


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roformer.modeling_flax_roformer import (
							    FlaxRoFormerForMaskedLM,
							    FlaxRoFormerForMultipleChoice,
							    FlaxRoFormerForQuestionAnswering,
							    FlaxRoFormerForSequenceClassification,
							    FlaxRoFormerForTokenClassification,
							    FlaxRoFormerModel,
							)







class 							UpperCamelCase__ (						unittest.TestCase ):

					def __init__(  self							: List[Any]				,       UpperCamelCase__							: Tuple				,       UpperCamelCase__							: Optional[Any]=13				,       UpperCamelCase__							: List[Any]=7				,       UpperCamelCase__							: Optional[Any]=True				,       UpperCamelCase__							: List[Any]=True				,       UpperCamelCase__							: List[str]=True				,       UpperCamelCase__							: List[str]=True				,       UpperCamelCase__							: List[Any]=99				,       UpperCamelCase__							: Optional[Any]=32				,       UpperCamelCase__							: List[Any]=5				,       UpperCamelCase__							: Any=4				,       UpperCamelCase__							: Union[str, Any]=37				,       UpperCamelCase__							: List[Any]="gelu"				,       UpperCamelCase__							: Any=0.1				,       UpperCamelCase__							: str=0.1				,       UpperCamelCase__							: List[Any]=512				,       UpperCamelCase__							: List[str]=16				,       UpperCamelCase__							: Optional[Any]=2				,       UpperCamelCase__							: List[Any]=0.02				,       UpperCamelCase__							: Any=4				,       ):






										'''simple docstring'''






										lowercase_					=      parent
										lowercase_					=      batch_size
										lowercase_					=      seq_length
										lowercase_					=      is_training
										lowercase_					=      use_attention_mask
										lowercase_					=      use_token_type_ids
										lowercase_					=      use_labels
										lowercase_					=      vocab_size
										lowercase_					=      hidden_size
										lowercase_					=      num_hidden_layers
										lowercase_					=      num_attention_heads
										lowercase_					=      intermediate_size
										lowercase_					=      hidden_act
										lowercase_					=      hidden_dropout_prob
										lowercase_					=      attention_probs_dropout_prob
										lowercase_					=      max_position_embeddings
										lowercase_					=      type_vocab_size
										lowercase_					=      type_sequence_label_size
										lowercase_					=      initializer_range
										lowercase_					=      num_choices

					def 	UpperCAmelCase__							(  self							: List[Any]							):






										'''simple docstring'''






										lowercase_					=      ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size							)

										lowercase_					=      None
										if self.use_attention_mask:
															lowercase_					=      random_attention_mask([self.batch_size, self.seq_length]							)

										lowercase_					=      None
										if self.use_token_type_ids:
															lowercase_					=      ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size							)

										lowercase_					=      RoFormerConfig(
										    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       is_decoder=_snake_case				,       initializer_range=self.initializer_range				,       )

										return config, input_ids, token_type_ids, attention_mask







					def 	UpperCAmelCase__							(  self							: Any							):






										'''simple docstring'''






										lowercase_					=      self.prepare_config_and_inputs()
										lowercase_				,       lowercase_				,       lowercase_				,       lowercase_					=      config_and_inputs
										lowercase_					=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
										return config, inputs_dict







@require_flax
class 							UpperCamelCase__ (						a__				,					unittest.TestCase ):
					__SCREAMING_SNAKE_CASE							:	str            = True

					__SCREAMING_SNAKE_CASE							:	Any            = (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										lowercase_					=      FlaxRoFormerModelTester(self							)







					@slow
					def 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										for model_class_name in self.all_model_classes:
															lowercase_					=      model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,       from_pt=_snake_case							)
															lowercase_					=      model(np.ones((1, 1)							)							)
															self.assertIsNotNone(_snake_case							)







@require_flax
class 							UpperCamelCase__ (						unittest.TestCase ):

					@slow
					def 	UpperCAmelCase__							(  self							: str							):






										'''simple docstring'''






										lowercase_					=      FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""							)
										lowercase_					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
										lowercase_					=      model(_snake_case							)[0]

										lowercase_					=      50_000

										lowercase_					=      (1, 6, vocab_size)
										self.assertEqual(output.shape				,       _snake_case							)

										lowercase_					=      jnp.array(
										    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

										self.assertTrue(jnp.allclose(output[:, :3, :3]				,       _snake_case				,       atol=1e-4							)							)









"""simple docstring"""
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     :						str						       =  logging.getLogger(__name__)

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

 logger.info(F'Loading Tokenizer ({args.tokenizer_name})'			)
 if args.tokenizer_type == "bert":
  lowerCAmelCase    				= BertTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  lowerCAmelCase    				= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['cls_token']  # `<s>`
  lowerCAmelCase    				= tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  lowerCAmelCase    				= tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  lowerCAmelCase    				= 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:
  lowerCAmelCase    				= fp.readlines()

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

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

  iter += 1
  if iter % interval == 0:
   lowerCAmelCase    				= time.time()
   logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
   lowerCAmelCase    				= time.time()
 logger.info('Finished binarization'			)
 logger.info(F'{len(_UpperCAmelCase			)} examples processed.'			)

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


if __name__ == "__main__":
       main()





