






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   lowerCamelCase      (		a_			)			->    Optional[Any]:
							lowerCAmelCase_       		=  filter(lambda a_			: p.requires_grad				,		model.parameters()			)
							lowerCAmelCase_       		=  sum([np.prod(p.size()			) for p in model_parameters]			)
							return params


lowerCamelCase_		  =						logging.getLogger(__name__)




class 							a_				(		pl.Callback	):


							'''simple docstring'''






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Any:
														'''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(lowercase_     )

							@rank_zero_only
							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_=True     )      ->      None:
														'''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=lowercase_     )
																					generations_file.parent.mkdir(exist_ok=lowercase_     )
														with open(lowercase_    ,    'a+'     ) as writer:
																					for key in sorted(lowercase_     ):
																												if key in ["log", "progress_bar", "preds"]:
																																			continue
																												lowerCAmelCase_       		=  metrics[key]
																												if isinstance(lowercase_    ,    torch.Tensor     ):
																																			lowerCAmelCase_       		=  val.item()
																												lowerCAmelCase_       		=  f'''{key}: {val:.6f}\n'''
																												writer.write(lowercase_     )

														if not save_generations:
																					return

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

							@rank_zero_only
							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      int:
														'''simple docstring'''

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

														lowerCAmelCase_       		=  count_trainable_parameters(lowercase_     )
														# 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   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Any:
														'''simple docstring'''

														save_json(pl_module.metrics    ,    pl_module.metrics_save_path     )
														return self._write_logs(lowercase_    ,    lowercase_    ,    'test'     )






							@rank_zero_only
							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      int:
														'''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   lowerCamelCase      (		a_				,		a_				,		a_=1				,		a_=False			)			->    Union[str, Any]:
							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=a_				,		filename=a_				,		monitor=F'''val_{metric}'''				,		mode='min' if 'loss' in metric else 'max'				,		save_top_k=a_				,		)
							return checkpoint_callback



def   lowerCamelCase      (		a_				,		a_			)			->    str:
							return EarlyStopping(
							    monitor=F'''val_{metric}'''				,		mode='min' if 'loss' in metric else 'max'				,		patience=a_				,		verbose=a_				,		)











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 							a_				(		a_	):


							'''simple docstring'''






							__a:			int									=					['''image_processor''', '''tokenizer''']
							__a:			int									=					'''VideoMAEImageProcessor'''
							__a:			Dict									=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')

							def __init__(	self    ,    lowercase_=None    ,    lowercase_=None    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																					    ' instead.'    ,    lowercase_    ,    )
																					lowerCAmelCase_       		=  kwargs.pop('feature_extractor'     )

														lowerCAmelCase_       		=  image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError('You need to specify an `image_processor`.'     )
														if tokenizer is None:
																					raise ValueError('You need to specify a `tokenizer`.'     )

														super().__init__(lowercase_    ,    lowercase_     )
														lowerCAmelCase_       		=  self.image_processor

							def __call__(	self    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    **lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														if text is None and videos is None:
																					raise ValueError('You have to specify either text or videos. Both cannot be none.'     )

														if text is not None:
																					lowerCAmelCase_       		=  self.tokenizer(lowercase_    ,    return_tensors=lowercase_    ,    **lowercase_     )

														if videos is not None:
																					lowerCAmelCase_       		=  self.image_processor(lowercase_    ,    return_tensors=lowercase_    ,    **lowercase_     )

														if text is not None and videos is not None:
																					lowerCAmelCase_       		=  image_features.pixel_values
																					return encoding
														elif text is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**lowercase_     )    ,    tensor_type=lowercase_     )

							def   _lowercase (	self    ,    *lowercase_    ,    **lowercase_     )      ->      Union[str, Any]:
														'''simple docstring'''

														return self.tokenizer.batch_decode(*lowercase_    ,    **lowercase_     )

							def   _lowercase (	self    ,    *lowercase_    ,    **lowercase_     )      ->      str:
														'''simple docstring'''

														return self.tokenizer.decode(*lowercase_    ,    **lowercase_     )

							@property
							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														warnings.warn(
														    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,    lowercase_    ,    )
														return self.image_processor_class






							@property
							def   _lowercase (	self     )      ->      int:
														'''simple docstring'''

														warnings.warn(
														    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,    lowercase_    ,    )
														return self.image_processor



