






'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    HerbertTokenizer
  _a     :   Optional[int]			 =    HerbertTokenizerFast
  _a     :   Optional[int]			 =    True







  def       _a					( self  ):






         """simple docstring"""


         super().setUp()

         # Use a simpler test file without japanese/chinese characters
         with open(f'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'      ,  encoding='utf-8'  ) as f_data:
                a_	    = f_data.read().replace('\n\n'      ,  '\n'  ).strip()

         a_	    = [
             '<s>',
             '</s>',
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             ',</w>',
             '<unk>',
         ]
         a_	    = dict(zip(UpperCamelCase__      ,  range(len(UpperCamelCase__  )  )  )  )
         a_	    = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

         a_	    = os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']  )
         a_	    = os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['merges_file']  )
         with open(self.vocab_file      ,  'w'  ) as fp:
                fp.write(json.dumps(UpperCamelCase__  )  )
         with open(self.merges_file      ,  'w'  ) as fp:
                fp.write('\n'.join(UpperCamelCase__  )  )







  def       _a					( self      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = 'lower newer'
         a_	    = 'lower newer'
         return input_text, output_text







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.tokenizer_class(vocab_file=self.vocab_file      ,  merges_file=self.merges_file  )

         a_	    = 'lower'
         a_	    = ['low', 'er</w>']
         a_	    = tokenizer.tokenize(UpperCamelCase__  )
         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )

         a_	    = tokens + ['<unk>']
         a_	    = [16, 17, 23]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__  )      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         if not self.test_rust_tokenizer:
                return

         a_	    = self.get_tokenizer()
         a_	    = self.get_rust_tokenizer()

         a_	    = 'lower,newer'

         a_	    = tokenizer.tokenize(UpperCamelCase__  )
         a_	    = rust_tokenizer.tokenize(UpperCamelCase__  )
         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )

         a_	    = tokenizer.encode(UpperCamelCase__      ,  add_special_tokens=UpperCamelCase__  )
         a_	    = rust_tokenizer.encode(UpperCamelCase__      ,  add_special_tokens=UpperCamelCase__  )
         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )

         a_	    = self.get_rust_tokenizer()
         a_	    = tokenizer.encode(UpperCamelCase__  )
         a_	    = rust_tokenizer.encode(UpperCamelCase__  )
         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )







  @slow
  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'  )

         a_	    = tokenizer.encode('konstruowanie sekwencji'      ,  add_special_tokens=UpperCamelCase__  )
         a_	    = tokenizer.encode('konstruowanie wielu sekwencji'      ,  add_special_tokens=UpperCamelCase__  )

         a_	    = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__  )
         a_	    = tokenizer.build_inputs_with_special_tokens(UpperCamelCase__      ,  UpperCamelCase__  )

         assert encoded_sentence == [0] + text + [2]
         assert encoded_pair == [0] + text + [2] + text_a + [2]












'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''nystromformer'''







  def __init__( self      ,  UpperCamelCase__=30_000      ,  UpperCamelCase__=768      ,  UpperCamelCase__=12      ,  UpperCamelCase__=12      ,  UpperCamelCase__=3_072      ,  UpperCamelCase__="gelu_new"      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=510      ,  UpperCamelCase__=2      ,  UpperCamelCase__=64      ,  UpperCamelCase__=64      ,  UpperCamelCase__=65      ,  UpperCamelCase__=False      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=1e-5      ,  UpperCamelCase__=1      ,  UpperCamelCase__=0      ,  UpperCamelCase__=2      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = vocab_size
         a_	    = max_position_embeddings
         a_	    = hidden_size
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = hidden_act
         a_	    = hidden_dropout_prob
         a_	    = attention_probs_dropout_prob
         a_	    = initializer_range
         a_	    = type_vocab_size
         a_	    = segment_means_seq_len
         a_	    = num_landmarks
         a_	    = conv_kernel_size
         a_	    = inv_coeff_init_option
         a_	    = layer_norm_eps
         super().__init__(pad_token_id=UpperCamelCase__      ,  bos_token_id=UpperCamelCase__      ,  eos_token_id=UpperCamelCase__      ,  **UpperCamelCase__  )




