

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    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    42
			__lowerCamelCase	    =    42
			__lowerCamelCase	    =    'PyTorch'


			@property
			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							return torch.__version__


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  float:
							'''simple docstring'''






							A__            =     self._prepare_inference_func(lowercase , lowercase , lowercase    )
							return self._measure_speed(_inference    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  [Memory, Optional[MemorySummary]]:
							'''simple docstring'''






							A__            =     self._prepare_inference_func(lowercase , lowercase , lowercase    )
							return self._measure_memory(_inference    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  float:
							'''simple docstring'''






							A__            =     self._prepare_train_func(lowercase , lowercase , lowercase    )
							return self._measure_speed(_train    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  [Memory, Optional[MemorySummary]]:
							'''simple docstring'''






							A__            =     self._prepare_train_func(lowercase , lowercase , lowercase    )
							return self._measure_memory(_train    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  Callable[[], None]:
							'''simple docstring'''






							A__            =     self.config_dict[model_name]

							if self.args.torchscript:
											A__            =     True

							A__            =     (
							    hasattr(lowercase , "architectures"    )
							    and isinstance(config.architectures , lowercase    )
							    and len(config.architectures    ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
															A__            =     config.architectures[0]
															A__            =     __import__("transformers" , fromlist=[model_class]    )
															A__            =     getattr(lowercase , lowercase    )
															A__            =     model_cls(lowercase    )
											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__            =     MODEL_MAPPING[config.__class__](lowercase    )

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

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

							def encoder_decoder_forward():
											with torch.no_grad():
															A__            =     inference_model(lowercase , decoder_input_ids=lowercase    )
											return outputs

							def encoder_forward():
											with torch.no_grad():
															A__            =     inference_model(lowercase    )
											return outputs

							A__            =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
							return _forward


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  Callable[[], None]:
							'''simple docstring'''






							A__            =     self.config_dict[model_name]

							A__            =     (
							    hasattr(lowercase , "architectures"    )
							    and isinstance(config.architectures , lowercase    )
							    and len(config.architectures    ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
															A__            =     config.architectures[0]
															A__            =     __import__("transformers" , fromlist=[model_class]    )
															A__            =     getattr(lowercase , lowercase    )
															A__            =     model_cls(lowercase    )
											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__            =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase    )

							if self.args.torchscript:
											raise NotImplementedError("Training for torchscript is currently not implemented"    )
							else:
											A__            =     model

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

							# encoder-decoder has vocab size saved differently
							A__            =     config.vocab_size if hasattr(lowercase , "vocab_size"    ) else config.encoder.vocab_size
							A__            =     torch.randint(lowercase , (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__            =     train_model(lowercase , labels=lowercase    )[0]
											loss.backward()
											return loss

							def compute_loss_and_backprob_encoder_decoder():
											A__            =     train_model(lowercase , decoder_input_ids=lowercase , labels=lowercase    )[0]
											loss.backward()
											return loss

							A__            =     (
							    compute_loss_and_backprob_encoder_decoder
							    if config.is_encoder_decoder
							    else compute_loss_and_backprob_encoder
							)
							return _train


			def        UpperCamelCase						(      self , lowercase    )					->  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(
															    lowercase , 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__            =     timeit.repeat(
											    lowercase , 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(lowercase    ) / 10.0
							except RuntimeError as e:
											self.print_fn(F'Doesn\'t fit on GPU. {e}'    )
											return "N/A"


			def        UpperCamelCase						(      self , lowercase    )					->  [Memory, MemorySummary]:
							'''simple docstring'''






							try:
											if self.args.trace_memory_line_by_line:
															A__            =     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__            =     "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__            =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
																			A__            =     nvml.nvmlDeviceGetMemoryInfo(lowercase    )
																			A__            =     meminfo.used
																			A__            =     Memory(lowercase    )
																			# shutdown nvml
																			nvml.nvmlShutdown()
											else:
															# cpu
															A__            =     measure_peak_memory_cpu(lowercase    )
															A__            =     Memory(lowercase    ) if isinstance(lowercase , lowercase    ) else memory_bytes

											if self.args.trace_memory_line_by_line:
															A__            =     stop_memory_tracing(lowercase    )
											else:
															A__            =     None

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



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

