




from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


lowercase_			:       Any						       =				logging.get_logger(__name__)

lowercase_			:       int						       =				{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona






class 					__lowerCAmelCase				( __SCREAMING_SNAKE_CASE				):
			snake_case_   :				str				  =  "layoutlmv2"







			def __init__( self					:    List[str]					,					snake_case__					:    str=30_522					,					snake_case__					:    Any=768					,					snake_case__					:    Dict=12					,					snake_case__					:    List[str]=12					,					snake_case__					:    Optional[int]=3_072					,					snake_case__					:    List[Any]="gelu"					,					snake_case__					:    Optional[int]=0.1					,					snake_case__					:    str=0.1					,					snake_case__					:    Optional[Any]=512					,					snake_case__					:    int=2					,					snake_case__					:    List[Any]=0.02					,					snake_case__					:    Optional[int]=1e-12					,					snake_case__					:    Union[str, Any]=0					,					snake_case__					:    int=1_024					,					snake_case__					:    List[Any]=128					,					snake_case__					:    List[str]=32					,					snake_case__					:    Optional[int]=True					,					snake_case__					:    List[str]=256					,					snake_case__					:    List[str]=64					,					snake_case__					:    Dict=True					,					snake_case__					:    Tuple=[7, 7, 256]					,					snake_case__					:    Optional[Any]=128					,					snake_case__					:    int=128					,					snake_case__					:    Union[str, Any]=True					,					snake_case__					:    Any=True					,					snake_case__					:    Optional[int]=False					,					snake_case__					:    List[str]=None					,					**snake_case__					:    str					,					):




									"""simple docstring"""


									super().__init__(
									    vocab_size=UpperCamelCase__					,					hidden_size=UpperCamelCase__					,					num_hidden_layers=UpperCamelCase__					,					num_attention_heads=UpperCamelCase__					,					intermediate_size=UpperCamelCase__					,					hidden_act=UpperCamelCase__					,					hidden_dropout_prob=UpperCamelCase__					,					attention_probs_dropout_prob=UpperCamelCase__					,					max_position_embeddings=UpperCamelCase__					,					type_vocab_size=UpperCamelCase__					,					initializer_range=UpperCamelCase__					,					layer_norm_eps=UpperCamelCase__					,					pad_token_id=UpperCamelCase__					,					**UpperCamelCase__					,					)
									_UpperCAmelCase									=  max_ad_position_embeddings
									_UpperCAmelCase									=  max_rel_pos
									_UpperCAmelCase									=  rel_pos_bins
									_UpperCAmelCase									=  fast_qkv
									_UpperCAmelCase									=  max_rel_ad_pos
									_UpperCAmelCase									=  rel_ad_pos_bins
									_UpperCAmelCase									=  convert_sync_batchnorm
									_UpperCAmelCase									=  image_feature_pool_shape
									_UpperCAmelCase									=  coordinate_size
									_UpperCAmelCase									=  shape_size
									_UpperCAmelCase									=  has_relative_attention_bias
									_UpperCAmelCase									=  has_spatial_attention_bias
									_UpperCAmelCase									=  has_visual_segment_embedding
									_UpperCAmelCase									=  (
									    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
									)







			@classmethod
			def UpperCamelCase    ( self					:    Optional[int]   ):




									"""simple docstring"""


									return {
									    "MODEL.MASK_ON": True,
									    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
									    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
									    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
									    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
									    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
									    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
									    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
									    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
									    "MODEL.POST_NMS_TOPK_TEST": 1_000,
									    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
									    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
									    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
									    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
									    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
									    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
									    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
									    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
									    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
									    "MODEL.RESNETS.DEPTH": 101,
									    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
									    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
									    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
									    "MODEL.RESNETS.NUM_GROUPS": 32,
									    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
									    "MODEL.RESNETS.STRIDE_IN_1X1": False,
									}







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  detectrona.config.get_cfg()
									for k, v in self.detectrona_config_args.items():
															_UpperCAmelCase									=  k.split("."   )
															_UpperCAmelCase									=  detectrona_config
															for attribute in attributes[:-1]:
																					_UpperCAmelCase									=  getattr(UpperCamelCase__					,					UpperCamelCase__   )
															setattr(UpperCamelCase__					,					attributes[-1]					,					UpperCamelCase__   )

									return detectrona_config










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


lowercase_			:       int						       =				logging.get_logger(__name__)






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				PyTorchBenchmarkArguments
			snake_case_   :				PretrainedConfig
			snake_case_   :				str				  =  "PyTorch"







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




									"""simple docstring"""


									return torch.__version__







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


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







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




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


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







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




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Dict					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									if self.args.torchscript:
															_UpperCAmelCase									=  True

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

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

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

									def encoder_decoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__					,					decoder_input_ids=snake_case__   )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__   )
															return outputs

									_UpperCAmelCase									=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

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

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

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

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

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

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







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Tuple   ):




									"""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
															_UpperCAmelCase									=  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 UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    Callable[[], None]   ):




									"""simple docstring"""


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

															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  stop_memory_tracing(snake_case__   )
															else:
																					_UpperCAmelCase									=  None

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




