






"""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


_UpperCamelCase				:    Union[str, Any]     =						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     42
    __magic_name__         =     42
    __magic_name__         =     "PyTorch"





    @property
    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return torch.__version__





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			str				=   self._prepare_inference_func(A				,		A				,		A			)
      return self._measure_speed(_inference			)





    def 				lowerCamelCase__      (							self	:					Optional[Any]				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			str				=   self._prepare_inference_func(A				,		A				,		A			)
      return self._measure_memory(_inference			)





    def 				lowerCamelCase__      (							self	:					Tuple				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			Optional[int]				=   self._prepare_train_func(A				,		A				,		A			)
      return self._measure_speed(_train			)





    def 				lowerCamelCase__      (							self	:					int				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			Any				=   self._prepare_train_func(A				,		A				,		A			)
      return self._measure_memory(_train			)





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			int				=   self.config_dict[model_name]

      if self.args.torchscript:
        a     :			Any				=   True

      a     :			Tuple				=   (
          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     :			Optional[int]				=   config.architectures[0]
          a     :			Optional[Any]				=   __import__('transformers'				,		fromlist=[model_class]			)
          a     :			Tuple				=   getattr(A				,		A			)
          a     :			int				=   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     :			Dict				=   MODEL_MAPPING[config.__class__](A			)

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

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

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

      def encoder_forward():
        with torch.no_grad():
          a     :			Optional[int]				=   inference_model(A			)
        return outputs

      a     :			Dict				=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward





    def 				lowerCamelCase__      (							self	:					Tuple				,		A	:					str				,		A	:					int				,		A	:					int			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.config_dict[model_name]

      a     :			Optional[int]				=   (
          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     :			Optional[Any]				=   config.architectures[0]
          a     :			Dict				=   __import__('transformers'				,		fromlist=[model_class]			)
          a     :			List[str]				=   getattr(A				,		A			)
          a     :			str				=   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     :			Optional[Any]				=   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A			)

      if self.args.torchscript:
        raise NotImplementedError('Training for torchscript is currently not implemented'			)
      else:
        a     :			Optional[int]				=   model

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

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

      def compute_loss_and_backprob_encoder_decoder():
        a     :			Optional[Any]				=   train_model(A				,		decoder_input_ids=A				,		labels=A			)[0]
        loss.backward()
        return loss

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





    def 				lowerCamelCase__      (							self	:					Optional[int]				,		A	:					Optional[int]			):


      '''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(
              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     :			List[Any]				=   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			) / 10.0
      except RuntimeError as e:
        self.print_fn(F'''Doesn\'t fit on GPU. {e}'''			)
        return "N/A"





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					Callable[[], None]			):


      '''simple docstring'''




      try:
        if self.args.trace_memory_line_by_line:
          a     :			int				=   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     :			List[Any]				=   '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     :			List[str]				=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
            a     :			Optional[int]				=   nvml.nvmlDeviceGetMemoryInfo(A			)
            a     :			Tuple				=   meminfo.used
            a     :			Any				=   Memory(A			)
            # shutdown nvml
            nvml.nvmlShutdown()
        else:
          # cpu
          a     :			str				=   measure_peak_memory_cpu(A			)
          a     :			Tuple				=   Memory(A			) if isinstance(A				,		A			) else memory_bytes

        if self.args.trace_memory_line_by_line:
          a     :			str				=   stop_memory_tracing(A			)
        else:
          a     :			List[Any]				=   None

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






'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							UpperCamelCase					       =			np.stack((horizontal_flow, vertical_flow)						,			2)

							# Create a grid of all pixel coordinates and subtract the flow to get the
							# target pixels coordinates
							UpperCamelCase					       =			np.stack(
							    np.meshgrid(np.arange(0						,			image.shape[1])						,			np.arange(0						,			image.shape[0]))						,			2)
							UpperCamelCase					       =			np.round(grid - flow).astype(np.intaa)

							# Find the locations outside of the original image
							UpperCamelCase					       =			(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
							UpperCamelCase					       =			0

							UpperCamelCase					       =			image[grid[:, :, 1], grid[:, :, 0]]

							# Set pixels at invalid locations to 0
							UpperCamelCase					       =			0

							return warped







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# Prepare kernels for the calculation of the derivatives and the average velocity
							UpperCamelCase					       =			np.array([[-1, 1], [-1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[-1, -1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[1, 1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array(
							    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

							# Iteratively refine the flow
							for _ in range(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# This updates the flow as proposed in the paper (Step 12)
														UpperCamelCase					       =			(
														    derivative_x * avg_horizontal_velocity
														    + derivative_y * avg_vertical_velocity
														    + derivative_t
														)
														UpperCamelCase					       =			update / (alpha**2 + derivative_x**2 + derivative_y**2)

														UpperCamelCase					       =			avg_horizontal_velocity - derivative_x * update
														UpperCamelCase					       =			avg_vertical_velocity - derivative_y * update

							return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()




