






"""simple docstring"""
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_rag import save_json






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   filter(lambda snake_case__					: p.requires_grad   ,   model.parameters()					)
	A_		:						Union[str, Any]	    =   sum([np.prod(p.size()					) for p in model_parameters]					)
	return params


_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if metric == "rouge2":
		A_		:						List[str]	    =   """{val_avg_rouge2:.4f}-{step_count}"""
	elif metric == "bleu":
		A_		:						Optional[int]	    =   """{val_avg_bleu:.4f}-{step_count}"""
	elif metric == "em":
		A_		:						Union[str, Any]	    =   """{val_avg_em:.4f}-{step_count}"""
	else:
		raise NotImplementedError(
		    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
		    """ function."""					)

	A_		:						int	    =   ModelCheckpoint(
	    dirpath=snake_case__   ,   filename=snake_case__   ,   monitor=F"""val_{metric}"""   ,   mode="""max"""   ,   save_top_k=3   ,   every_n_epochs=1   ,   )
	return checkpoint_callback






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




class      SCREAMING_SNAKE_CASE  (				pl.Callback						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   {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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=True							):
		logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
		A_		:						int	    =   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
		A_		:						Dict	    =   Path(pl_module.hparams.output_dir							)
		if type_path == "test":
			A_		:						Dict	    =   od / """test_results.txt"""
			A_		:						Tuple	    =   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.
			A_		:						Tuple	    =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
			A_		:						int	    =   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
				A_		:						Union[str, Any]	    =   metrics[key]
				if isinstance(lowerCAmelCase_     ,     torch.Tensor							):
					A_		:						Optional[Any]	    =   val.item()
				A_		:						Any	    =   f"""{key}: {val:.6f}\n"""
				writer.write(lowerCAmelCase_							)

		if not save_generations:
			return

		if "preds" in metrics:
			A_		:						Optional[Any]	    =   """\n""".join(metrics["""preds"""]							)
			generations_file.open("""w+"""							).write(lowerCAmelCase_							)





	@rank_zero_only
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		try:
			A_		:						str	    =   pl_module.model.model.num_parameters()
		except AttributeError:
			A_		:						Tuple	    =   pl_module.model.num_parameters()

		A_		:						List[str]	    =   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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		save_json(pl_module.metrics     ,     pl_module.metrics_save_path							)
		return self._write_logs(lowerCAmelCase_     ,     lowerCAmelCase_     ,     """test"""							)





	@rank_zero_only
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		save_json(pl_module.metrics     ,     pl_module.metrics_save_path							)
		# Uncommenting this will save val generations
		# return self._write_logs(trainer, pl_module, "valid")









"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		if two_stage is True and with_box_refine is False:
			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
		# Hungarian matcher
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

