




"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_      			=       get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ReformerTokenizer
 lowercase__				      =      ReformerTokenizerFast
 lowercase__				      =      True
 lowercase__				      =      False
 lowercase__				      =      True


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   super().setUp()

   _snake_case							:	Any			=						ReformerTokenizer(a_,   keep_accents=a_	)
   tokenizer.save_pretrained(self.tmpdirname	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						"""<s>"""
   _snake_case							:	Optional[int]			=						1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_	),   a_	)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_	),   a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						list(self.get_tokenizer().get_vocab().keys()	)

   self.assertEqual(vocab_keys[0],   """<unk>"""	)
   self.assertEqual(vocab_keys[1],   """<s>"""	)
   self.assertEqual(vocab_keys[-1],   """j"""	)
   self.assertEqual(len(a_	),   1_000	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,   1_000	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:	Union[str, Any]			=						self.get_tokenizer()
   _snake_case							:	Union[str, Any]			=						self.get_rust_tokenizer()

   _snake_case							:	Tuple			=						"""I was born in 92000, and this is falsé."""

   _snake_case							:	List[Any]			=						tokenizer.tokenize(a_	)
   _snake_case							:	Optional[int]			=						rust_tokenizer.tokenize(a_	)
   self.assertListEqual(a_,   a_	)

   _snake_case							:	Optional[int]			=						tokenizer.encode(a_,   add_special_tokens=a_	)
   _snake_case							:	Dict			=						rust_tokenizer.encode(a_,   add_special_tokens=a_	)
   self.assertListEqual(a_,   a_	)

   _snake_case							:	Tuple			=						self.get_rust_tokenizer()
   _snake_case							:	List[str]			=						tokenizer.encode(a_	)
   _snake_case							:	List[str]			=						rust_tokenizer.encode(a_	)
   self.assertListEqual(a_,   a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	str=15	):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
       _snake_case							:	int			=						self.rust_tokenizer_class.from_pretrained(a_,   **a_	)

       # Simple input
       _snake_case							:	List[Any]			=						"""This is a simple input"""
       _snake_case							:	Union[str, Any]			=						["""This is a simple input 1""", """This is a simple input 2"""]
       _snake_case							:	Union[str, Any]			=						("""This is a simple input""", """This is a pair""")
       _snake_case							:	int			=						[
           ("""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 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ReformerTokenizer(a_,   keep_accents=a_	)

   _snake_case							:	List[str]			=						tokenizer.tokenize("""This is a test"""	)
   self.assertListEqual(a_,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(a_	),   [285, 46, 10, 170, 382],   )

   _snake_case							:	Union[str, Any]			=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
   self.assertListEqual(
       a_,   [
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """9""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """é""",
           """.""",
       ],   )
   _snake_case							:	List[Any]			=						tokenizer.convert_tokens_to_ids(a_	)
   self.assertListEqual(
       a_,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],   )

   _snake_case							:	List[str]			=						tokenizer.convert_ids_to_tokens(a_	)
   self.assertListEqual(
       a_,   [
           SPIECE_UNDERLINE + """I""",
           SPIECE_UNDERLINE + """was""",
           SPIECE_UNDERLINE + """b""",
           """or""",
           """n""",
           SPIECE_UNDERLINE + """in""",
           SPIECE_UNDERLINE + """""",
           """<unk>""",
           """2""",
           """0""",
           """0""",
           """0""",
           """,""",
           SPIECE_UNDERLINE + """and""",
           SPIECE_UNDERLINE + """this""",
           SPIECE_UNDERLINE + """is""",
           SPIECE_UNDERLINE + """f""",
           """al""",
           """s""",
           """<unk>""",
           """.""",
       ],   )


 @cached_property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""	)


 @slow
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						"""Hello World!"""
   _snake_case							:	Tuple			=						[126, 32, 262, 152, 38, 72, 287]

   self.assertListEqual(a_,   self.big_tokenizer.encode(a_	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						(
       """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
       """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
   )
   _snake_case							:	List[Any]			=						[
       108,
       265,
       24,
       111,
       4,
       258,
       156,
       35,
       28,
       275,
       3,
       259,
       297,
       260,
       84,
       4,
       35,
       110,
       44,
       8,
       259,
       91,
       268,
       21,
       11,
       209,
       274,
       109,
       266,
       277,
       117,
       86,
       93,
       315,
       258,
       278,
       258,
       277,
       258,
       0,
       258,
       288,
       258,
       319,
       258,
       0,
       258,
       0,
       258,
       0,
       258,
       0,
       258,
       287,
       258,
       315,
       258,
       289,
       258,
       278,
       99,
       269,
       266,
       262,
       8,
       259,
       241,
       4,
       217,
       230,
       268,
       266,
       55,
       168,
       106,
       75,
       193,
       266,
       223,
       27,
       49,
       26,
       282,
       25,
       264,
       299,
       19,
       26,
       0,
       258,
       277,
       117,
       86,
       93,
       176,
       183,
       270,
       11,
       262,
       42,
       61,
       265,
   ]

   self.assertListEqual(a_,   self.big_tokenizer.encode(a_	)	)


 @require_torch
 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   import torch

   from transformers import ReformerConfig, ReformerModel

   # Build sequence
   _snake_case							:	Optional[int]			=						list(self.big_tokenizer.get_vocab().keys()	)[:10]
   _snake_case							:	Tuple			=						""" """.join(a_	)
   _snake_case							:	Union[str, Any]			=						self.big_tokenizer.encode_plus(a_,   return_tensors="""pt"""	)
   _snake_case							:	List[Any]			=						self.big_tokenizer.batch_encode_plus([sequence, sequence],   return_tensors="""pt"""	)

   _snake_case							:	List[str]			=						ReformerConfig()
   # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
   _snake_case							:	Optional[Any]			=						encoded_sequence["""input_ids"""].shape
   _snake_case							:	Dict			=						ReformerModel(a_	)

   # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
   assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

   with torch.no_grad():
     model(**a_	)
     model(**a_	)


 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Dict			=						{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # This tokenizer does not know some characters like ")".
   # That is the reason why we use very simple texts here.
   # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
   _snake_case							:	Tuple			=						[
       """This is a very simple sentence.""",
       """The quick brown fox jumps over the lazy dog.""",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=a_,   model_name="""google/reformer-crime-and-punishment""",   revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""",   padding=a_,   sequences=a_,   )








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   if self.task == "multiple-choice":
     _snake_case							:	str			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _snake_case							:	List[str]			=						{0: """batch""", 1: """sequence"""}
   _snake_case							:	List[Any]			=						{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]	)


