






import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def 	UpperCamelCase	(							snake_case__  : Tuple=32						,snake_case__  : Optional[int]=10						,snake_case__  : int=100						,snake_case__  : Tuple=1026						,snake_case__  : Optional[Any]=True						,snake_case__  : Optional[int]="data/tokenized_stories_train_wikitext103.jbl"						,snake_case__  : Tuple="igf_context_pairs.jbl"						,):




     '''simple docstring'''






     set_seed(3	)
     # generate train_data and objective_set
     __snake_case							,			__snake_case      :Optional[int] 							=    generate_datasets(
         snake_case__						,snake_case__						,number=snake_case__						,min_len=1026						,trim=snake_case__	)
     # keeps model same across runs
     set_seed(4	)
     # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
     # can we train on GPU?
     __snake_case      :str 							=    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""	)

     # load pretrained model
     __snake_case      :List[str] 							=    load_gpta("""gpt2"""	).to(snake_case__	)
     print("""computing perplexity on objective set"""	)
     __snake_case      :Optional[int] 							=    compute_perplexity(snake_case__						,snake_case__						,snake_case__	).item()
     print("""perplexity on objective set:"""						,snake_case__	)

     # collect igf pairs and save to file demo.jbl
     collect_objective_set(snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__	)

     # clean up, delete model and data we don't need anymore
     del model, train_data, objective_set
     torch.cuda.empty_cache()






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : int=15						,snake_case__  : Optional[int]=128						,snake_case__  : Dict=100						,snake_case__  : Tuple="igf_model.pt"						,):




     '''simple docstring'''






     set_seed(42	)

     # Load pre-trained model
     __snake_case      :Dict 							=    GPTaLMHeadModel.from_pretrained("""gpt2"""	)

     # Initialize secondary learner to use embedding weights of model
     __snake_case      :str 							=    SecondaryLearner(snake_case__	)

     # Train secondary learner
     __snake_case      :Any 							=    train_secondary_learner(
         snake_case__						,snake_case__						,max_epochs=snake_case__						,batch_size=snake_case__						,eval_freq=100						,igf_model_path=snake_case__						,)

     del model, secondary_learner_train_data
     torch.cuda.empty_cache()

     return secondary_learner






def 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : List[str]						,snake_case__  : str						,snake_case__  : Optional[Any]=32						,snake_case__  : str=1000						,snake_case__  : List[Any]=16						,snake_case__  : Dict=1.0						,snake_case__  : Dict=recopy_gpta						,snake_case__  : Any=None						,snake_case__  : List[Any]=10						,snake_case__  : List[Any]="gpt2_finetuned.pt"						,):




     '''simple docstring'''






     __snake_case      :List[str] 							=    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""	)
     __snake_case      :Tuple 							=    RandomSampler(snake_case__	)
     __snake_case      :Optional[Any] 							=    DataLoader(snake_case__						,sampler=snake_case__	)

     __snake_case      :int 							=    max_steps // (len(snake_case__	)) + 1
     __snake_case      :Any 							=    0
     __snake_case      :Union[str, Any] 							=    torch.zeros((1, context_len)						,dtype=torch.long						,device=snake_case__	)
     __snake_case							,			__snake_case							,			__snake_case      :List[str] 							=    recopy_model(snake_case__						,snake_case__						,snake_case__	)

     model.train()
     if secondary_learner is not None:
          secondary_learner.to(snake_case__	)
          secondary_learner.eval()
     __snake_case      :int 							=    []
     __snake_case      :int 							=    0

     __snake_case      :Any 							=    []
     __snake_case      :List[str] 							=    []

     # Compute the performance of the transformer model at the beginning
     __snake_case      :int 							=    compute_perplexity(snake_case__						,snake_case__						,snake_case__	)
     test_perps.append(snake_case__	)
     print("""Test perplexity, step"""						,snake_case__						,""":"""						,snake_case__	)
     for epoch in range(int(snake_case__	)	):
          for step, example in enumerate(snake_case__	):
               torch.cuda.empty_cache()
               __snake_case      :Optional[int] 							=    random.randint(0						,example.size(2	) - context_len - 1	)
               __snake_case      :Dict 							=    example[0, 0, start : start + context_len]
               lm_optimizer.zero_grad()
               __snake_case      :Tuple 							=    model(snake_case__						,labels=snake_case__	)
               __snake_case      :str 							=    True

               if secondary_learner is not None:
                    __snake_case      :Any 							=    secondary_learner.forward(
                        torch.tensor(snake_case__						,dtype=torch.long						,device=snake_case__	).unsqueeze(0	)	)[0].item()
                    observed_qs.append(float(snake_case__	)	)

                    # Here we implement the simple non-constant threshold for the predicted IG(X) value
                    # We will decay the selectivity of our secondary learner filter from
                    # 1 standard deviation above average to 1 below average after 10 batches.

                    if global_step == 10:
                         __snake_case      :Optional[Any] 							=    -1
                    if predicted_q < threshold:
                         __snake_case      :Tuple 							=    False

            # If we passed the filter, add the context to the batch!
               if do_backprop:
                    contexts.append(np.array(context.cpu()	)	)
                    __snake_case      :int 							=    outputs[0]
                    lm_loss.backward()
                    examples += 1

               del outputs

               # Once the batch is filled with enough contexts, backprop on the batch.
               if examples == batch_size:
                    torch.cuda.empty_cache()
                    __snake_case      :Optional[Any] 							=    0
                    # Do LM backprop
                    torch.nn.utils.clip_grad_norm_(model.parameters()						,3.0	)
                    lm_optimizer.step()
                    lm_scheduler.step()  # Update learning rate schedule
                    global_step += 1
                    # Compute the performance of the transformer model at this batch
                    if global_step % eval_interval == 0:
                         __snake_case      :List[Any] 							=    compute_perplexity(snake_case__						,snake_case__						,snake_case__	)
                         test_perps.append(snake_case__	)

                         print("""Test perplexity, step"""						,snake_case__						,""":"""						,snake_case__	)
            # Break out of the loop after 60 batches
               if max_steps > 0 and global_step > 60:
                    break
          if max_steps > 0 and global_step > 60:
               break

    # save finetuned transformer model
     torch.save(model.state_dict()						,snake_case__	)
     torch.cuda.empty_cache()
     # Do some cleaning up so we can reinitialize for the next run of this function
     del lm_optimizer
     del lm_scheduler
     return model






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""	)

     # Required parameters
     parser.add_argument(
         """--data_dir"""						,default=snake_case__						,type=snake_case__						,required=snake_case__						,help="""The input data dir. Should contain data files for WikiText."""						,)
     parser.add_argument(
         """--model_name_or_path"""						,default=snake_case__						,type=snake_case__						,required=snake_case__						,help="""Path to pretrained model or model identifier from huggingface.co/models"""						,)
     parser.add_argument(
         """--data_file"""						,type=snake_case__						,default=snake_case__						,help=(
             """A jbl file containing tokenized data which can be split as objective dataset, """
             """train_dataset and test_dataset."""
         )						,)

     parser.add_argument(
         """--igf_data_file"""						,type=snake_case__						,default=snake_case__						,help="""A jbl file containing the context and information gain pairs to train secondary learner."""						,)

     parser.add_argument(
         """--output_dir"""						,default=snake_case__						,type=snake_case__						,required=snake_case__						,help="""The output directory where the final fine-tuned model is stored."""						,)

     parser.add_argument(
         """--tokenizer_name"""						,default=snake_case__						,type=snake_case__						,help="""Pretrained tokenizer name or path if not the same as model_name"""						,)
     parser.add_argument("""--seed"""						,type=snake_case__						,default=snake_case__						,help="""A seed for reproducible training."""	)

     parser.add_argument(
         """--context_len"""						,default=32						,type=snake_case__						,help=(
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )						,)

     parser.add_argument(
         """--size_objective_set"""						,default=100						,type=snake_case__						,help="""number of articles that are long enough to be used as our objective set"""						,)
     parser.add_argument(
         """--eval_freq"""						,default=100						,type=snake_case__						,help="""secondary model evaluation is triggered at eval_freq"""	)

     parser.add_argument("""--max_steps"""						,default=1000						,type=snake_case__						,help="""To calculate training epochs"""	)

     parser.add_argument(
         """--secondary_learner_batch_size"""						,default=128						,type=snake_case__						,help="""batch size of training data for secondary learner"""						,)

     parser.add_argument(
         """--batch_size"""						,default=16						,type=snake_case__						,help="""batch size of training data of language model(gpt2) """	)

     parser.add_argument(
         """--eval_interval"""						,default=10						,type=snake_case__						,help=(
             """decay the selectivity of our secondary learner filter from"""
             """1 standard deviation above average to 1 below average after 10 batches"""
         )						,)

     parser.add_argument(
         """--number"""						,default=100						,type=snake_case__						,help="""The number of examples split to be used as objective_set/test_data"""	)

     parser.add_argument(
         """--min_len"""						,default=1026						,type=snake_case__						,help="""The minimum length of the article to be used as objective set"""	)

     parser.add_argument(
         """--secondary_learner_max_epochs"""						,default=15						,type=snake_case__						,help="""number of epochs to train secondary learner"""	)

     parser.add_argument("""--trim"""						,default=snake_case__						,type=snake_case__						,help="""truncate the example if it exceeds context length"""	)

     parser.add_argument(
         """--threshold"""						,default=1.0						,type=snake_case__						,help=(
             """The threshold value used by secondary learner to filter the train_data and allow only"""
             """ informative data as input to the model"""
         )						,)

     parser.add_argument("""--finetuned_model_name"""						,default="""gpt2_finetuned.pt"""						,type=snake_case__						,help="""finetuned_model_name"""	)

     parser.add_argument(
         """--recopy_model"""						,default=snake_case__						,type=snake_case__						,help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""						,)

     # function calls
     # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
     generate_n_pairs(
         context_len=32						,max_steps=10						,size_objective_set=100						,min_len=1026						,trim=snake_case__						,data_file="""data/tokenized_stories_train_wikitext103.jbl"""						,igf_data_file="""igf_context_pairs.jbl"""						,)

     # Load train data for secondary learner
     __snake_case      :Tuple 							=    joblib.load("""data/IGF_values.jbl"""	)

     # Train secondary learner
     __snake_case      :Dict 							=    training_secondary_learner(
         snake_case__						,secondary_learner_max_epochs=15						,secondary_learner_batch_size=128						,eval_freq=100						,igf_model_path="""igf_model.pt"""						,)

     # load pretrained gpt2 model
     __snake_case      :Union[str, Any] 							=    GPTaLMHeadModel.from_pretrained("""gpt2"""	)
     set_seed(42	)

     # Generate train and test data to train and evaluate gpt2 model
     __snake_case							,			__snake_case      :Tuple 							=    generate_datasets(
         context_len=32						,file="""data/tokenized_stories_train_wikitext103.jbl"""						,number=100						,min_len=1026						,trim=snake_case__	)

     # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
     finetune(
         snake_case__						,snake_case__						,snake_case__						,context_len=32						,max_steps=1000						,batch_size=16						,threshold=1.0						,recopy_model=snake_case__						,secondary_learner=snake_case__						,eval_interval=10						,finetuned_model_name="""gpt2_finetuned.pt"""						,)


if __name__ == "__main__":
     main()










import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			GPTaTokenizer
       lowerCamelCase				:					Optional[int]								=			GPTaTokenizerFast
       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					List[str]								=			{"add_prefix_space": True}
       lowerCamelCase				:					Optional[int]								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case      :Optional[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
                """<|endoftext|>""",
            ]
            __snake_case      :Dict 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :List[Any] 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :List[str] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Dict 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[Any] 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write(json.dumps(a__     ) + """\n"""     )
            with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(a__     )     )



       def        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :Any 							=    """lower newer"""
            return input_text, output_text



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    GPTaTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map     )
            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :int 							=    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __snake_case      :Any 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[Any] 							=    tokens + [tokenizer.unk_token]
            __snake_case      :List[Any] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()
            __snake_case      :Optional[Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )

            __snake_case      :Optional[int] 							=    """lower newer"""

            # Testing tokenization
            __snake_case      :List[Any] 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.tokenize(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids without special tokens
            __snake_case      :List[str] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.encode(a__      ,    add_special_tokens=a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids with special tokens
            __snake_case      :Union[str, Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    tokenizer.encode(a__      ,    add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    rust_tokenizer.encode(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing the unknown token
            __snake_case      :Dict 							=    tokens + [rust_tokenizer.unk_token]
            __snake_case      :Optional[int] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   Any:






            '''simple docstring'''





            pass



       def        __lowercase    (				self      ,    a__=15     )			->   Optional[Any]:






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Optional[int] 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )

                      # Simple input
                      __snake_case      :List[str] 							=    """This is a simple input"""
                      __snake_case      :List[str] 							=    ["""This is a simple input 1""", """This is a simple input 2"""]
                      __snake_case      :Tuple 							=    ("""This is a simple input""", """This is a pair""")
                      __snake_case      :Union[str, Any] 							=    [
                          ("""This is a simple input 1""", """This is a simple input 2"""),
                          ("""This is a simple pair 1""", """This is a simple pair 2"""),
                      ]

                      # Simple input tests
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    pad_token="""<pad>"""     )

            # Simple input
            __snake_case      :Tuple 							=    """This is a simple input"""
            __snake_case      :Optional[int] 							=    ["""This is a simple input looooooooong""", """This is a simple input"""]
            __snake_case      :List[str] 							=    ("""This is a simple input""", """This is a pair""")
            __snake_case      :List[str] 							=    [
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            __snake_case      :Tuple 							=    tokenizer.pad_token_id

            __snake_case      :int 							=    tokenizer(a__      ,    padding="""max_length"""      ,    max_length=30      ,    return_tensors="""np"""     )
            __snake_case      :Optional[Any] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )
            __snake_case      :List[Any] 							=    tokenizer(*a__      ,    padding="""max_length"""      ,    max_length=60      ,    return_tensors="""np"""     )
            __snake_case      :Optional[int] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )

            # s
            # test single string max_length padding
            self.assertEqual(out_s["""input_ids"""].shape[-1]      ,    30     )
            self.assertTrue(pad_token_id in out_s["""input_ids"""]     )
            self.assertTrue(0 in out_s["""attention_mask"""]     )

            # s2
            # test automatic padding
            self.assertEqual(out_sa["""input_ids"""].shape[-1]      ,    33     )
            # long slice doesn't have padding
            self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]     )
            self.assertFalse(0 in out_sa["""attention_mask"""][0]     )
            # short slice does have padding
            self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]     )
            self.assertTrue(0 in out_sa["""attention_mask"""][1]     )

            # p
            # test single pair max_length padding
            self.assertEqual(out_p["""input_ids"""].shape[-1]      ,    60     )
            self.assertTrue(pad_token_id in out_p["""input_ids"""]     )
            self.assertTrue(0 in out_p["""attention_mask"""]     )

            # p2
            # test automatic padding pair
            self.assertEqual(out_pa["""input_ids"""].shape[-1]      ,    52     )
            # long slice pair doesn't have padding
            self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]     )
            self.assertFalse(0 in out_pa["""attention_mask"""][0]     )
            # short slice pair does have padding
            self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]     )
            self.assertTrue(0 in out_pa["""attention_mask"""][1]     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    """$$$"""
            __snake_case      :Optional[Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    bos_token=a__      ,    add_bos_token=a__     )

            __snake_case      :List[Any] 							=    """This is a simple input"""
            __snake_case      :Tuple 							=    ["""This is a simple input 1""", """This is a simple input 2"""]

            __snake_case      :Tuple 							=    tokenizer.bos_token_id

            __snake_case      :List[str] 							=    tokenizer(a__     )
            __snake_case      :Any 							=    tokenizer(a__     )

            self.assertEqual(out_s.input_ids[0]      ,    a__     )
            self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

            __snake_case      :int 							=    tokenizer.decode(out_s.input_ids     )
            __snake_case      :Tuple 							=    tokenizer.batch_decode(out_sa.input_ids     )

            self.assertEqual(decode_s.split()[0]      ,    a__     )
            self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    [self.get_tokenizer(do_lower_case=a__      ,    add_bos_token=a__     )]
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                      __snake_case      :Tuple 							=    """Encode this."""
                      __snake_case      :Tuple 							=    """This one too please."""
                      __snake_case      :Union[str, Any] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      encoded_sequence += tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      __snake_case      :List[str] 							=    tokenizer.encode_plus(
                          a__      ,    a__      ,    add_special_tokens=a__      ,    return_special_tokens_mask=a__      ,    )
                      __snake_case      :Union[str, Any] 							=    encoded_sequence_dict["""input_ids"""]
                      __snake_case      :Optional[Any] 							=    encoded_sequence_dict["""special_tokens_mask"""]
                      self.assertEqual(len(a__     )      ,    len(a__     )     )

                      __snake_case      :Optional[int] 							=    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__     )
                      ]
                      __snake_case      :str 							=    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(a__      ,    a__     )




@require_tokenizers
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )
            __snake_case      :Union[str, Any] 							=    """A photo of a cat"""

            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""test_opt"""     )

            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""./test_opt"""     )
            __snake_case      :Tuple 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    use_slow=a__     )
            __snake_case      :str 							=    """A photo of a cat"""

            __snake_case      :List[str] 							=    tokenizer.encode(
                a__      ,    )
            # Same as above
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""     )
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )

            __snake_case      :Optional[Any] 							=    """bos"""
            __snake_case      :int 							=    tokenizer.get_vocab()["""bos"""]

            __snake_case      :str 							=    """A photo of a cat"""
            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            # We changed the bos token
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""./tok"""     )
            __snake_case      :Dict 							=    AutoTokenizer.from_pretrained("""./tok"""     )
            self.assertTrue(tokenizer.is_fast     )
            __snake_case      :List[Any] 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


