



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							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       filter(lambda snake_case     : p.requires_grad      ,		model.parameters()     )
					_lowerCAmelCase         =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


A__       =			logging.getLogger(__name__)





class    __lowerCAmelCase					(	pl.Callback			):




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       {F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
									pl_module.logger.log_metrics(_snake_case						)




				@rank_zero_only
				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case=True						):



									"""simple docstring"""





									logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'						)
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       Path(pl_module.hparams.output_dir						)
									if type_path == "test":
														_lowerCAmelCase         =       od / """test_results.txt"""
														_lowerCAmelCase         =       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.
														_lowerCAmelCase         =       od / F'{type_path}_results/{trainer.global_step:05d}.txt'
														_lowerCAmelCase         =       od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
														results_file.parent.mkdir(exist_ok=_snake_case						)
														generations_file.parent.mkdir(exist_ok=_snake_case						)
									with open(_snake_case		,							"""a+"""						) as writer:
														for key in sorted(_snake_case						):
																			if key in ["log", "progress_bar", "preds"]:
																								continue
																			_lowerCAmelCase         =       metrics[key]
																			if isinstance(_snake_case		,							torch.Tensor						):
																								_lowerCAmelCase         =       val.item()
																			_lowerCAmelCase         =       F'{key}: {val:.6f}\n'
																			writer.write(_snake_case						)

									if not save_generations:
														return

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




				@rank_zero_only
				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									try:
														_lowerCAmelCase         =       pl_module.model.model.num_parameters()
									except AttributeError:
														_lowerCAmelCase         =       pl_module.model.num_parameters()

									_lowerCAmelCase         =       count_trainable_parameters(_snake_case						)
									# 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 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									save_json(pl_module.metrics		,							pl_module.metrics_save_path						)
									return self._write_logs(_snake_case		,							_snake_case		,							"""test"""						)




				@rank_zero_only
				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									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							( snake_case      ,		snake_case      ,		snake_case=1      ,		snake_case=False     ):





					"""simple docstring"""

					if metric == "rouge2":
										_lowerCAmelCase         =       """{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										_lowerCAmelCase         =       """{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "loss":
										_lowerCAmelCase         =       """{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."""     )

					_lowerCAmelCase         =       ModelCheckpoint(
					    dirpath=snake_case      ,		filename=snake_case      ,		monitor=F'val_{metric}'      ,		mode="""min""" if """loss""" in metric else """max"""      ,		save_top_k=snake_case      ,		)
					return checkpoint_callback


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					return EarlyStopping(
					    monitor=F'val_{metric}'      ,		mode="""min""" if """loss""" in metric else """max"""      ,		patience=snake_case      ,		verbose=snake_case      ,		)





import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




