






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				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					PyTorchBenchmarkArguments
		UpperCAmelCase__   :					PretrainedConfig
		UpperCAmelCase__   :					str					     =    "PyTorch"


		@property
		def 	__snake_case      (    self							):
						return torch.__version__


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self._prepare_inference_func(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)
						return self._measure_speed(_inference							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Any    				= self._prepare_inference_func(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)
						return self._measure_memory(_inference							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= self._prepare_train_func(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)
						return self._measure_speed(_train							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						int    				= self._prepare_train_func(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)
						return self._measure_memory(_train							)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.config_dict[model_name]

						if self.args.torchscript:
										SCREAMING_SNAKE_CASE_ :						int    				= True

						SCREAMING_SNAKE_CASE_ :						int    				= (
						    hasattr(UpperCamelCase__			,     'architectures'							)
						    and isinstance(config.architectures			,     UpperCamelCase__							)
						    and len(config.architectures							) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
										try:
														SCREAMING_SNAKE_CASE_ :						Optional[int]    				= config.architectures[0]
														SCREAMING_SNAKE_CASE_ :						List[Any]    				= __import__('transformers'			,     fromlist=[model_class]							)
														SCREAMING_SNAKE_CASE_ :						Dict    				= getattr(UpperCamelCase__			,     UpperCamelCase__							)
														SCREAMING_SNAKE_CASE_ :						List[str]    				= model_cls(UpperCamelCase__							)
										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_ :						Any    				= MODEL_MAPPING[config.__class__](UpperCamelCase__							)

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

						# encoder-decoder has vocab size saved differently
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= config.vocab_size if hasattr(UpperCamelCase__			,     'vocab_size'							) else config.encoder.vocab_size
						SCREAMING_SNAKE_CASE_ :						Dict    				= torch.randint(UpperCamelCase__			,     (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_ :						Tuple    				= torch.jit.trace(UpperCamelCase__			,     UpperCamelCase__							)
						else:
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= model

						def encoder_decoder_forward():
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						int    				= inference_model(UpperCamelCase__			,     decoder_input_ids=UpperCamelCase__							)
										return outputs

						def encoder_forward():
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						List[Any]    				= inference_model(UpperCamelCase__							)
										return outputs

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.config_dict[model_name]

						SCREAMING_SNAKE_CASE_ :						str    				= (
						    hasattr(UpperCamelCase__			,     'architectures'							)
						    and isinstance(config.architectures			,     UpperCamelCase__							)
						    and len(config.architectures							) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
										try:
														SCREAMING_SNAKE_CASE_ :						int    				= config.architectures[0]
														SCREAMING_SNAKE_CASE_ :						Tuple    				= __import__('transformers'			,     fromlist=[model_class]							)
														SCREAMING_SNAKE_CASE_ :						Any    				= getattr(UpperCamelCase__			,     UpperCamelCase__							)
														SCREAMING_SNAKE_CASE_ :						Any    				= model_cls(UpperCamelCase__							)
										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_ :						List[str]    				= MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__							)

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

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

						# encoder-decoder has vocab size saved differently
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= config.vocab_size if hasattr(UpperCamelCase__			,     'vocab_size'							) else config.encoder.vocab_size
						SCREAMING_SNAKE_CASE_ :						Any    				= torch.randint(UpperCamelCase__			,     (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_ :						Union[str, Any]    				= train_model(UpperCamelCase__			,     labels=UpperCamelCase__							)[0]
										loss.backward()
										return loss

						def compute_loss_and_backprob_encoder_decoder():
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= train_model(UpperCamelCase__			,     decoder_input_ids=UpperCamelCase__			,     labels=UpperCamelCase__							)[0]
										loss.backward()
										return loss

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= (
						    compute_loss_and_backprob_encoder_decoder
						    if config.is_encoder_decoder
						    else compute_loss_and_backprob_encoder
						)
						return _train


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						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(
														    UpperCamelCase__			,     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_ :						str    				= timeit.repeat(
										    UpperCamelCase__			,     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(UpperCamelCase__							) / 10.0
						except RuntimeError as e:
										self.print_fn(F'''Doesn\'t fit on GPU. {e}'''							)
										return "N/A"






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						try:
										if self.args.trace_memory_line_by_line:
														SCREAMING_SNAKE_CASE_ :						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.'							)
																		SCREAMING_SNAKE_CASE_ :						List[str]    				= '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_ :						str    				= nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
																		SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__							)
																		SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= meminfo.used
																		SCREAMING_SNAKE_CASE_ :						int    				= Memory(UpperCamelCase__							)
																		# shutdown nvml
																		nvml.nvmlShutdown()
										else:
														# cpu
														SCREAMING_SNAKE_CASE_ :						Any    				= measure_peak_memory_cpu(UpperCamelCase__							)
														SCREAMING_SNAKE_CASE_ :						str    				= Memory(UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else memory_bytes

										if self.args.trace_memory_line_by_line:
														SCREAMING_SNAKE_CASE_ :						Any    				= stop_memory_tracing(UpperCamelCase__							)
										else:
														SCREAMING_SNAKE_CASE_ :						Any    				= None

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







from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.stack((horizontal_flow, vertical_flow)     ,     2   )

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

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

				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 0

				return warped



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		SupportsIndex     ,     lowerCAmelCase__					:		float | None = None     ,     ):





				'''simple docstring'''







				if alpha is None:
								SCREAMING_SNAKE_CASE_ :						Any    				= 0.1

				# Initialize flow
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.zeros_like(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Any    				= np.zeros_like(lowerCAmelCase__   )

				# Prepare kernels for the calculation of the derivatives and the average velocity
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[-1, 1], [-1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= np.array([[-1, -1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[1, 1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= 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(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= warp(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Any    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     -kernel_t   )

								SCREAMING_SNAKE_CASE_ :						List[str]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )

								# This updates the flow as proposed in the paper (Step 12)
								SCREAMING_SNAKE_CASE_ :						Tuple    				= (
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= update / (alpha**2 + derivative_x**2 + derivative_y**2)

								SCREAMING_SNAKE_CASE_ :						List[str]    				= avg_horizontal_velocity - derivative_x * update
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()