




"""simple docstring"""

import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def       snake_case__	(       lowerCAmelCase   :		Union[str, Any]				,					lowerCAmelCase   :		Dict=False				,					lowerCAmelCase   :		int=False				,					lowerCAmelCase   :		Optional[int]="data2vec."		):
					__snake_case          =	"""backbone.""" if is_semantic else """"""

					__snake_case          =	[]
					for i in range(config.num_hidden_layers		):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
										rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
										rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
										rename_keys.append(
										    (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
										rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
										rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
					        (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
					        (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
					    ]		)

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ("""mask_token""", F'''{hf_prefix}embeddings.mask_token'''),
										        (
										            """rel_pos_bias.relative_position_bias_table""",
										            F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
										        ),
										        (
										            """rel_pos_bias.relative_position_index""",
										            F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
										        ),
										        ("""norm.weight""", """layernorm.weight"""),
										        ("""norm.bias""", """layernorm.bias"""),
										    ]		)
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
										        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
										        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
										        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
										    ]		)
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("""fc_norm.weight""", F'''{hf_prefix}pooler.layernorm.weight'''),
										        ("""fc_norm.bias""", F'''{hf_prefix}pooler.layernorm.bias'''),
										        ("""head.weight""", """classifier.weight"""),
										        ("""head.bias""", """classifier.bias"""),
										    ]		)

					return rename_keys




def       snake_case__	(       lowerCAmelCase   :		Union[str, Any]				,					lowerCAmelCase   :		str				,					lowerCAmelCase   :		int=False				,					lowerCAmelCase   :		Optional[int]=False				,					lowerCAmelCase   :		str="data2vec_vision."		):
					for i in range(config.num_hidden_layers		):
										__snake_case          =	"""backbone.""" if is_semantic else """"""
										# queries, keys and values
										__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
										__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
										__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

										__snake_case          =	in_proj_weight[
										    : config.hidden_size, :
										]
										__snake_case          =	q_bias
										__snake_case          =	in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__snake_case          =	in_proj_weight[
										    -config.hidden_size :, :
										]
										__snake_case          =	v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
										__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

										__snake_case          =	gamma_a
										__snake_case          =	gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
															__snake_case          =	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

															__snake_case          =	table
															__snake_case          =	index




def       snake_case__	(       ):
					__snake_case          =	argparse.ArgumentParser(
					    """Convert Data2VecVision to HF for image classification and pretraining"""				,					add_help=lowerCAmelCase		)
					parser.add_argument("""--hf_checkpoint_name"""				,					type=lowerCAmelCase		)
					parser.add_argument("""--input_size"""				,					default=224				,					type=lowerCAmelCase				,					help="""images input size"""		)
					parser.add_argument("""--beit_checkpoint"""				,					default=""""""				,					help="""beit checkpoint"""		)

					return parser.parse_args()




def       snake_case__	(       lowerCAmelCase   :		List[Any]				,					lowerCAmelCase   :		Dict				,					lowerCAmelCase   :		Tuple		):
					def load_state_dict(lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		Optional[Any]				,					lowerCAmelCase   :		str=""				,					lowerCAmelCase   :		Tuple="relative_position_index"		):
										__snake_case          =	[]
										__snake_case          =	[]
										__snake_case          =	[]
										# copy state_dict so _load_from_state_dict can modify it
										__snake_case          =	getattr(lowerCAmelCase				,					"""_metadata"""				,					lowerCAmelCase		)
										__snake_case          =	state_dict.copy()
										if metadata is not None:
															__snake_case          =	metadata

										def load(lowerCAmelCase   :		Tuple				,					lowerCAmelCase   :		Optional[int]=""		):
															__snake_case          =	{} if metadata is None else metadata.get(prefix[:-1]				,					{}		)
															module._load_from_state_dict(
															    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
															for name, child in module._modules.items():
																				if child is not None:
																									load(lowerCAmelCase				,					prefix + name + """."""		)

										load(lowerCAmelCase				,					prefix=lowerCAmelCase		)

										__snake_case          =	[]
										__snake_case          =	[]
										for key in missing_keys:
															__snake_case          =	True
															for ignore_key in ignore_missing.split("""|"""		):
																				if ignore_key in key:
																									__snake_case          =	False
																									break
															if keep_flag:
																				warn_missing_keys.append(lowerCAmelCase		)
															else:
																				ignore_missing_keys.append(lowerCAmelCase		)

										__snake_case          =	warn_missing_keys

										if len(lowerCAmelCase		) > 0:
															print(
															    """Weights of {} not initialized from pretrained model: {}""".format(
															        model.__class__.__name__				,					lowerCAmelCase		)		)
										if len(lowerCAmelCase		) > 0:
															print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				,					lowerCAmelCase		)		)
										if len(lowerCAmelCase		) > 0:
															print(
															    """Ignored weights of {} not initialized from pretrained model: {}""".format(
															        model.__class__.__name__				,					lowerCAmelCase		)		)
										if len(lowerCAmelCase		) > 0:
															print("""\n""".join(lowerCAmelCase		)		)

					__snake_case          =	{
					    """pretrained""": False,
					    """use_shared_rel_pos_bias""": True,
					    """use_abs_pos_emb""": False,
					    """init_values""": 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        """num_classes""": 1_000,
										        """use_mean_pooling""": True,
										        """init_scale""": 0.0_0_1,
										        """use_rel_pos_bias""": True,
										    }		)

					__snake_case          =	create_model(
					    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				,					**lowerCAmelCase				,					)
					__snake_case          =	model.patch_embed.patch_size
					__snake_case          =	(args.input_size // patch_size[0], args.input_size // patch_size[1])
					__snake_case          =	torch.load(args.beit_checkpoint				,					map_location="""cpu"""		)

					print(F'''Load ckpt from {args.beit_checkpoint}'''		)
					__snake_case          =	None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															__snake_case          =	checkpoint[model_key]
															print(F'''Load state_dict by model_key = {model_key}'''		)
															break

					__snake_case          =	list(checkpoint_model.keys()		)
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(lowerCAmelCase		)

										if "relative_position_bias_table" in key:
															__snake_case          =	checkpoint_model[key]
															__snake_case      ,      __snake_case          =	rel_pos_bias.size()
															__snake_case      ,      __snake_case          =	model.state_dict()[key].size()
															__snake_case          =	model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(lowerCAmelCase				,					lowerCAmelCase				,					prefix=""""""		)

					return model




def       snake_case__	(       ):
					__snake_case          =	get_args()

					__snake_case          =	"""ft1k""" in args.hf_checkpoint_name
					__snake_case          =	"""large""" in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					__snake_case          =	DataaVecVisionConfig()
					if is_finetuned:
										__snake_case          =	True
										__snake_case          =	False
										__snake_case          =	True
										__snake_case          =	1_000

										__snake_case          =	"""huggingface/label-files"""
										__snake_case          =	"""imagenet-1k-id2label.json"""
										__snake_case          =	json.load(open(hf_hub_download(lowerCAmelCase				,					lowerCAmelCase				,					repo_type="""dataset"""		)				,					"""r"""		)		)
										__snake_case          =	{int(lowerCAmelCase		): v for k, v in idalabel.items()}
										__snake_case          =	idalabel
										__snake_case          =	{v: k for k, v in idalabel.items()}
					else:
										__snake_case          =	False
										__snake_case          =	True
										__snake_case          =	False

					if is_large:
										__snake_case          =	1_024
										__snake_case          =	4_096
										__snake_case          =	24
										__snake_case          =	16

					# 2. Load Beit model
					__snake_case          =	load_beit_model(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase		)
					orig_model.eval()

					# 3. Forward Beit model
					__snake_case          =	BeitImageProcessor(size=config.image_size				,					do_center_crop=lowerCAmelCase		)
					__snake_case          =	Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""		)
					__snake_case          =	image_processor(images=lowerCAmelCase				,					return_tensors="""pt"""		)
					__snake_case          =	encoding["""pixel_values"""]

					__snake_case          =	(pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__snake_case          =	orig_model(*lowerCAmelCase		)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__snake_case          =	DataaVecVisionForImageClassification(lowerCAmelCase		)
										hf_model.eval()
										__snake_case          =	False
										__snake_case          =	"""data2vec_vision."""
					else:
										__snake_case          =	DataaVecVisionModel(lowerCAmelCase		)
										hf_model.eval()
										__snake_case          =	True
										__snake_case          =	""""""

					__snake_case          =	create_rename_keys(lowerCAmelCase				,					hf_prefix=lowerCAmelCase				,					has_lm_head=lowerCAmelCase		)
					__snake_case          =	orig_model.state_dict()
					for src, dest in rename_keys:
										__snake_case          =	state_dict.pop(lowerCAmelCase		)
										__snake_case          =	val

					read_in_q_k_v(lowerCAmelCase				,					lowerCAmelCase				,					hf_prefix=lowerCAmelCase				,					has_lm_head=lowerCAmelCase		)
					__snake_case      ,      __snake_case          =	hf_model.load_state_dict(lowerCAmelCase				,					strict=lowerCAmelCase		)
					print("""HF missing"""				,					lowerCAmelCase		)
					print("""HF unexpected_keys"""				,					lowerCAmelCase		)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__snake_case          =	hf_model(lowerCAmelCase		)

					__snake_case          =	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__snake_case          =	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

					print(F'''max_absolute_diff = {max_absolute_diff}'''		)
					__snake_case          =	torch.allclose(lowerCAmelCase				,					lowerCAmelCase				,					atol=1e-3		)
					print("""Do both models output the same tensors?"""				,					"""🔥""" if success else """💩"""		)
					if not success:
										raise Exception("""Something went wRoNg"""		)

					# 7. Save
					print(F'''Saving to {args.hf_checkpoint_name}'''		)
					hf_model.save_pretrained(args.hf_checkpoint_name		)
					image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
		main()
		# Run the following to convert checkpoints
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./pretrained_base.pt \
		#          --hf_checkpoint_name "./data2vec-vision-base"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./finetuned_base.pt \
		#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./pretrained_large.pt \
		#          --hf_checkpoint_name "./data2vec-vision-large"
		#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
		#          --beit_checkpoint ./finetuned_large.pt \
		#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








"""simple docstring"""

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


UpperCamelCase__ :    List[str] 					=							logging.get_logger(__name__)
class     A__      (      lowercase_     ):
	A_							:				PyTorchBenchmarkArguments
	A_							:				PretrainedConfig
	A_							:				str						     =      "PyTorch"




	@property
	def 							a_		(   self  :	Optional[Any]	)					-> Tuple:
						return torch.__version__




	def 							a_		(   self  :	int      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_inference	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_inference	)




	def 							a_		(   self  :	str      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_train	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_train	)




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						if self.args.torchscript:
											__snake_case          =	True

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

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

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

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

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

						__snake_case          =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
						return _forward




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

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

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

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

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

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

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




	def 							a_		(   self  :	Tuple      ,    lowercase  :	List[Any]	)					-> float:
						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
											__snake_case          =	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 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Callable[[], None]	)					-> [Memory, MemorySummary]:
						try:
											if self.args.trace_memory_line_by_line:
																__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."""	)
																					__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()
																					__snake_case          =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																					__snake_case          =	nvml.nvmlDeviceGetMemoryInfo(lowercase	)
																					__snake_case          =	meminfo.used
																					__snake_case          =	Memory(lowercase	)
																					# shutdown nvml
																					nvml.nvmlShutdown()
											else:
																# cpu
																__snake_case          =	measure_peak_memory_cpu(lowercase	)
																__snake_case          =	Memory(lowercase	) if isinstance(lowercase      ,    lowercase	) else memory_bytes

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

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


