
'''simple docstring'''



from typing import Dict, Optional, Tuple

import torch





def  _A		(				lowercase__	):
						lowercase__							 =       boundaries[1] - boundaries[0]
						lowercase__							 =       boundaries + step / 2
						lowercase__							 =       torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1	)]		,		dim=0	)
						return bin_centers





def  _A		(				lowercase__		,		lowercase__		,		):
						lowercase__							 =       _calculate_bin_centers(snake_case__	)
						return (
						    torch.sum(aligned_distance_error_probs * bin_centers		,		dim=-1	),
						    bin_centers[-1],
						)





def  _A		(				lowercase__		,		lowercase__ = 31		,		lowercase__ = 64		,		**lowercase__		,		):
						lowercase__							 =       torch.linspace(0		,		snake_case__		,		steps=(no_bins - 1)		,		device=logits.device	)

						lowercase__							 =       torch.nn.functional.softmax(snake_case__		,		dim=-1	)
						lowercase__ ,						lowercase__							 =       _calculate_expected_aligned_error(
						    alignment_confidence_breaks=snake_case__		,		aligned_distance_error_probs=snake_case__		,		)

						return {
						    "aligned_confidence_probs": aligned_confidence_probs,
						    "predicted_aligned_error": predicted_aligned_error,
						    "max_predicted_aligned_error": max_predicted_aligned_error,
						}







def  _A		(				lowercase__		,		lowercase__ = None		,		lowercase__ = 31		,		lowercase__ = 64		,		lowercase__ = 1e-8		,		**lowercase__		,		):
						if residue_weights is None:
												lowercase__							 =       logits.new_ones(logits.shape[-2]	)

						lowercase__							 =       torch.linspace(0		,		snake_case__		,		steps=(no_bins - 1)		,		device=logits.device	)

						lowercase__							 =       _calculate_bin_centers(snake_case__	)
						torch.sum(snake_case__	)
						lowercase__							 =       logits.shape[-2]
						lowercase__							 =       max(snake_case__		,		19	)

						lowercase__							 =       1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

						lowercase__							 =       torch.nn.functional.softmax(snake_case__		,		dim=-1	)

						lowercase__							 =       1.0 / (1 + (bin_centers**2) / (da**2))
						lowercase__							 =       torch.sum(probs * tm_per_bin		,		dim=-1	)

						lowercase__							 =       residue_weights / (eps + residue_weights.sum())
						lowercase__							 =       torch.sum(predicted_tm_term * normed_residue_mask		,		dim=-1	)

						lowercase__							 =       per_alignment * residue_weights

						lowercase__							 =       (weighted == torch.max(snake_case__	)).nonzero()[0]
						return per_alignment[tuple(snake_case__	)]








'''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          =    logging.get_logger(__name__)





class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							PyTorchBenchmarkArguments
			lowerCamelCase							:							PretrainedConfig
			lowerCamelCase							:							str       		=  "PyTorch"



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return torch.__version__



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




									'''simple docstring'''



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



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




									'''simple docstring'''



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



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




									'''simple docstring'''



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



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




									'''simple docstring'''



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



			def A__ (					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 A__ (					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 A__ (					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 A__ (					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






