





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      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    42
							lowerCamelCase_							=    42
							lowerCamelCase_							=    "PyTorch"


							@property
							def    _snake_case     (							self			:Any						)      ->      str:



									"""simple docstring"""



									return torch.__version__


							def    _snake_case     (							self			:int							, __A			:str							, __A			:int							, __A			:int						)      ->      float:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self._prepare_inference_func(__A							, __A							, __A						)
									return self._measure_speed(_inference						)


							def    _snake_case     (							self			:Tuple							, __A			:str							, __A			:int							, __A			:int						)      ->      [Memory, Optional[MemorySummary]]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self._prepare_inference_func(__A							, __A							, __A						)
									return self._measure_memory(_inference						)


							def    _snake_case     (							self			:Dict							, __A			:str							, __A			:int							, __A			:int						)      ->      float:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self._prepare_train_func(__A							, __A							, __A						)
									return self._measure_speed(_train						)


							def    _snake_case     (							self			:str							, __A			:str							, __A			:int							, __A			:int						)      ->      [Memory, Optional[MemorySummary]]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self._prepare_train_func(__A							, __A							, __A						)
									return self._measure_memory(_train						)


							def    _snake_case     (							self			:Optional[Any]							, __A			:str							, __A			:int							, __A			:int						)      ->      Callable[[], None]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.config_dict[model_name]

									if self.args.torchscript:
											SCREAMING_SNAKE_CASE__								=					True

									SCREAMING_SNAKE_CASE__								=					(
									    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:
													SCREAMING_SNAKE_CASE__								=					config.architectures[0]
													SCREAMING_SNAKE_CASE__								=					__import__("""transformers"""							, fromlist=[model_class]						)
													SCREAMING_SNAKE_CASE__								=					getattr(__A							, __A						)
													SCREAMING_SNAKE_CASE__								=					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:
											SCREAMING_SNAKE_CASE__								=					MODEL_MAPPING[config.__class__](__A						)

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

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

									def encoder_decoder_forward():
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								=					inference_model(__A							, decoder_input_ids=__A						)
											return outputs

									def encoder_forward():
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								=					inference_model(__A						)
											return outputs

									SCREAMING_SNAKE_CASE__								=					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward


							def    _snake_case     (							self			:Union[str, Any]							, __A			:str							, __A			:int							, __A			:int						)      ->      Callable[[], None]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.config_dict[model_name]

									SCREAMING_SNAKE_CASE__								=					(
									    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:
													SCREAMING_SNAKE_CASE__								=					config.architectures[0]
													SCREAMING_SNAKE_CASE__								=					__import__("""transformers"""							, fromlist=[model_class]						)
													SCREAMING_SNAKE_CASE__								=					getattr(__A							, __A						)
													SCREAMING_SNAKE_CASE__								=					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:
											SCREAMING_SNAKE_CASE__								=					MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__A						)

									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(__A							, """vocab_size"""						) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE__								=					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():
											SCREAMING_SNAKE_CASE__								=					train_model(__A							, labels=__A						)[0]
											loss.backward()
											return loss

									def compute_loss_and_backprob_encoder_decoder():
											SCREAMING_SNAKE_CASE__								=					train_model(__A							, decoder_input_ids=__A							, labels=__A						)[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    _snake_case     (							self			:List[Any]							, __A			:Optional[int]						)      ->      float:



									"""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
											SCREAMING_SNAKE_CASE__								=					timeit.repeat(
											    __A							, 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(__A						) / 1_0.0
									except RuntimeError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''						)
											return "N/A"


							def    _snake_case     (							self			:List[Any]							, __A			:Callable[[], None]						)      ->      [Memory, MemorySummary]:



									"""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(__A						)
															SCREAMING_SNAKE_CASE__								=					meminfo.used
															SCREAMING_SNAKE_CASE__								=					Memory(__A						)
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													SCREAMING_SNAKE_CASE__								=					measure_peak_memory_cpu(__A						)
													SCREAMING_SNAKE_CASE__								=					Memory(__A						) if isinstance(__A							, __A						) else memory_bytes

											if self.args.trace_memory_line_by_line:
													SCREAMING_SNAKE_CASE__								=					stop_memory_tracing(__A						)
											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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""						)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""						)

									super().__init__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							, __A							, )
									return self.image_processor_class


							@property
							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor