 1		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    List[str]		)       ->   int:


       """simple docstring"""






       return idx * 2






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       return idx * 2 + 1






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    str		,		_snake_case					:    List[Any]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if l == r:
        lowerCamelCase_							:    List[Any]							=     A[l]
       else:
        lowerCamelCase_							:    int							=     (l + r) // 2
        self.build(self.left(_snake_case		)		,		_snake_case		,		_snake_case		)
        self.build(self.right(_snake_case		)		,		mid + 1		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Tuple		,		_snake_case					:    Any		,		_snake_case					:    str		)       ->   Tuple:


       """simple docstring"""






       return self.update_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    List[str]		,		_snake_case					:    Tuple		,		_snake_case					:    Optional[int]		,		_snake_case					:    int		,		_snake_case					:    Tuple		,		_snake_case					:    List[str]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return True
       if l == r:
        lowerCamelCase_							:    Tuple							=     val
        return True
       lowerCamelCase_							:    Optional[int]							=     (l + r) // 2
       self.update_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       self.update_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)
       return True






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[int]		)       ->   str:


       """simple docstring"""






       return self.query_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    int		,		_snake_case					:    Optional[int]		,		_snake_case					:    Optional[Any]		)       ->   str:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return -math.inf
       if l >= a and r <= b:
        return self.st[idx]
       lowerCamelCase_							:    int							=     (l + r) // 2
       lowerCamelCase_							:    Optional[Any]							=     self.query_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     self.query_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		)
       return max(_snake_case		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     []
       for i in range(1		,		N + 1		):
        show_list += [self.query(_snake_case		,		_snake_case		)]
       print(_snake_case		)


if __name__ == "__main__":
       UpperCamelCase											=      [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
       UpperCamelCase											=      1_5
       UpperCamelCase											=      SegmentTree(A)
       print(segt.query(4, 6))
       print(segt.query(7, 1_1))
       print(segt.query(7, 1_2))
       segt.update(1, 3, 1_1_1)
       print(segt.query(1, 1_5))
       segt.update(7, 8, 2_3_5)
       segt.show_data()
