

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


_lowerCamelCase       :     Any           =							logging.get_logger(__name__)





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  42
   UpperCamelCase       =  42
   UpperCamelCase       =  "PyTorch"



   @property
   def  __magic_name__					(							self							:  List[str]		):
     return torch.__version__



   def  __magic_name__					(							self							:  Union[str, Any],	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							Tuple								=				self._prepare_inference_func(__A,	__A,	__A		)
     return self._measure_speed(_inference		)



   def  __magic_name__					(							self							:  Optional[Any],	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							Tuple								=				self._prepare_inference_func(__A,	__A,	__A		)
     return self._measure_memory(_inference		)



   def  __magic_name__					(							self							:  Tuple,	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							List[Any]								=				self._prepare_train_func(__A,	__A,	__A		)
     return self._measure_speed(_train		)



   def  __magic_name__					(							self							:  List[Any],	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							Any								=				self._prepare_train_func(__A,	__A,	__A		)
     return self._measure_memory(_train		)



   def  __magic_name__					(							self							:  Tuple,	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							List[str]								=				self.config_dict[model_name]

     if self.args.torchscript:
       UpperCAmelCase :							List[Any]								=				True

     UpperCAmelCase :							Optional[Any]								=				(
         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:
         UpperCAmelCase :							List[str]								=				config.architectures[0]
         UpperCAmelCase :							Tuple								=				__import__('''transformers''',	fromlist=[model_class]		)
         UpperCAmelCase :							str								=				getattr(__A,	__A		)
         UpperCAmelCase :							Any								=				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:
       UpperCAmelCase :							Optional[Any]								=				MODEL_MAPPING[config.__class__](__A		)

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase :							Optional[int]								=				config.vocab_size if hasattr(__A,	'''vocab_size'''		) else config.encoder.vocab_size
     UpperCAmelCase :							Tuple								=				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():
         UpperCAmelCase :							List[str]								=				torch.jit.trace(__A,	__A		)
     else:
       UpperCAmelCase :							List[str]								=				model

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

     def encoder_forward():
       with torch.no_grad():
         UpperCAmelCase :							Union[str, Any]								=				inference_model(__A		)
       return outputs

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



   def  __magic_name__					(							self							:  List[str],	__A							:  str,	__A							:  int,	__A							:  int		):
     UpperCAmelCase :							Optional[Any]								=				self.config_dict[model_name]

     UpperCAmelCase :							str								=				(
         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:
         UpperCAmelCase :							List[str]								=				config.architectures[0]
         UpperCAmelCase :							Tuple								=				__import__('''transformers''',	fromlist=[model_class]		)
         UpperCAmelCase :							Union[str, Any]								=				getattr(__A,	__A		)
         UpperCAmelCase :							Dict								=				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:
       UpperCAmelCase :							Union[str, Any]								=				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__A		)

     if self.args.torchscript:
       raise NotImplementedError('''Training for torchscript is currently not implemented'''		)
     else:
       UpperCAmelCase :							Tuple								=				model

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase :							Optional[Any]								=				config.vocab_size if hasattr(__A,	'''vocab_size'''		) else config.encoder.vocab_size
     UpperCAmelCase :							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()

     def compute_loss_and_backprob_encoder():
       UpperCAmelCase :							Tuple								=				train_model(__A,	labels=__A		)[0]
       loss.backward()
       return loss

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

     UpperCAmelCase :							Dict								=				(
         compute_loss_and_backprob_encoder_decoder
         if config.is_encoder_decoder
         else compute_loss_and_backprob_encoder
     )
     return _train



   def  __magic_name__					(							self							:  Optional[int],	__A							:  int		):
     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
       UpperCAmelCase :							int								=				timeit.repeat(
           __A,	repeat=self.args.repeat,	number=1_0,	)

       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		) / 1_0.0
     except RuntimeError as e:
       self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
       return "N/A"





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

       if self.args.trace_memory_line_by_line:
         UpperCAmelCase :							Optional[Any]								=				stop_memory_tracing(__A		)
       else:
         UpperCAmelCase :							int								=				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

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       :     Optional[int]           =							{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							Dict								=				{start}
  UpperCAmelCase :							Optional[int]								=				[start]
  UpperCAmelCase :							Queue								=				Queue()
  queue.put(UpperCAmelCase    )
  while not queue.empty():
    UpperCAmelCase :							List[Any]								=				queue.get()
    for w in graph[v]:
      if w not in explored:
        explored.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.put(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							str								=				{start}
  UpperCAmelCase :							List[str]								=				[start]
  UpperCAmelCase :							Union[str, Any]								=				deque([start]    )
  while queue:
    UpperCAmelCase :							Dict								=				queue.popleft()
    for child in graph[v]:
      if child not in visited:
        visited.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.append(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    str    )						->		None:
  UpperCAmelCase :							Optional[int]								=				f'''from __main__ import G, {name}'''
  UpperCAmelCase :							Tuple								=				10_000
  UpperCAmelCase :							Any								=				timeit(f'''{name}(G, \'A\')'''       ,				setup=UpperCAmelCase       ,				number=UpperCAmelCase    )
  print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     benchmark_function("breadth_first_search")
     benchmark_function("breadth_first_search_with_deque")
     # breadth_first_search                finished 10000 runs in 0.20999 seconds
     # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





