

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:    Tuple											=   logging.get_logger(__name__)



class       lowercase_		(_UpperCAmelCase   ):
						lowerCAmelCase__ 			=42
						lowerCAmelCase__ 			=42
						lowerCAmelCase__ 			="PyTorch"



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

								"""simple docstring"""

								return torch.__version__



						def   __a				( self  :      Optional[int]				,       snake_case__  :      Optional[int]				,       snake_case__  :      List[Any]				,       snake_case__  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self._prepare_inference_func(snake_case__				,       snake_case__				,       snake_case__     )
								return self._measure_speed(_inference     )



						def   __a				( self  :      str				,       snake_case__  :      Dict				,       snake_case__  :      Dict				,       snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self._prepare_inference_func(snake_case__				,       snake_case__				,       snake_case__     )
								return self._measure_memory(_inference     )



						def   __a				( self  :      str				,       snake_case__  :      Any				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self._prepare_train_func(snake_case__				,       snake_case__				,       snake_case__     )
								return self._measure_speed(_train     )



						def   __a				( self  :      List[str]				,       snake_case__  :      List[Any]				,       snake_case__  :      int				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self._prepare_train_func(snake_case__				,       snake_case__				,       snake_case__     )
								return self._measure_memory(_train     )



						def   __a				( self  :      List[str]				,       snake_case__  :      str				,       snake_case__  :      Any				,       snake_case__  :      int     ):

								"""simple docstring"""

								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   __a				( self  :      str				,       snake_case__  :      Optional[int]				,       snake_case__  :      Any				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								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   __a				( self  :      Any				,       snake_case__  :      Optional[Any]     ):

								"""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(
												    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   __a				( self  :      List[str]				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								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





SCREAMING_SNAKE_CASE:    Optional[Any]											=   [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE:    int											=   3
SCREAMING_SNAKE_CASE:    str											=   5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE:    list[float]											=   []
SCREAMING_SNAKE_CASE:    list[float]											=   []

# Machine Time
SCREAMING_SNAKE_CASE:    str											=   0


def 				_a       (				lowerCAmelCase   )->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(lowerCAmelCase   ):
				# Evolution Parameter
				SCREAMING_SNAKE_CASE_							=       float(seed / value   )

				# Control Theory: Orbit Change
				SCREAMING_SNAKE_CASE_							=       (buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				SCREAMING_SNAKE_CASE_							=       (params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       r  # Saving to Parameters Space

		# Logistic Map
		assert max(lowerCAmelCase   ) < 1
		assert max(lowerCAmelCase   ) < 4

		# Machine Time
		machine_time += 1


def 				_a       (				)->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(lowerCAmelCase						,	lowerCAmelCase   ):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		SCREAMING_SNAKE_CASE_							=       machine_time % m

		# Evolution (Time Length)
		for _ in range(0						,	lowerCAmelCase   ):
				# Variables (Position + Parameters)
				SCREAMING_SNAKE_CASE_							=       params_space[key]
				SCREAMING_SNAKE_CASE_							=       buffer_space[key]

				# Modification (Transition Function) - Flow
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

		# Choosing Chaotic Data
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key + 2) % m] * (10**10)   )
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key - 2) % m] * (10**10)   )

		# Machine Time
		machine_time += 1

		return xorshift(lowerCAmelCase						,	lowerCAmelCase   ) % 0xFFFF_FFFF


def 				_a       (				)->  Optional[Any]:
		global buffer_space, params_space, machine_time, K, m, t

		SCREAMING_SNAKE_CASE_							=       K
		SCREAMING_SNAKE_CASE_							=       [0] * m
		SCREAMING_SNAKE_CASE_							=       0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						SCREAMING_SNAKE_CASE:    List[Any]											=   random.sample(range(0xff_ff_ff_ff), 1_0_0)
						for chunk in message:
												push(chunk)

						# for controlling
						SCREAMING_SNAKE_CASE:    List[str]											=   ''''''

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f"""{format(pull(), '#04x')}""")
												print(buffer_space)
												print(params_space)
												SCREAMING_SNAKE_CASE:    List[Any]											=   input('''(e)exit? ''').strip()