

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


_SCREAMING_SNAKE_CASE       :  Optional[Any]        = logging.get_logger(__name__)

class     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						42
			SCREAMING_SNAKE_CASE_						     =						42
			SCREAMING_SNAKE_CASE_						     =						'PyTorch'






			@property
			def    _lowerCAmelCase			(							self     :					List[Any]						):
								return torch.__version__






			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					Any						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					List[Any]						):
								UpperCamelCase__		:    List[str]         =self._prepare_inference_func(__lowerCamelCase						,  __lowerCamelCase						,  __lowerCamelCase						)
								return self._measure_speed(_inference						)






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					str						,  lowercase_     :					int						,  lowercase_     :					str						):
								UpperCamelCase__		:    List[Any]         =self._prepare_inference_func(__lowerCamelCase						,  __lowerCamelCase						,  __lowerCamelCase						)
								return self._measure_memory(_inference						)






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					List[str]						,  lowercase_     :					List[str]						,  lowercase_     :					Union[str, Any]						):
								UpperCamelCase__		:    Union[str, Any]         =self._prepare_train_func(__lowerCamelCase						,  __lowerCamelCase						,  __lowerCamelCase						)
								return self._measure_speed(_train						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					List[Any]						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					Dict						):
								UpperCamelCase__		:    Union[str, Any]         =self._prepare_train_func(__lowerCamelCase						,  __lowerCamelCase						,  __lowerCamelCase						)
								return self._measure_memory(_train						)






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					Optional[int]						,  lowercase_     :					int						,  lowercase_     :					Any						):
								UpperCamelCase__		:    Union[str, Any]         =self.config_dict[model_name]

								if self.args.torchscript:
													UpperCamelCase__		:    Tuple         =True

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

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

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

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

								def encoder_forward():
													with torch.no_grad():
																		UpperCamelCase__		:    Tuple         =inference_model(__lowerCamelCase						)
													return outputs

								UpperCamelCase__		:    List[str]         =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Optional[int]						,  lowercase_     :					int						,  lowercase_     :					Dict						):
								UpperCamelCase__		:    Dict         =self.config_dict[model_name]

								UpperCamelCase__		:    Tuple         =(
								    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:
																		UpperCamelCase__		:    List[Any]         =config.architectures[0]
																		UpperCamelCase__		:    Optional[int]         =__import__('''transformers'''						,  fromlist=[model_class]						)
																		UpperCamelCase__		:    List[Any]         =getattr(__lowerCamelCase						,  __lowerCamelCase						)
																		UpperCamelCase__		:    Optional[int]         =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:
													UpperCamelCase__		:    Optional[Any]         =MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCamelCase						)

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

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

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

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

								UpperCamelCase__		:    List[Any]         =(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train






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

													if self.args.trace_memory_line_by_line:
																		UpperCamelCase__		:    Dict         =stop_memory_tracing(__lowerCamelCase						)
													else:
																		UpperCamelCase__		:    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"""





def        _lowerCAmelCase      (						UpperCAmelCase     :							list[list[int]]				):


					'''simple docstring'''






					UpperCamelCase__		:    Dict         =[list(UpperCAmelCase				) for row in matrix]
					if matrix and isinstance(UpperCAmelCase      ,							UpperCAmelCase				):
										if isinstance(matrix[0]      ,							UpperCAmelCase				):
															UpperCamelCase__		:    List[Any]         =0
															for row in matrix:
																				if prev_len == 0:
																									UpperCamelCase__		:    Optional[Any]         =len(UpperCAmelCase				)
																									UpperCamelCase__		:    Optional[int]         =True
																				else:
																									UpperCamelCase__		:    List[str]         =prev_len == len(UpperCAmelCase				)
										else:
															UpperCamelCase__		:    Tuple         =True
					else:
										UpperCamelCase__		:    Optional[Any]         =False

					return result



def        _lowerCAmelCase      (						UpperCAmelCase     :							list[list[int]]				):


					'''simple docstring'''






					if check_matrix(UpperCAmelCase				) and len(UpperCAmelCase				) > 0:
										UpperCamelCase__		:    Optional[int]         =[list(UpperCAmelCase				) for row in a]
										UpperCamelCase__		:    Any         =len(UpperCAmelCase				)
										if isinstance(a[0]      ,							UpperCAmelCase				):
															UpperCamelCase__		:    Tuple         =len(a[0]				)
										else:
															for dat in a:
																				print(UpperCAmelCase				)
															return

										# horizotal printing increasing
										for i in range(0      ,							UpperCAmelCase				):
															print(a[0][i]				)
										# vertical printing down
										for i in range(1      ,							UpperCAmelCase				):
															print(a[i][mat_col - 1]				)
										# horizotal printing decreasing
										if mat_row > 1:
															for i in range(mat_col - 2      ,							-1      ,							-1				):
																				print(a[mat_row - 1][i]				)
        # vertical printing up
										for i in range(mat_row - 2      ,							0      ,							-1				):
															print(a[i][0]				)
										UpperCamelCase__		:    Tuple         =[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
										if len(UpperCAmelCase				) > 0:
															spiral_print_clockwise(UpperCAmelCase				)
										else:
															return
					else:
										print('''Not a valid matrix'''				)
										return



def        _lowerCAmelCase      (						UpperCAmelCase     :							list[list]				):


					'''simple docstring'''






					if matrix:
										return list(matrix.pop(0				)				) + spiral_traversal(list(zip(*UpperCAmelCase				)				)[::-1]				)
					else:
										return []


# driver code
if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_SCREAMING_SNAKE_CASE       :  int        = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 1_0, 1_1, 1_2]]
				spiral_print_clockwise(a)


