






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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    42
							A_       =    42
							A_       =    "PyTorch"

							@property
							def 					__A       (     self:      Optional[Any]      )			-> Optional[Any]:
												return torch.__version__

							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      int						,   __A:      int      )			-> float:
												_A									=       self._prepare_inference_func(__A						,   __A						,   __A      )
												return self._measure_speed(_inference      )

							def 					__A       (     self:      Tuple						,   __A:      str						,   __A:      int						,   __A:      int      )			-> [Memory, Optional[MemorySummary]]:
												_A									=       self._prepare_inference_func(__A						,   __A						,   __A      )
												return self._measure_memory(_inference      )

							def 					__A       (     self:      Optional[Any]						,   __A:      str						,   __A:      int						,   __A:      int      )			-> float:
												_A									=       self._prepare_train_func(__A						,   __A						,   __A      )
												return self._measure_speed(_train      )

							def 					__A       (     self:      List[Any]						,   __A:      str						,   __A:      int						,   __A:      int      )			-> [Memory, Optional[MemorySummary]]:
												_A									=       self._prepare_train_func(__A						,   __A						,   __A      )
												return self._measure_memory(_train      )

							def 					__A       (     self:      Tuple						,   __A:      str						,   __A:      int						,   __A:      int      )			-> Callable[[], None]:
												_A									=       self.config_dict[model_name]

												if self.args.torchscript:
																	_A									=       True

												_A									=       (
												    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:
																						_A									=       config.architectures[0]
																						_A									=       __import__('''transformers'''						,   fromlist=[model_class]      )
																						_A									=       getattr(__A						,   __A      )
																						_A									=       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:
																	_A									=       MODEL_MAPPING[config.__class__](__A      )

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

												# encoder-decoder has vocab size saved differently
												_A									=       config.vocab_size if hasattr(__A						,   '''vocab_size'''      ) else config.encoder.vocab_size
												_A									=       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():
																						_A									=       torch.jit.trace(__A						,   __A      )
												else:
																	_A									=       model

												def encoder_decoder_forward():
																	with torch.no_grad():
																						_A									=       inference_model(__A						,   decoder_input_ids=__A      )
																	return outputs

												def encoder_forward():
																	with torch.no_grad():
																						_A									=       inference_model(__A      )
																	return outputs

												_A									=       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
												return _forward

							def 					__A       (     self:      Union[str, Any]						,   __A:      str						,   __A:      int						,   __A:      int      )			-> Callable[[], None]:
												_A									=       self.config_dict[model_name]

												_A									=       (
												    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:
																						_A									=       config.architectures[0]
																						_A									=       __import__('''transformers'''						,   fromlist=[model_class]      )
																						_A									=       getattr(__A						,   __A      )
																						_A									=       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:
																	_A									=       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__A      )

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

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

												# encoder-decoder has vocab size saved differently
												_A									=       config.vocab_size if hasattr(__A						,   '''vocab_size'''      ) else config.encoder.vocab_size
												_A									=       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():
																	_A									=       train_model(__A						,   labels=__A      )[0]
																	loss.backward()
																	return loss

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

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

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

																	if self.args.trace_memory_line_by_line:
																						_A									=       stop_memory_tracing(__A      )
																	else:
																						_A									=       None

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











class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Any      )			-> None:
												# numbermap keys are the number and its values are lists of indexes sorted
												# in ascending order
												_A									=       {}
												# indexmap keys are an index and it's values are the number at that index
												_A									=       {}

							def 					__A       (     self:      Any						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_delete() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == item:
																						array.pop(__A      )
																						return array
																	elif array[mid] < item:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1
												raise ValueError(
												    '''Either the item is not in the array or the array was unsorted'''      )

							def 					__A       (     self:      int						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_insert() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == index:
																						# If the item already exists in the array,
																						# insert it after the existing item
																						array.insert(mid + 1						,   __A      )
																						return array
																	elif array[mid] < index:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
												array.insert(__A						,   __A      )
												return array

							def 					__A       (     self:      Union[str, Any]						,   __A:      int						,   __A:      int      )			-> None:
												# Remove previous index
												if index in self.indexmap:
																	_A									=       self.indexmap[index]
																	if len(self.numbermap[n]      ) == 1:
																						del self.numbermap[n]
																	else:
																						_A									=       self.binary_search_delete(self.numbermap[n]						,   __A      )

        # Set new index
												_A									=       number

												# Number not seen before or empty so insert number value
												if number not in self.numbermap:
																	_A									=       [index]

												# Here we need to perform a binary search insertion in order to insert
												# The item in the correct place
												else:
																	_A									=       self.binary_search_insert(
																	    self.numbermap[number]						,   __A      )



							def 					__A       (     self:      List[Any]						,   __A:      int      )			-> int:
												return self.numbermap.get(__A						,   [-1]      )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



