
__a         =   'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'




def 							a		(				snake_case__:   bytes   ):


				'''simple docstring'''
				# Make sure the supplied data is a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
								raise TypeError(snake_case__   )

				lowercase_	    =   ''''''.join(bin(snake_case__   )[2:].zfill(8   ) for byte in data   )

				lowercase_	    =   len(snake_case__   ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								lowercase_	    =   B'''=''' * ((6 - len(snake_case__   ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(snake_case__   ) % 6)
				else:
								lowercase_	    =   B''''''

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]		,  2   )]
				        for index in range(0		,  len(snake_case__   )		,  6   )   ).encode()
				    + padding
				)




def 							a		(				snake_case__:   str   ):


				'''simple docstring'''
				# Make sure encoded_data is either a string or a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ) and not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   (
								    '''argument should be a bytes-like object or ASCII string, '''
								    F'''not \'{encoded_data.__class__.__name__}\''''
								)
								raise TypeError(snake_case__   )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(snake_case__		,  snake_case__   ):
								try:
												lowercase_	    =   encoded_data.decode('''utf-8'''   )
								except UnicodeDecodeError:
												raise ValueError('''base64 encoded data should only contain ASCII characters'''   )

				lowercase_	    =   encoded_data.count('''='''   )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(snake_case__   ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								lowercase_	    =   encoded_data[:-padding]

								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
				else:
								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )

				lowercase_	    =   [
				    int(binary_stream[index : index + 8]		,  2   )
				    for index in range(0		,  len(snake_case__   )		,  8   )
				]

				return bytes(snake_case__   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()











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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




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


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{val_avg_loss:.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     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




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







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"							)
         _A     =							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     =							Path(pl_module.hparams.output_dir							)
         if type_path == "test":
             _A     =							od / 'test_results.txt'
             _A     =							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     =							od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             _A     =							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
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

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




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




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





