
'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class UpperCAmelCase__      (					UpperCamelCase__       ,							unittest.TestCase      ):





  """simple docstring"""

  __UpperCAmelCase							:							List[Any]    					=   LongformerTokenizer
  __UpperCAmelCase							:							List[str]    					=   True
  __UpperCAmelCase							:							Optional[Any]    					=   LongformerTokenizerFast
  __UpperCAmelCase							:							Tuple    					=   True


  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     _a							:      Optional[Any]     =  [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     _a							:      Optional[Any]     =  dict(zip(__A				,range(len(__A			)			)			)			)
     _a							:      Any     =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     _a							:      List[str]     =  {'unk_token': '<unk>'}

     _a							:      Any     =  os.path.join(self.tmpdirname				,VOCAB_FILES_NAMES['vocab_file']			)
     _a							:      str     =  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 :	str				,**_a :	Tuple			):


     '''simple docstring'''






     kwargs.update(self.special_tokens_map			)
     return self.tokenizer_class.from_pretrained(self.tmpdirname				,**__A			)


  def     __lowercase						(						self :	Union[str, Any]				,**_a :	str			):


     '''simple docstring'''






     kwargs.update(self.special_tokens_map			)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,**__A			)


  def     __lowercase						(						self :	List[Any]				,_a :	Optional[Any]			):


     '''simple docstring'''






     _a							:      Dict     =  'lower newer'
     _a							:      Any     =  'lower newer'
     return input_text, output_text


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






     _a							:      Any     =  self.tokenizer_class(self.vocab_file				,self.merges_file				,**self.special_tokens_map			)
     _a							:      Union[str, Any]     =  'lower newer'
     _a							:      Any     =  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
     _a							:      Dict     =  tokenizer.tokenize(__A			)  # , add_prefix_space=True)
     self.assertListEqual(__A				,__A			)

     _a							:      Any     =  tokens + [tokenizer.unk_token]
     _a							:      str     =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A			)				,__A			)


  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     _a							:      Tuple     =  self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('Hello world!'				,add_special_tokens=__A			)				,[0, 3_1414, 232, 328, 2]			)
     self.assertListEqual(
         tokenizer.encode('Hello world! cécé herlolip 418'				,add_special_tokens=__A			)				,[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]				,)


  @slow
  def     __lowercase						(						self :	Any			):


     '''simple docstring'''






     _a							:      Tuple     =  self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'			)

     _a							:      Optional[int]     =  tokenizer.encode('sequence builders'				,add_special_tokens=__A			)
     _a							:      Dict     =  tokenizer.encode('multi-sequence build'				,add_special_tokens=__A			)

     _a							:      List[str]     =  tokenizer.encode(
         'sequence builders'				,add_special_tokens=__A				,add_prefix_space=__A			)
     _a							:      Union[str, Any]     =  tokenizer.encode(
         'sequence builders'				,'multi-sequence build'				,add_special_tokens=__A				,add_prefix_space=__A			)

     _a							:      Any     =  tokenizer.build_inputs_with_special_tokens(__A			)
     _a							:      Any     =  tokenizer.build_inputs_with_special_tokens(__A				,__A			)

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode


  def     __lowercase						(						self :	List[str]			):


     '''simple docstring'''






     _a							:      List[str]     =  self.get_tokenizer()

     _a							:      List[str]     =  'Encode this sequence.'
     _a							:      Dict     =  tokenizer.byte_encoder[' '.encode('utf-8'			)[0]]

     # Testing encoder arguments
     _a							:      Optional[Any]     =  tokenizer.encode(__A				,add_special_tokens=__A				,add_prefix_space=__A			)
     _a							:      Any     =  tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
     self.assertNotEqual(__A				,__A			)

     _a							:      Union[str, Any]     =  tokenizer.encode(__A				,add_special_tokens=__A				,add_prefix_space=__A			)
     _a							:      Any     =  tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
     self.assertEqual(__A				,__A			)

     tokenizer.add_special_tokens({'bos_token': '<s>'}			)
     _a							:      List[Any]     =  tokenizer.encode(__A				,add_special_tokens=__A			)
     _a							:      Tuple     =  tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
     self.assertNotEqual(__A				,__A			)

     # Testing spaces after special tokens
     _a							:      Union[str, Any]     =  '<mask>'
     tokenizer.add_special_tokens(
         {'mask_token': AddedToken(__A				,lstrip=__A				,rstrip=__A			)}			)  # mask token has a left space
     _a							:      Any     =  tokenizer.convert_tokens_to_ids(__A			)

     _a							:      int     =  'Encode <mask> sequence'
     _a							:      Any     =  'Encode <mask>sequence'

     _a							:      str     =  tokenizer.encode(__A			)
     _a							:      Union[str, Any]     =  encoded.index(__A			)
     _a							:      List[str]     =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
     self.assertEqual(__A				,__A			)

     _a							:      Any     =  tokenizer.encode(__A			)
     _a							:      Optional[int]     =  encoded.index(__A			)
     _a							:      Optional[Any]     =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
     self.assertNotEqual(__A				,__A			)


  def     __lowercase						(						self :	Tuple			):


     '''simple docstring'''






     pass


  def     __lowercase						(						self :	List[str]			):


     '''simple docstring'''






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
           _a							:      str     =  self.rust_tokenizer_class.from_pretrained(__A				,**__A			)
           _a							:      str     =  self.tokenizer_class.from_pretrained(__A				,**__A			)
           _a							:      Optional[int]     =  'A, <mask> AllenNLP sentence.'
           _a							:      int     =  tokenizer_r.encode_plus(__A				,add_special_tokens=__A				,return_token_type_ids=__A			)
           _a							:      Dict     =  tokenizer_p.encode_plus(__A				,add_special_tokens=__A				,return_token_type_ids=__A			)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r['token_type_ids']			)				,sum(tokens_p['token_type_ids']			)			)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)				,sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)				,)

           _a							:      Tuple     =  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
           _a							:      Optional[int]     =  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p['input_ids']				,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)
           self.assertSequenceEqual(tokens_r['input_ids']				,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)

           self.assertSequenceEqual(
               __A				,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
           self.assertSequenceEqual(
               __A				,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)


  def     __lowercase						(						self :	Optional[int]			):


     '''simple docstring'''






     for trim_offsets, add_prefix_space in itertools.product([True, False]				,repeat=2			):
        _a							:      str     =  self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)

        _a							:      Optional[int]     =  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
        _a							:      Dict     =  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

        self.assertEqual(pre_tokenizer_state['add_prefix_space']				,__A			)

        self.assertEqual(post_processor_state['add_prefix_space']				,__A			)
        self.assertEqual(post_processor_state['trim_offsets']				,__A			)


  def     __lowercase						(						self :	Optional[int]			):


     '''simple docstring'''






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
           _a							:      Any     =  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
           _a							:      Optional[Any]     =  F"""{text_of_1_token} {text_of_1_token}"""

           _a							:      Optional[Any]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      List[str]     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(len(__A			) + 1, len(__A			) + 1 + len(__A			))				,)

           _a							:      int     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      Optional[int]     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(len(__A			) + 1, len(__A			) + 1 + len(__A			))				,)

           _a							:      Optional[Any]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      Tuple     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(len(__A			), len(__A			) + 1 + len(__A			))				,)

           _a							:      List[str]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      Any     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(len(__A			), len(__A			) + 1 + len(__A			))				,)

           _a							:      Optional[Any]     =  F""" {text}"""

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           _a							:      Optional[int]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      str     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(1, 1 + len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(1 + len(__A			) + 1, 1 + len(__A			) + 1 + len(__A			))				,)

           _a							:      Optional[Any]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      Union[str, Any]     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, 1 + len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(1 + len(__A			), 1 + len(__A			) + 1 + len(__A			))				,)

           _a							:      List[Any]     =  self.rust_tokenizer_class.from_pretrained(
               __A				,use_fast=__A				,add_prefix_space=__A				,trim_offsets=__A			)
           _a							:      Optional[Any]     =  tokenizer_r(__A				,return_offsets_mapping=__A				,add_special_tokens=__A			)
           self.assertEqual(encoding.offset_mapping[0]				,(0, 1 + len(__A			))			)
           self.assertEqual(
               encoding.offset_mapping[1]				,(1 + len(__A			), 1 + len(__A			) + 1 + len(__A			))				,)










from torch import nn






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		if act_fn in ["swish", "silu"]:
				return nn.SiLU()
		elif act_fn == "mish":
				return nn.Mish()
		elif act_fn == "gelu":
				return nn.GELU()
		else:
				raise ValueError(f'''Unsupported activation function: {act_fn}'''     )