





'''simple docstring'''







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)


lowerCAmelCase_					:    str      	=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase_					:    List[Any]      	=			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase_					:    str      	=			{'facebook/blenderbot_small-90M': 5_12}






def  _lowerCamelCase				( lowercase						:      List[Any]						)	->	List[str]:
				_a								= set()
				_a								= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)						)
								_a								= char

				_a								= set(lowercase						)
				return pairs



class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =VOCAB_FILES_NAMES
				__a            =PRETRAINED_VOCAB_FILES_MAP
				__a            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a            =['input_ids', 'attention_mask']




				def __init__(  self     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					Optional[int]="__start__"					,				__a     :					Union[str, Any]="__end__"					,				__a     :					Any="__unk__"					,				__a     :					Union[str, Any]="__null__"					,				**__a     :					Tuple					,				):
								super().__init__(unk_token=__a					,				bos_token=__a					,				eos_token=__a					,				pad_token=__a					,				**__a					)

								with open(__a					,				encoding="utf-8"					) as vocab_handle:
												_a								= json.load(__a					)
								_a								= {v: k for k, v in self.encoder.items()}
								with open(__a					,				encoding="utf-8"					) as merges_handle:
												_a								= merges_handle.read().split("\n"					)[1:-1]
								_a								= [tuple(merge.split()					) for merge in merges]
								_a								= dict(zip(__a					,				range(len(__a					)					)					)					)
								_a								= {}




				@property
				def   UpperCamelCase__   (  self     :					Any					):
								return len(self.encoder					)




				def   UpperCamelCase__   (  self     :					Dict					):
								return dict(self.encoder					,				**self.added_tokens_encoder					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					str					):
								if token in self.cache:
												return self.cache[token]
								_a								= re.sub("([.,!?()])"					,				r" \1"					,				__a					)
								_a								= re.sub("(')"					,				r" \1 "					,				__a					)
								_a								= re.sub(r"\s{2,}"					,				" "					,				__a					)
								if "\n" in token:
												_a								= token.replace("\n"					,				" __newln__"					)

								_a								= token.split(" "					)
								_a								= []
								for token in tokens:
												if not len(__a					):
																continue

												_a								= token.lower()
												_a								= tuple(__a					)
												_a								= tuple(list(word[:-1]					) + [word[-1] + "</w>"]					)
												_a								= get_pairs(__a					)

												if not pairs:
																words.append(__a					)
																continue

												while True:
																_a								= min(__a					,				key=lambda __a					: self.bpe_ranks.get(__a					,				float("inf"					)					)					)
																if bigram not in self.bpe_ranks:
																				break
																_a	,					_a								= bigram
																_a								= []
																_a								= 0

																while i < len(__a					):
																				try:
																								_a								= word.index(__a					,				__a					)
																								new_word.extend(word[i:j]					)
																								_a								= j
																				except ValueError:
																								new_word.extend(word[i:]					)
																								break

																				if word[i] == first and i < len(__a					) - 1 and word[i + 1] == second:
																								new_word.append(first + second					)
																								i += 2
																				else:
																								new_word.append(word[i]					)
																								i += 1
																_a								= tuple(__a					)
																_a								= new_word
																if len(__a					) == 1:
																				break
																else:
																				_a								= get_pairs(__a					)
												_a								= "@@ ".join(__a					)
												_a								= word[:-4]

												_a								= word
												words.append(__a					)
								return " ".join(__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					str					):
								_a								= []

								_a								= re.findall(r"\S+\n?"					,				__a					)

								for token in words:
												split_tokens.extend(list(self.bpe(__a					).split(" "					)					)					)
								return split_tokens




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					):
								_a								= token.lower()
								return self.encoder.get(__a					,				self.encoder.get(self.unk_token					)					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					int					):
								return self.decoder.get(__a					,				self.unk_token					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					List[str]					):
								_a								= " ".join(__a					).replace("@@ "					,				""					).strip()
								return out_string




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					Optional[str] = None					):
								if not os.path.isdir(__a					):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
												return
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

								with open(__a					,				"w"					,				encoding="utf-8"					) as f:
												f.write(json.dumps(self.encoder					,				indent=2					,				sort_keys=__a					,				ensure_ascii=__a					) + "\n"					)

								_a								= 0
								with open(__a					,				"w"					,				encoding="utf-8"					) as writer:
												writer.write("#version: 0.2\n"					)
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,				key=lambda __a					: kv[1]					):
																if index != token_index:
																				logger.warning(
																				    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																				    " Please check that the tokenizer is not corrupted!"					)
																				_a								= token_index
																writer.write(" ".join(__a					) + "\n"					)
																index += 1

								return vocab_file, merge_file







'''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  _lowerCamelCase				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				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}"
				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=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								_a								= {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   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					,				__a     :					str					,				__a     :					Tuple=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=__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
																_a								= metrics[key]
																if isinstance(__a					,				torch.Tensor					):
																				_a								= val.item()
																_a								= f'{key}: {val:.6f}\n'
																writer.write(__a					)

								if not save_generations:
												return

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




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								try:
												_a								= pl_module.model.model.num_parameters()
								except AttributeError:
												_a								= pl_module.model.num_parameters()

								_a								= 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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					int					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
