





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


__lowercase							:List[str]							=	logging.get_logger(__name__)

__lowercase							:str							=	{
    "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  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "layoutlmv2"

							def __init__(   self					:  Optional[Any]				,						a					:  Optional[int]=3_05_22				,						a					:  List[Any]=7_68				,						a					:  Union[str, Any]=12				,						a					:  Optional[int]=12				,						a					:  Tuple=30_72				,						a					:  Optional[Any]="gelu"				,						a					:  List[str]=0.1				,						a					:  Optional[Any]=0.1				,						a					:  Any=5_12				,						a					:  Optional[Any]=2				,						a					:  Dict=0.02				,						a					:  int=1E-12				,						a					:  Tuple=0				,						a					:  Union[str, Any]=10_24				,						a					:  Optional[int]=1_28				,						a					:  Tuple=32				,						a					:  Tuple=True				,						a					:  Tuple=2_56				,						a					:  Tuple=64				,						a					:  Dict=True				,						a					:  Dict=[7, 7, 2_56]				,						a					:  str=1_28				,						a					:  Tuple=1_28				,						a					:  List[Any]=True				,						a					:  Optional[int]=True				,						a					:  List[str]=False				,						a					:  Optional[int]=None				,						**a					:  str				,						)						->Dict:
													super().__init__(
													    vocab_size=_lowerCAmelCase				,						hidden_size=_lowerCAmelCase				,						num_hidden_layers=_lowerCAmelCase				,						num_attention_heads=_lowerCAmelCase				,						intermediate_size=_lowerCAmelCase				,						hidden_act=_lowerCAmelCase				,						hidden_dropout_prob=_lowerCAmelCase				,						attention_probs_dropout_prob=_lowerCAmelCase				,						max_position_embeddings=_lowerCAmelCase				,						type_vocab_size=_lowerCAmelCase				,						initializer_range=_lowerCAmelCase				,						layer_norm_eps=_lowerCAmelCase				,						pad_token_id=_lowerCAmelCase				,						**_lowerCAmelCase				,						)
													SCREAMING_SNAKE_CASE__  :					str =       max_ad_position_embeddings
													SCREAMING_SNAKE_CASE__  :					int =       max_rel_pos
													SCREAMING_SNAKE_CASE__  :					Tuple =       rel_pos_bins
													SCREAMING_SNAKE_CASE__  :					int =       fast_qkv
													SCREAMING_SNAKE_CASE__  :					Tuple =       max_rel_ad_pos
													SCREAMING_SNAKE_CASE__  :					int =       rel_ad_pos_bins
													SCREAMING_SNAKE_CASE__  :					List[Any] =       convert_sync_batchnorm
													SCREAMING_SNAKE_CASE__  :					List[str] =       image_feature_pool_shape
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       coordinate_size
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       shape_size
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       has_relative_attention_bias
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       has_spatial_attention_bias
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       has_visual_segment_embedding
													SCREAMING_SNAKE_CASE__  :					List[Any] =       (
													    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
													)

							@classmethod
							def   A_	(   self					:  Any     )						->List[str]:
													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], [1_28], [2_56], [5_12]],
													    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
													    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
													    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
													    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
													    "MODEL.POST_NMS_TOPK_TEST": 10_00,
													    "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": 1_01,
													    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
													    "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   A_	(   self					:  str     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Tuple =       detectrona.config.get_cfg()
													for k, v in self.detectrona_config_args.items():
																			SCREAMING_SNAKE_CASE__  :					Tuple =       k.split("."     )
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       detectrona_config
																			for attribute in attributes[:-1]:
																									SCREAMING_SNAKE_CASE__  :					Tuple =       getattr(_lowerCAmelCase				,						_lowerCAmelCase     )
																			setattr(_lowerCAmelCase				,						attributes[-1]				,						_lowerCAmelCase     )

													return detectrona_config





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}					)
         # Log results
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    od / "test_generations.txt"
         else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # mp stands for million parameters
         trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



