





"""simple docstring"""


# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
lowerCamelCase__      =      1E-10
lowerCamelCase__      =      "This is incredible! I love it, this is the best chicken I have ever had."
lowerCamelCase__      =      100



class    __SCREAMING_SNAKE_CASE			(  nn.Module      ):







  '''simple docstring'''


  def __init__(						self      :				Optional[int]    ,      __a      :				str    ,      __a      :				str="gpt2-medium"    ,      __a      :				str=False    ,      __a      :				Tuple="cpu"		)						-> Union[str, Any]:
        super().__init__()
        _UpperCamelCase					:		Union[str, Any]  =				GPTaTokenizer.from_pretrained(__a		)
        _UpperCamelCase					:		int  =				GPTaLMHeadModel.from_pretrained(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				self.encoder.transformer.config.hidden_size
        _UpperCamelCase					:		Optional[int]  =				ClassificationHead(class_size=__a    ,      embed_size=self.embed_size		)
        _UpperCamelCase					:		List[str]  =				cached_mode
        _UpperCamelCase					:		str  =				device

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Dict:
        return self.classifier_head

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> int:
        for param in self.encoder.parameters():
              _UpperCamelCase					:		Optional[Any]  =				False
        self.classifier_head.train()

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				Optional[int]		)						-> Tuple:
        _UpperCamelCase					:		Tuple  =				x.ne(0		).unsqueeze(2		).repeat(1    ,      1    ,      self.embed_size		).float().to(self.device		).detach()
        _UpperCamelCase					:		List[str]  =				self.encoder.transformer(__a		)["last_hidden_state"]
        _UpperCamelCase					:		Dict  =				hidden * mask
        _UpperCamelCase					:		Any  =				torch.sum(__a    ,      dim=1		) / (torch.sum(__a    ,      dim=1		).detach() + EPSILON)
        return avg_hidden


  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				Optional[Any]		)						-> Dict:
        if self.cached_mode:
              _UpperCamelCase					:		Optional[Any]  =				x.to(self.device		)
        else:
              _UpperCamelCase					:		Tuple  =				self.avg_representation(x.to(self.device		)		)

        _UpperCamelCase					:		Optional[int]  =				self.classifier_head(__a		)
        _UpperCamelCase					:		List[Any]  =				nn.functional.log_softmax(__a    ,      dim=-1		)

        return probs





class    __SCREAMING_SNAKE_CASE			(  data.Dataset      ):







  '''simple docstring'''


  def __init__(						self      :				Optional[Any]    ,      __a      :				str    ,      __a      :				Optional[Any]		)						-> Dict:
        _UpperCamelCase					:		str  =				X
        _UpperCamelCase					:		Union[str, Any]  =				y

  def __len__(						self      :				Tuple		)						-> List[str]:
        return len(self.X		)


  def __getitem__(						self      :				Tuple    ,      __a      :				List[str]		)						-> Union[str, Any]:
        _UpperCamelCase					:		Optional[Any]  =				{}
        _UpperCamelCase					:		Tuple  =				self.X[index]
        _UpperCamelCase					:		List[Any]  =				self.y[index]
        return data





def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      def pad_sequences(lowercase_	):
            _UpperCamelCase					:		Dict  =				[len(lowercase_	) for seq in sequences]

            _UpperCamelCase					:		List[Any]  =				torch.zeros(len(lowercase_	)						,max(lowercase_	)	).long()  # padding value = 0

            for i, seq in enumerate(lowercase_	):
                  _UpperCamelCase					:		int  =				lengths[i]
                  _UpperCamelCase					:		Dict  =				seq[:end]

            return padded_sequences, lengths

      _UpperCamelCase					:		Optional[Any]  =				{}
      for key in data[0].keys():
            _UpperCamelCase					:		List[Any]  =				[d[key] for d in data]

      _UpperCamelCase,     _UpperCamelCase					:		Dict  =				pad_sequences(item_info["X"]	)
      _UpperCamelCase					:		Optional[int]  =				torch.tensor(item_info["y"]						,dtype=torch.long	)

      return x_batch, y_batch




def     lowercase__			(    lowercase_	)    ->						Optional[int]:




      """simple docstring"""

      _UpperCamelCase					:		List[str]  =				{}
      for key in data[0].keys():
            _UpperCamelCase					:		Dict  =				[d[key] for d in data]

      _UpperCamelCase					:		int  =				torch.cat(item_info["X"]						,0	)
      _UpperCamelCase					:		int  =				torch.tensor(item_info["y"]						,dtype=torch.long	)

      return x_batch, y_batch




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_=0						,lowercase_=10						,lowercase_="cpu"	)    ->						str:




      """simple docstring"""

      _UpperCamelCase					:		Optional[Any]  =				0
      discriminator.train_custom()
      for batch_idx, (input_t, target_t) in enumerate(lowercase_	):
            _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				input_t.to(lowercase_	), target_t.to(lowercase_	)

            optimizer.zero_grad()

            _UpperCamelCase					:		int  =				discriminator(lowercase_	)
            _UpperCamelCase					:		Optional[int]  =				nn.functional.nll_loss(lowercase_						,lowercase_	)
            loss.backward(retain_graph=lowercase_	)
            optimizer.step()

            samples_so_far += len(lowercase_	)

            if batch_idx % log_interval == 0:
                  print(
                      "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                          epoch + 1						,lowercase_						,len(data_loader.dataset	)						,100 * samples_so_far / len(data_loader.dataset	)						,loss.item()						,)	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_="cpu"	)    ->						Tuple:




      """simple docstring"""

      discriminator.eval()
      _UpperCamelCase					:		str  =				0
      _UpperCamelCase					:		Union[str, Any]  =				0
      with torch.no_grad():
            for input_t, target_t in data_loader:
                  _UpperCamelCase,     _UpperCamelCase					:		Optional[int]  =				input_t.to(lowercase_	), target_t.to(lowercase_	)
                  _UpperCamelCase					:		Optional[Any]  =				discriminator(lowercase_	)
                  # sum up batch loss
                  test_loss += nn.functional.nll_loss(lowercase_						,lowercase_						,reduction="sum"	).item()
                  # get the index of the max log-probability
                  _UpperCamelCase					:		List[str]  =				output_t.argmax(dim=1						,keepdim=lowercase_	)
                  correct += pred_t.eq(target_t.view_as(lowercase_	)	).sum().item()

      test_loss /= len(data_loader.dataset	)

      print(
          "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
              lowercase_						,lowercase_						,len(data_loader.dataset	)						,100.0 * correct / len(data_loader.dataset	)	)	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_=False						,lowercase_="cpu"	)    ->						List[str]:




      """simple docstring"""

      _UpperCamelCase					:		int  =				model.tokenizer.encode(lowercase_	)
      _UpperCamelCase					:		Tuple  =				torch.tensor([input_t]						,dtype=torch.long						,device=lowercase_	)
      if cached:
            _UpperCamelCase					:		List[Any]  =				model.avg_representation(lowercase_	)

      _UpperCamelCase					:		List[Any]  =				model(lowercase_	).data.cpu().numpy().flatten().tolist()
      print("Input sentence:"						,lowercase_	)
      print(
          "Predictions:"						,", ".join("{}: {:.4f}".format(lowercase_						,math.exp(lowercase_	)	) for c, log_prob in zip(lowercase_						,lowercase_	)	)						,)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_=False						,lowercase_="cpu"	)    ->						Tuple:




      """simple docstring"""

      _UpperCamelCase					:		int  =				torch.utils.data.DataLoader(dataset=lowercase_						,batch_size=lowercase_						,collate_fn=lowercase_	)

      _UpperCamelCase					:		Union[str, Any]  =				[]
      _UpperCamelCase					:		str  =				[]
      for batch_idx, (x, y) in enumerate(tqdm(lowercase_						,ascii=lowercase_	)	):
            with torch.no_grad():
                  _UpperCamelCase					:		Tuple  =				x.to(lowercase_	)
                  _UpperCamelCase					:		Dict  =				discriminator.avg_representation(lowercase_	).cpu().detach()
                  _UpperCamelCase					:		str  =				torch.unbind(avg_rep.unsqueeze(1	)	)
                  xs += avg_rep_list
                  ys += y.cpu().numpy().tolist()

      _UpperCamelCase					:		Optional[int]  =				torch.utils.data.DataLoader(
          dataset=Dataset(lowercase_						,lowercase_	)						,batch_size=lowercase_						,shuffle=lowercase_						,collate_fn=lowercase_	)

      return data_loader




def     lowercase__			(    lowercase_						,lowercase_=None						,lowercase_="gpt2-medium"						,lowercase_=10						,lowercase_=64						,lowercase_=10						,lowercase_=False						,lowercase_=False						,lowercase_=False						,)    ->						Optional[int]:




      """simple docstring"""

      _UpperCamelCase					:		Tuple  =				"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

      print("Preprocessing {} dataset...".format(lowercase_	)	)
      _UpperCamelCase					:		Dict  =				time.time()

      if dataset == "SST":
            _UpperCamelCase					:		List[Any]  =				["positive", "negative", "very positive", "very negative", "neutral"]
            _UpperCamelCase					:		str  =				{c: i for i, c in enumerate(lowercase_	)}

            _UpperCamelCase					:		int  =				Discriminator(
                class_size=len(lowercase_	)						,pretrained_model=lowercase_						,cached_mode=lowercase_						,device=lowercase_	).to(lowercase_	)

            _UpperCamelCase					:		Union[str, Any]  =				torchtext_data.Field()
            _UpperCamelCase					:		str  =				torchtext_data.Field(sequential=lowercase_	)
            _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Optional[int]  =				datasets.SST.splits(
                lowercase_						,lowercase_						,fine_grained=lowercase_						,train_subtrees=lowercase_						,)

            _UpperCamelCase					:		List[str]  =				[]
            _UpperCamelCase					:		Tuple  =				[]
            for i in trange(len(lowercase_	)						,ascii=lowercase_	):
                  _UpperCamelCase					:		Any  =				TreebankWordDetokenizer().detokenize(vars(train_data[i]	)["text"]	)
                  _UpperCamelCase					:		str  =				discriminator.tokenizer.encode(lowercase_	)
                  _UpperCamelCase					:		Dict  =				torch.tensor([50_256] + seq						,device=lowercase_						,dtype=torch.long	)
                  x.append(lowercase_	)
                  y.append(classaidx[vars(train_data[i]	)["label"]]	)
            _UpperCamelCase					:		Optional[int]  =				Dataset(lowercase_						,lowercase_	)

            _UpperCamelCase					:		Any  =				[]
            _UpperCamelCase					:		Optional[int]  =				[]
            for i in trange(len(lowercase_	)						,ascii=lowercase_	):
                  _UpperCamelCase					:		List[str]  =				TreebankWordDetokenizer().detokenize(vars(test_data[i]	)["text"]	)
                  _UpperCamelCase					:		str  =				discriminator.tokenizer.encode(lowercase_	)
                  _UpperCamelCase					:		str  =				torch.tensor([50_256] + seq						,device=lowercase_						,dtype=torch.long	)
                  test_x.append(lowercase_	)
                  test_y.append(classaidx[vars(test_data[i]	)["label"]]	)
            _UpperCamelCase					:		Dict  =				Dataset(lowercase_						,lowercase_	)

            _UpperCamelCase					:		Optional[Any]  =				{
                "class_size": len(lowercase_	),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 2,
            }

      elif dataset == "clickbait":
            _UpperCamelCase					:		int  =				["non_clickbait", "clickbait"]
            _UpperCamelCase					:		str  =				{c: i for i, c in enumerate(lowercase_	)}

            _UpperCamelCase					:		Tuple  =				Discriminator(
                class_size=len(lowercase_	)						,pretrained_model=lowercase_						,cached_mode=lowercase_						,device=lowercase_	).to(lowercase_	)

            with open("datasets/clickbait/clickbait_train_prefix.txt"	) as f:
                  _UpperCamelCase					:		str  =				[]
                  for i, line in enumerate(lowercase_	):
                        try:
                              data.append(eval(lowercase_	)	)
                        except Exception:
                              print("Error evaluating line {}: {}".format(lowercase_						,lowercase_	)	)
                              continue
            _UpperCamelCase					:		Optional[Any]  =				[]
            _UpperCamelCase					:		Optional[Any]  =				[]
            with open("datasets/clickbait/clickbait_train_prefix.txt"	) as f:
                  for i, line in enumerate(tqdm(lowercase_						,ascii=lowercase_	)	):
                        try:
                              _UpperCamelCase					:		Dict  =				eval(lowercase_	)
                              _UpperCamelCase					:		Optional[int]  =				discriminator.tokenizer.encode(d["text"]	)

                              if len(lowercase_	) < max_length_seq:
                                    _UpperCamelCase					:		Any  =				torch.tensor([50_256] + seq						,device=lowercase_						,dtype=torch.long	)
                              else:
                                    print("Line {} is longer than maximum length {}".format(lowercase_						,lowercase_	)	)
                                    continue
                              x.append(lowercase_	)
                              y.append(d["label"]	)
                        except Exception:
                              print("Error evaluating / tokenizing line {}, skipping it".format(lowercase_	)	)
                              pass

            _UpperCamelCase					:		Optional[int]  =				Dataset(lowercase_						,lowercase_	)
            _UpperCamelCase					:		Dict  =				int(0.9 * len(lowercase_	)	)
            _UpperCamelCase					:		Dict  =				len(lowercase_	) - train_size
            _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				torch.utils.data.random_split(lowercase_						,[train_size, test_size]	)

            _UpperCamelCase					:		Any  =				{
                "class_size": len(lowercase_	),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 1,
            }

      elif dataset == "toxic":
            _UpperCamelCase					:		List[Any]  =				["non_toxic", "toxic"]
            _UpperCamelCase					:		Tuple  =				{c: i for i, c in enumerate(lowercase_	)}

            _UpperCamelCase					:		Dict  =				Discriminator(
                class_size=len(lowercase_	)						,pretrained_model=lowercase_						,cached_mode=lowercase_						,device=lowercase_	).to(lowercase_	)

            _UpperCamelCase					:		Union[str, Any]  =				[]
            _UpperCamelCase					:		Any  =				[]
            with open("datasets/toxic/toxic_train.txt"	) as f:
                  for i, line in enumerate(tqdm(lowercase_						,ascii=lowercase_	)	):
                        try:
                              _UpperCamelCase					:		Tuple  =				eval(lowercase_	)
                              _UpperCamelCase					:		Dict  =				discriminator.tokenizer.encode(d["text"]	)

                              if len(lowercase_	) < max_length_seq:
                                    _UpperCamelCase					:		Tuple  =				torch.tensor([50_256] + seq						,device=lowercase_						,dtype=torch.long	)
                              else:
                                    print("Line {} is longer than maximum length {}".format(lowercase_						,lowercase_	)	)
                                    continue
                              x.append(lowercase_	)
                              y.append(int(np.sum(d["label"]	) > 0	)	)
                        except Exception:
                              print("Error evaluating / tokenizing line {}, skipping it".format(lowercase_	)	)
                              pass

            _UpperCamelCase					:		Optional[int]  =				Dataset(lowercase_						,lowercase_	)
            _UpperCamelCase					:		Optional[int]  =				int(0.9 * len(lowercase_	)	)
            _UpperCamelCase					:		Optional[Any]  =				len(lowercase_	) - train_size
            _UpperCamelCase,     _UpperCamelCase					:		Dict  =				torch.utils.data.random_split(lowercase_						,[train_size, test_size]	)

            _UpperCamelCase					:		Optional[Any]  =				{
                "class_size": len(lowercase_	),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 0,
            }

      else:  # if dataset == "generic":
            # This assumes the input dataset is a TSV with the following structure:
            # class \t text

            if dataset_fp is None:
                  raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."	)

            _UpperCamelCase					:		Any  =				set()
            with open(lowercase_	) as f:
                  _UpperCamelCase					:		str  =				csv.reader(lowercase_						,delimiter="\t"	)
                  for row in tqdm(lowercase_						,ascii=lowercase_	):
                        if row:
                              classes.add(row[0]	)

            _UpperCamelCase					:		Tuple  =				sorted(lowercase_	)
            _UpperCamelCase					:		Optional[Any]  =				{c: i for i, c in enumerate(lowercase_	)}

            _UpperCamelCase					:		Tuple  =				Discriminator(
                class_size=len(lowercase_	)						,pretrained_model=lowercase_						,cached_mode=lowercase_						,device=lowercase_	).to(lowercase_	)

            _UpperCamelCase					:		int  =				[]
            _UpperCamelCase					:		int  =				[]
            with open(lowercase_	) as f:
                  _UpperCamelCase					:		List[str]  =				csv.reader(lowercase_						,delimiter="\t"	)
                  for i, row in enumerate(tqdm(lowercase_						,ascii=lowercase_	)	):
                        if row:
                              _UpperCamelCase					:		Any  =				row[0]
                              _UpperCamelCase					:		str  =				row[1]

                              try:
                                    _UpperCamelCase					:		List[str]  =				discriminator.tokenizer.encode(lowercase_	)
                                    if len(lowercase_	) < max_length_seq:
                                          _UpperCamelCase					:		int  =				torch.tensor([50_256] + seq						,device=lowercase_						,dtype=torch.long	)

                                    else:
                                          print("Line {} is longer than maximum length {}".format(lowercase_						,lowercase_	)	)
                                          continue

                                    x.append(lowercase_	)
                                    y.append(classaidx[label]	)

                              except Exception:
                                    print("Error tokenizing line {}, skipping it".format(lowercase_	)	)
                                    pass

            _UpperCamelCase					:		List[Any]  =				Dataset(lowercase_						,lowercase_	)
            _UpperCamelCase					:		Union[str, Any]  =				int(0.9 * len(lowercase_	)	)
            _UpperCamelCase					:		int  =				len(lowercase_	) - train_size
            _UpperCamelCase,     _UpperCamelCase					:		int  =				torch.utils.data.random_split(lowercase_						,[train_size, test_size]	)

            _UpperCamelCase					:		str  =				{
                "class_size": len(lowercase_	),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 0,
            }

      _UpperCamelCase					:		Optional[int]  =				time.time()
      print("Preprocessed {} data points".format(len(lowercase_	) + len(lowercase_	)	)	)
      print("Data preprocessing took: {:.3f}s".format(end - start	)	)

      if cached:
            print("Building representation cache..."	)

            _UpperCamelCase					:		Any  =				time.time()

            _UpperCamelCase					:		Optional[Any]  =				get_cached_data_loader(lowercase_						,lowercase_						,lowercase_						,shuffle=lowercase_						,device=lowercase_	)

            _UpperCamelCase					:		List[str]  =				get_cached_data_loader(lowercase_						,lowercase_						,lowercase_						,device=lowercase_	)

            _UpperCamelCase					:		Any  =				time.time()
            print("Building representation cache took: {:.3f}s".format(end - start	)	)

      else:
            _UpperCamelCase					:		int  =				torch.utils.data.DataLoader(
                dataset=lowercase_						,batch_size=lowercase_						,shuffle=lowercase_						,collate_fn=lowercase_	)
            _UpperCamelCase					:		Any  =				torch.utils.data.DataLoader(dataset=lowercase_						,batch_size=lowercase_						,collate_fn=lowercase_	)

      if save_model:
            with open("{}_classifier_head_meta.json".format(lowercase_	)						,"w"	) as meta_file:
                  json.dump(lowercase_						,lowercase_	)

      _UpperCamelCase					:		List[str]  =				optim.Adam(discriminator.parameters()						,lr=0.0001	)

      for epoch in range(lowercase_	):
            _UpperCamelCase					:		int  =				time.time()
            print("\nEpoch"						,epoch + 1	)

            train_epoch(
                discriminator=lowercase_						,data_loader=lowercase_						,optimizer=lowercase_						,epoch=lowercase_						,log_interval=lowercase_						,device=lowercase_						,)
            evaluate_performance(data_loader=lowercase_						,discriminator=lowercase_						,device=lowercase_	)

            _UpperCamelCase					:		int  =				time.time()
            print("Epoch took: {:.3f}s".format(end - start	)	)

            print("\nExample prediction"	)
            predict(lowercase_						,lowercase_						,lowercase_						,cached=lowercase_						,device=lowercase_	)

            if save_model:
                  # torch.save(discriminator.state_dict(),
                  #           "{}_discriminator_{}.pt".format(
                  #               args.dataset, epoch + 1
                  #               ))
                  torch.save(
                      discriminator.get_classifier().state_dict()						,"{}_classifier_head_epoch_{}.pt".format(lowercase_						,epoch + 1	)						,)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
  parser.add_argument(
      "--dataset",
      type=str,
      default="SST",
      choices=("SST", "clickbait", "toxic", "generic"),
      help=(
          "dataset to train the discriminator on."
          "In case of generic, the dataset is expected"
          "to be a TSBV file with structure: class \\t text"
      ),
  )
  parser.add_argument(
      "--dataset_fp",
      type=str,
      default="",
      help="File path of the dataset to use. Needed only in case of generic datadset",
  )
  parser.add_argument(
      "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
  )
  parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
  parser.add_argument(
      "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
  )
  parser.add_argument(
      "--log_interval",
      type=int,
      default=10,
      metavar="N",
      help="how many batches to wait before logging training status",
  )
  parser.add_argument("--save_model", action="store_true", help="whether to save the model")
  parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
  parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
  lowerCamelCase__      =      parser.parse_args()

  train_discriminator(**(vars(args)))







"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)


lowerCamelCase__      =      "▁"

lowerCamelCase__      =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCamelCase__      =      {
    "google/reformer-crime-and-punishment": 52_4288,
}



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Any		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:str		   =    ReformerTokenizer

  def __init__(						self      :				List[str]    ,      __a      :				str=None    ,      __a      :				List[Any]=None    ,      __a      :				Optional[int]="</s>"    ,      __a      :				str="<unk>"    ,      __a      :				Dict=[]    ,      **__a      :				Tuple    ,      )						-> Optional[int]:
        super().__init__(
            __a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      unk_token=__a    ,      additional_special_tokens=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Dict  =				vocab_file
        _UpperCamelCase					:		Optional[Any]  =				False if not self.vocab_file else True


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

        if not os.path.isdir(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		Optional[Any]  =				os.path.join(
            __a    ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
