


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase_      : Optional[int]  					=       logging.getLogger(__name__)







def        SCREAMING_SNAKE_CASE_						(			)	->		Optional[int]:

       """simple docstring"""

       a_					:		Optional[int]							=  argparse.ArgumentParser(
           description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'				)
       parser.add_argument('--file_path'  ,    type=__A  ,    default='data/dump.txt'  ,    help='The path to the data.'				)
       parser.add_argument('--tokenizer_type'  ,    type=__A  ,    default='bert'  ,    choices=['bert', 'roberta', 'gpt2']				)
       parser.add_argument('--tokenizer_name'  ,    type=__A  ,    default='bert-base-uncased'  ,    help='The tokenizer to use.'				)
       parser.add_argument('--dump_file'  ,    type=__A  ,    default='data/dump'  ,    help='The dump file prefix.'				)
       a_					:		str							=  parser.parse_args()

       logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""				)
       if args.tokenizer_type == "bert":
              a_					:		Any							=  BertTokenizer.from_pretrained(args.tokenizer_name				)
              a_					:		Dict							=  tokenizer.special_tokens_map['cls_token']  # `[CLS]`
              a_					:		Optional[int]							=  tokenizer.special_tokens_map['sep_token']  # `[SEP]`
       elif args.tokenizer_type == "roberta":
              a_					:		List[str]							=  RobertaTokenizer.from_pretrained(args.tokenizer_name				)
              a_					:		Optional[int]							=  tokenizer.special_tokens_map['cls_token']  # `<s>`
              a_					:		Any							=  tokenizer.special_tokens_map['sep_token']  # `</s>`
       elif args.tokenizer_type == "gpt2":
              a_					:		Union[str, Any]							=  GPTaTokenizer.from_pretrained(args.tokenizer_name				)
              a_					:		Any							=  tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
              a_					:		Tuple							=  tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

       logger.info(F"""Loading text from {args.file_path}"""				)
       with open(args.file_path  ,    'r'  ,    encoding='utf8'				) as fp:
              a_					:		List[Any]							=  fp.readlines()

       logger.info('Start encoding'				)
       logger.info(F"""{len(__A				)} examples to process."""				)

       a_					:		Tuple							=  []
       a_					:		Dict							=  0
       a_					:		Any							=  1_00_00
       a_					:		Optional[int]							=  time.time()
       for text in data:
              a_					:		Optional[Any]							=  F"""{bos} {text.strip()} {sep}"""
              a_					:		Optional[Any]							=  tokenizer.encode(__A  ,    add_special_tokens=__A				)
              rslt.append(__A				)

              iter += 1
              if iter % interval == 0:
                     a_					:		Dict							=  time.time()
                     logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""				)
                     a_					:		Optional[int]							=  time.time()
       logger.info('Finished binarization'				)
       logger.info(F"""{len(__A				)} examples processed."""				)

       a_					:		List[Any]							=  F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
       a_					:		List[str]							=  tokenizer.vocab_size
       if vocab_size < (1 << 16):
              a_					:		List[str]							=  [np.uintaa(__A				) for d in rslt]
       else:
              a_					:		List[Any]							=  [np.intaa(__A				) for d in rslt]
       random.shuffle(rslt_				)
       logger.info(F"""Dump to {dp_file}"""				)
       with open(__A  ,    'wb'				) as handle:
              pickle.dump(rslt_  ,    __A  ,    protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
   main()








from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy as np
 import tensorflow as tf

 from transformers import (
     TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     FlaubertConfig,
     TFFlaubertForMultipleChoice,
     TFFlaubertForQuestionAnsweringSimple,
     TFFlaubertForSequenceClassification,
     TFFlaubertForTokenClassification,
     TFFlaubertModel,
     TFFlaubertWithLMHeadModel,
 )







class   A:



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  Optional[Any]						,							)   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    13
       lowerCamelCase_		     =    7
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    False
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    99
       lowerCamelCase_		     =    0
       lowerCamelCase_		     =    32
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    4
       lowerCamelCase_		     =    0.1
       lowerCamelCase_		     =    0.1
       lowerCamelCase_		     =    512
       lowerCamelCase_		     =    16
       lowerCamelCase_		     =    2
       lowerCamelCase_		     =    0.02
       lowerCamelCase_		     =    3
       lowerCamelCase_		     =    4
       lowerCamelCase_		     =    'last'
       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    0

 def 				a__ (				self					:  Dict  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )
       lowerCamelCase_		     =    random_attention_mask([self.batch_size, self.seq_length]						,							dtype=tf.floataa  )

       lowerCamelCase_		     =    None
       if self.use_input_lengths:
             lowerCamelCase_		     =    (
                 ids_tensor([self.batch_size]						,							vocab_size=2  ) + self.seq_length - 2
             )  # small variation of seq_length

       lowerCamelCase_		     =    None
       if self.use_token_type_ids:
             lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.n_langs  )

       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None
       if self.use_labels:
             lowerCamelCase_		     =    ids_tensor([self.batch_size]						,							self.type_sequence_label_size  )
             lowerCamelCase_		     =    ids_tensor([self.batch_size, self.seq_length]						,							self.num_labels  )
             lowerCamelCase_		     =    ids_tensor([self.batch_size]						,							2						,							dtype=tf.floataa  )
             lowerCamelCase_		     =    ids_tensor([self.batch_size]						,							self.num_choices  )

       lowerCamelCase_		     =    FlaubertConfig(
           vocab_size=self.vocab_size						,							n_special=self.n_special						,							emb_dim=self.hidden_size						,							n_layers=self.num_hidden_layers						,							n_heads=self.num_attention_heads						,							dropout=self.hidden_dropout_prob						,							attention_dropout=self.attention_probs_dropout_prob						,							gelu_activation=self.gelu_activation						,							sinusoidal_embeddings=self.sinusoidal_embeddings						,							asm=self.asm						,							causal=self.causal						,							n_langs=self.n_langs						,							max_position_embeddings=self.max_position_embeddings						,							initializer_range=self.initializer_range						,							summary_type=self.summary_type						,							use_proj=self.use_proj						,							bos_token_id=self.bos_token_id						,							)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )

 def 				a__ (				self					:  int						,							A_					:  List[str]						,							A_					:  List[Any]						,							A_					:  str						,							A_					:  List[Any]						,							A_					:  int						,							A_					:  Tuple						,							A_					:  Optional[int]						,							A_					:  Optional[int]						,							A_					:  str						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModel(config=A_  )
       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       lowerCamelCase_		     =    model(A_  )

       lowerCamelCase_		     =    [input_ids, input_mask]
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)  )

 def 				a__ (				self					:  Tuple						,							A_					:  List[str]						,							A_					:  int						,							A_					:  List[Any]						,							A_					:  Any						,							A_					:  Any						,							A_					:  Dict						,							A_					:  str						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertWithLMHeadModel(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)  )

 def 				a__ (				self					:  str						,							A_					:  Tuple						,							A_					:  Any						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Dict						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							A_					:  Optional[int]						,							A_					:  List[Any]						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertForQuestionAnsweringSimple(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths}

       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.start_logits.shape						,							(self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape						,							(self.batch_size, self.seq_length)  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  List[Any]						,							A_					:  str						,							A_					:  List[str]						,							A_					:  Dict						,							A_					:  Optional[Any]						,							A_					:  Tuple						,							A_					:  str						,							A_					:  Optional[int]						,							A_					:  Tuple						,							)   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertForSequenceClassification(A_  )

       lowerCamelCase_		     =    {'input_ids': input_ids, 'lengths': input_lengths}

       lowerCamelCase_		     =    model(A_  )

       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size)  )

 def 				a__ (				self					:  Dict						,							A_					:  Optional[Any]						,							A_					:  List[Any]						,							A_					:  int						,							A_					:  Any						,							A_					:  Union[str, Any]						,							A_					:  str						,							A_					:  Any						,							A_					:  Union[str, Any]						,							A_					:  List[str]						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.num_labels
       lowerCamelCase_		     =    TFFlaubertForTokenClassification(config=A_  )
       lowerCamelCase_		     =    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels)  )

 def 				a__ (				self					:  List[Any]						,							A_					:  Optional[int]						,							A_					:  List[Any]						,							A_					:  Optional[int]						,							A_					:  Tuple						,							A_					:  Union[str, Any]						,							A_					:  int						,							A_					:  str						,							A_					:  Tuple						,							A_					:  str						,							)   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.num_choices
       lowerCamelCase_		     =    TFFlaubertForMultipleChoice(config=A_  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_		     =    {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       lowerCamelCase_		     =    model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_choices)  )





 def 				a__ (				self					:  Union[str, Any]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				(
           lowerCamelCase_
       )     ,				
       )		     =    config_and_inputs
       lowerCamelCase_		     =    {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict







@require_tf
class   A(						UpperCamelCase							,      UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						(
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCamelCase						   =						(
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 UpperCamelCase						   =						(
     {
         '''feature-extraction''': TFFlaubertModel,
         '''fill-mask''': TFFlaubertWithLMHeadModel,
         '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
         '''text-classification''': TFFlaubertForSequenceClassification,
         '''token-classification''': TFFlaubertForTokenClassification,
         '''zero-shot''': TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						   =						False
 UpperCamelCase						   =						False

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Union[str, Any]						,							A_					:  str						,							A_					:  List[str]  )   ->	Optional[Any]:

       """simple docstring"""






       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'  )
       ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

       return False

 def 				a__ (				self					:  List[str]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModelTester(self  )
       lowerCamelCase_		     =    ConfigTester(self						,							config_class=A_						,							emb_dim=37  )

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

       """simple docstring"""






       self.config_tester.run_common_tests()

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

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*A_  )

 def 				a__ (				self					:  List[str]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*A_  )

 def 				a__ (				self					:  Dict  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*A_  )

 def 				a__ (				self					:  Union[str, Any]  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*A_  )

 def 				a__ (				self					:  List[Any]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_token_classification(*A_  )

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

       """simple docstring"""






       lowerCamelCase_		     =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_  )





 @slow
 def 				a__ (				self					:  Union[str, Any]  )   ->	Optional[int]:

       """simple docstring"""






       for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_		     =    TFFlaubertModel.from_pretrained(A_  )
             self.assertIsNotNone(A_  )







@require_tf
@require_sentencepiece
@require_tokenizers
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @slow
 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'  )

       lowerCamelCase_		     =    tf.convert_to_tensor(
           [[0, 158, 735, 2592, 1424, 6727, 82, 1]]						,							dtype=tf.intaa						,							)  # "J'aime flaubert !"

       lowerCamelCase_		     =    model(A_  )[0]
       lowerCamelCase_		     =    tf.TensorShape((1, 8, 512)  )
       self.assertEqual(output.shape						,							A_  )
       # compare the actual values for a slice.
       lowerCamelCase_		     =    tf.convert_to_tensor(
           [
               [
                   [-1.8768773, -1.566555, 0.27072418],
                   [-1.6920038, -0.5873505, 1.9329599],
                   [-2.9563985, -1.6993835, 1.7972052],
               ]
           ]						,							dtype=tf.floataa						,							)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()						,							expected_slice.numpy()						,							atol=1E-4  )  )



