

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							=logging.get_logger(__name__)



class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      __UpperCAmelCase     						= 42
      __UpperCAmelCase     						= 42
      __UpperCAmelCase     						= "PyTorch"
      @property
      def 			_lowerCAmelCase	( self	:				int							):
           return torch.__version__
      def 			_lowerCAmelCase	( self	:				Tuple     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self._prepare_inference_func(snake_case     ,snake_case     ,snake_case							)
           return self._measure_speed(_inference							)
      def 			_lowerCAmelCase	( self	:				str     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self._prepare_inference_func(snake_case     ,snake_case     ,snake_case							)
           return self._measure_memory(_inference							)
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self._prepare_train_func(snake_case     ,snake_case     ,snake_case							)
           return self._measure_speed(_train							)
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self._prepare_train_func(snake_case     ,snake_case     ,snake_case							)
           return self._measure_memory(_train							)
      def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self.config_dict[model_name]

           if self.args.torchscript:
                SCREAMING_SNAKE_CASE 		=True

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

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

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

           def encoder_decoder_forward():
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE 		=inference_model(snake_case     ,decoder_input_ids=snake_case							)
                return outputs

           def encoder_forward():
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE 		=inference_model(snake_case							)
                return outputs

           SCREAMING_SNAKE_CASE 		=encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
           return _forward
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				str     ,snake_case	:				int     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=self.config_dict[model_name]

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

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

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

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

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

           SCREAMING_SNAKE_CASE 		=(
               compute_loss_and_backprob_encoder_decoder
               if config.is_encoder_decoder
               else compute_loss_and_backprob_encoder
           )
           return _train
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				Dict							):
           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
                SCREAMING_SNAKE_CASE 		=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 			_lowerCAmelCase	( self	:				Tuple     ,snake_case	:				Callable[[], None]							):
           try:
                if self.args.trace_memory_line_by_line:
                     SCREAMING_SNAKE_CASE 		=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.'							)
                          SCREAMING_SNAKE_CASE 		='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()
                          SCREAMING_SNAKE_CASE 		=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
                          SCREAMING_SNAKE_CASE 		=nvml.nvmlDeviceGetMemoryInfo(snake_case							)
                          SCREAMING_SNAKE_CASE 		=meminfo.used
                          SCREAMING_SNAKE_CASE 		=Memory(snake_case							)
                          # shutdown nvml
                          nvml.nvmlShutdown()
                else:
                     # cpu
                     SCREAMING_SNAKE_CASE 		=measure_peak_memory_cpu(snake_case							)
                     SCREAMING_SNAKE_CASE 		=Memory(snake_case							) if isinstance(snake_case     ,snake_case							) else memory_bytes

                if self.args.trace_memory_line_by_line:
                     SCREAMING_SNAKE_CASE 		=stop_memory_tracing(snake_case							)
                else:
                     SCREAMING_SNAKE_CASE 		=None

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




from math import cos, sin







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=float(lowerCAmelCase_     )
     return [[scaling_factor * int(x == y     ) for x in range(2     )] for y in range(2     )]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     return [[c, -s], [s, c]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[c * c, cs], [cs, s * s]]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=cos(lowerCAmelCase_     ), sin(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'    {scaling(5) = }')
print(f'  {rotation(45) = }')
print(f'{projection(45) = }')



print(f'{reflection(45) = }')

