




"""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      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			str    =    filter(lambda lowercase__		: p.requires_grad							,    model.parameters()		)
				_lowerCamelCase     :			Optional[int]    =    sum([np.prod(p.size()		) for p in model_parameters]		)
				return params


lowercase__     						=						logging.getLogger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):




				if metric == "rouge2":
								_lowerCamelCase     :			Any    =    '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								_lowerCamelCase     :			Optional[Any]    =    '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "em":
								_lowerCamelCase     :			Tuple    =    '{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.'		)

				_lowerCamelCase     :			List[str]    =    ModelCheckpoint(
				    dirpath=_UpperCamelCase							,    filename=_UpperCamelCase							,    monitor=f'''val_{metric}'''							,    mode='max'							,    save_top_k=3							,    every_n_epochs=1							,    )
				return checkpoint_callback







def      _snake_case			(				lowercase__							,    lowercase__		):
				return EarlyStopping(
				    monitor=f'''val_{metric}'''							,    mode='min' if 'loss' in metric else 'max'							,    patience=_UpperCamelCase							,    verbose=_UpperCamelCase							,    )






class lowerCAmelCase__	(     pl.Callback						):






			'''simple docstring'''



			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
							pl_module.logger.log_metrics(__a  )
			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase=True  ):
							logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''  )
							_lowerCamelCase     :			Any    =    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     :			Any    =    Path(pl_module.hparams.output_dir  )
							if type_path == "test":
											_lowerCamelCase     :			str    =    od / 'test_results.txt'
											_lowerCamelCase     :			Union[str, Any]    =    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     :			List[str]    =    od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											_lowerCamelCase     :			Optional[Any]    =    od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=__a  )
											generations_file.parent.mkdir(exist_ok=__a  )
							with open(__a  ,      'a+'  ) as writer:
											for key in sorted(__a  ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_lowerCamelCase     :			int    =    metrics[key]
															if isinstance(__a  ,      torch.Tensor  ):
																			_lowerCamelCase     :			str    =    val.item()
															_lowerCamelCase     :			int    =    F'''{key}: {val:.6f}\n'''
															writer.write(__a  )

							if not save_generations:
											return

							if "preds" in metrics:
											_lowerCamelCase     :			Tuple    =    '\n'.join(metrics['preds']  )
											generations_file.open('w+'  ).write(__a  )
			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							try:
											_lowerCamelCase     :			Dict    =    pl_module.model.model.num_parameters()
							except AttributeError:
											_lowerCamelCase     :			int    =    pl_module.model.num_parameters()

							_lowerCamelCase     :			int    =    count_trainable_parameters(__a  )
							# 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 					A_    (  self  ,      lowercase  ,      lowercase  ):
							save_json(pl_module.metrics  ,      pl_module.metrics_save_path  )
							return self._write_logs(__a  ,      __a  ,      'test'  )






			@rank_zero_only
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							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 warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A     :   str			    =			logging.get_logger(__name__)
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      =["""input_ids""", """attention_mask"""]

							def __init__(     self    ,   __a="</s>"    ,   __a="<unk>"    ,   __a="<pad>"    ,   __a=1_25    ,   __a=None    ,   **__a    ,   ):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
													__lowerCAmelCase				    =			[f"<extra_id_{i}>" for i in range(__a	)]
										elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													__lowerCAmelCase				    =			len(set(filter(lambda __a	: bool("extra_id" in str(__a	)	)    ,   __a	)	)	)
													if extra_tokens != extra_ids:
																raise ValueError(
																    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
																    " extra_ids tokens"	)

										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else pad_token
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else eos_token
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else unk_token

										super().__init__(
										    eos_token=__a    ,   unk_token=__a    ,   pad_token=__a    ,   extra_ids=__a    ,   additional_special_tokens=__a    ,   **__a    ,   )

										__lowerCAmelCase				    =			extra_ids

										__lowerCAmelCase				    =			2**8  # utf is 8 bits

										# define special tokens dict
										__lowerCAmelCase				    =			{
										    self.pad_token: 0,
										    self.eos_token: 1,
										    self.unk_token: 2,
										}
										__lowerCAmelCase				    =			len(self.special_tokens_encoder	)
										__lowerCAmelCase				    =			len(__a	)
										for i, token in enumerate(__a	):
													__lowerCAmelCase				    =			self.vocab_size + i - n
										__lowerCAmelCase				    =			{v: k for k, v in self.special_tokens_encoder.items()}

							@property
							def 			snake_case				(     self	):
										return self._utf_vocab_size + self._num_special_tokens + self._extra_ids

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = False	):
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__a    ,   token_ids_a=__a    ,   already_has_special_tokens=__a	)

										# normal case: some special tokens
										if token_ids_a is None:
													return ([0] * len(__a	)) + [1]
										return ([0] * len(__a	)) + [1] + ([0] * len(__a	)) + [1]

							def 			snake_case				(     self    ,   __a	):
										if len(__a	) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
													    " eos tokens being added."	)
													return token_ids
										else:
													return token_ids + [self.eos_token_id]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.eos_token_id]

										if token_ids_a is None:
													return len(token_ids_a + eos	) * [0]
										return len(token_ids_a + eos + token_ids_a + eos	) * [0]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			self._add_eos_if_not_present(__a	)
										if token_ids_a is None:
													return token_ids_a
										else:
													__lowerCAmelCase				    =			self._add_eos_if_not_present(__a	)
													return token_ids_a + token_ids_a

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[chr(__a	) for i in text.encode("utf-8"	)]
										return tokens

							def 			snake_case				(     self    ,   __a	):
										if token in self.special_tokens_encoder:
													__lowerCAmelCase				    =			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
													__lowerCAmelCase				    =			self.added_tokens_encoder[token]
										elif len(__a	) != 1:
													__lowerCAmelCase				    =			self.unk_token_id
										else:
													__lowerCAmelCase				    =			ord(__a	) + self._num_special_tokens
										return token_id

							def 			snake_case				(     self    ,   __a	):
										if index in self.special_tokens_decoder:
													__lowerCAmelCase				    =			self.special_tokens_decoder[index]
										else:
													__lowerCAmelCase				    =			chr(index - self._num_special_tokens	)
										return token

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			B""
										for token in tokens:
													if token in self.special_tokens_decoder:
																__lowerCAmelCase				    =			self.special_tokens_decoder[token].encode("utf-8"	)
													elif token in self.added_tokens_decoder:
																__lowerCAmelCase				    =			self.special_tokens_decoder[token].encode("utf-8"	)
													elif token in self.special_tokens_encoder:
																__lowerCAmelCase				    =			token.encode("utf-8"	)
													elif token in self.added_tokens_encoder:
																__lowerCAmelCase				    =			token.encode("utf-8"	)
													else:
																__lowerCAmelCase				    =			bytes([ord(__a	)]	)
													bstring += tok_string
										__lowerCAmelCase				    =			bstring.decode("utf-8"    ,   errors="ignore"	)
										return string



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										return ()




