

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 	__a     (      A__			:  List[str]       ):
    SCREAMING_SNAKE_CASE										=   filter(lambda A__       : p.requires_grad			,  model.parameters()       )
    SCREAMING_SNAKE_CASE										=   sum([np.prod(p.size()       ) for p in model_parameters]       )
    return params


__A			:					Optional[int]						=			logging.getLogger(__name__)





def 	__a     (      A__			:  Any			,  A__			:  str       ):
    if metric == "rouge2":
        SCREAMING_SNAKE_CASE										=   "{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        SCREAMING_SNAKE_CASE										=   "{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        SCREAMING_SNAKE_CASE										=   "{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."       )

    SCREAMING_SNAKE_CASE										=   ModelCheckpoint(
        dirpath=A__			,  filename=A__			,  monitor=F"val_{metric}"			,  mode="max"			,  save_top_k=3			,  every_n_epochs=1			,  )
    return checkpoint_callback





def 	__a     (      A__			:  int			,  A__			:  List[str]       ):
    return EarlyStopping(
        monitor=F"val_{metric}"			,  mode="min" if "loss" in metric else "max"			,  patience=A__			,  verbose=A__			,  )





class 			_SCREAMING_SNAKE_CASE (   pl.Callback		):







   '''simple docstring'''




   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
       pl_module.logger.log_metrics(__lowerCamelCase			)





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

       if not save_generations:
           return

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





   @rank_zero_only
   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			List[str]			):
       try:
           SCREAMING_SNAKE_CASE										=   pl_module.model.model.num_parameters()
       except AttributeError:
           SCREAMING_SNAKE_CASE										=   pl_module.model.num_parameters()

       SCREAMING_SNAKE_CASE										=   count_trainable_parameters(__lowerCamelCase			)
       # 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					:			Union[str, Any]     , __lowerCamelCase					:			pl.Trainer     , __lowerCamelCase					:			pl.LightningModule			):
       save_json(pl_module.metrics     , pl_module.metrics_save_path			)
       return self._write_logs(__lowerCamelCase     , __lowerCamelCase     , "test"			)





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


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

__A			:					Any						=			logging.get_logger(__name__)


__A			:					Any						=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__A			:					Optional[Any]						=			{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__A			:					Union[str, Any]						=			{'facebook/blenderbot-3B': 1_2_8}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   (
        list(range(ord("!"       )			,  ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,  ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,  ord("ÿ"       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE										=   bs[:]
    SCREAMING_SNAKE_CASE										=   0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(A__       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE										=   [chr(A__       ) for n in cs]
    return dict(zip(A__			,  A__       )       )





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   set()
    SCREAMING_SNAKE_CASE										=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE										=   char
    return pairs





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Any="replace"     , __lowerCamelCase					:			Optional[Any]="<s>"     , __lowerCamelCase					:			Optional[Any]="</s>"     , __lowerCamelCase					:			Any="</s>"     , __lowerCamelCase					:			Union[str, Any]="<s>"     , __lowerCamelCase					:			List[str]="<unk>"     , __lowerCamelCase					:			Optional[Any]="<pad>"     , __lowerCamelCase					:			Dict="<mask>"     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Optional[Any]     , ):
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else bos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else eos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else sep_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else cls_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else unk_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else mask_token

       super().__init__(
           errors=__lowerCamelCase     , bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , unk_token=__lowerCamelCase     , sep_token=__lowerCamelCase     , cls_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , mask_token=__lowerCamelCase     , add_prefix_space=__lowerCamelCase     , **__lowerCamelCase     , )

       with open(__lowerCamelCase     , encoding="utf-8"			) as vocab_handle:
           SCREAMING_SNAKE_CASE										=   json.load(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.encoder.items()}
       SCREAMING_SNAKE_CASE										=   errors  # how to handle errors in decoding
       SCREAMING_SNAKE_CASE										=   bytes_to_unicode()
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.byte_encoder.items()}
       with open(__lowerCamelCase     , encoding="utf-8"			) as merges_handle:
           SCREAMING_SNAKE_CASE										=   merges_handle.read().split("\n"			)[1:-1]
       SCREAMING_SNAKE_CASE										=   [tuple(merge.split()			) for merge in bpe_merges]
       SCREAMING_SNAKE_CASE										=   dict(zip(__lowerCamelCase     , range(len(__lowerCamelCase			)			)			)			)
       SCREAMING_SNAKE_CASE										=   {}
       SCREAMING_SNAKE_CASE										=   add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       SCREAMING_SNAKE_CASE										=   re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"			)





   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def      _snake_case		(       self					:			str			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[Any]			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

       if not pairs:
           return token

       while True:
           SCREAMING_SNAKE_CASE										=   min(__lowerCamelCase     , key=lambda __lowerCamelCase			: self.bpe_ranks.get(__lowerCamelCase     , float("inf"			)			)			)
           if bigram not in self.bpe_ranks:
               break
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   bigram
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   0
           while i < len(__lowerCamelCase			):
               try:
                   SCREAMING_SNAKE_CASE										=   word.index(__lowerCamelCase     , __lowerCamelCase			)
               except ValueError:
                   new_word.extend(word[i:]			)
                   break
               else:
                   new_word.extend(word[i:j]			)
                   SCREAMING_SNAKE_CASE										=   j

               if word[i] == first and i < len(__lowerCamelCase			) - 1 and word[i + 1] == second:
                   new_word.append(first + second			)
                   i += 2
               else:
                   new_word.append(word[i]			)
                   i += 1
           SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   new_word
           if len(__lowerCamelCase			) == 1:
               break
           else:
               SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   " ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   word
       return word





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   []
       for token in re.findall(self.pat     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   "".join(
               self.byte_encoder[b] for b in token.encode("utf-8"			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase			).split(" "			)			)
       return bpe_tokens





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Dict			):
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[int]			):
       return self.decoder.get(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"     , errors=self.errors			)
       return text





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

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

       SCREAMING_SNAKE_CASE										=   0
       with open(__lowerCamelCase     , "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 __lowerCamelCase			: 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!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(" ".join(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowerCamelCase     , token_ids_a=__lowerCamelCase     , already_has_special_tokens=__lowerCamelCase			)

       if token_ids_a is None:
           return [1] + ([0] * len(__lowerCamelCase			)) + [1]
       return [1] + ([0] * len(__lowerCamelCase			)) + [1, 1] + ([0] * len(__lowerCamelCase			)) + [1]





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [self.cls_token_id]

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





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("add_prefix_space"     , self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase			) > 0 and not text[0].isspace()):
           SCREAMING_SNAKE_CASE										=   " " + text
       return (text, kwargs)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       return token_ids_a + [self.eos_token_id]





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			"Conversation"			):
       SCREAMING_SNAKE_CASE										=   []
       for is_user, text in conversation.iter_texts():
           if is_user:
               # We need to space prefix as it's being done within blenderbot
               inputs.append(" " + text			)
           else:
               # Generated responses should contain them already.
               inputs.append(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "  ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.encode(__lowerCamelCase			)
       if len(__lowerCamelCase			) > self.model_max_length:
           SCREAMING_SNAKE_CASE										=   input_ids[-self.model_max_length :]
           logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."			)
       return input_ids