

"""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					:List[str]    = get_tests_dir("fixtures/test_sentencepiece.model")

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






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = ReformerTokenizer
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = ReformerTokenizerFast
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = True
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = False
     _SCREAMING_SNAKE_CASE						:List[Any]       = True

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


           """simple docstring"""


           super().setUp()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ReformerTokenizer(_a	,    keep_accents=_a  )
           tokenizer.save_pretrained(self.tmpdirname  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	"""<s>"""
           SCREAMING_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 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[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 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

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


           """simple docstring"""


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE__   :      int      		=	"""I was born in 92000, and this is falsé."""

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.tokenize(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	rust_tokenizer.tokenize(_a  )
           self.assertListEqual(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.encode(_a	,    add_special_tokens=_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rust_tokenizer.encode(_a	,    add_special_tokens=_a  )
           self.assertListEqual(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.encode(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	rust_tokenizer.encode(_a  )
           self.assertListEqual(_a	,    _a  )

     def 					_a			(						self	,    _a=15  )  ->      Union[str, Any]:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )

                       # Simple input
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""This is a simple input"""
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""This is a simple input 1""", """This is a simple input 2"""]
                       SCREAMING_SNAKE_CASE__   :      int      		=	("""This is a simple input""", """This is a pair""")
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	[
                           ("""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 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           pass

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ReformerTokenizer(_a	,    keep_accents=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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]	,    )

           SCREAMING_SNAKE_CASE__   :      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""",
                   """.""",
               ]	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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]	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


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

     @slow
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	"""Hello World!"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[126, 32, 262, 152, 38, 72, 287]

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

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	(
               """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"""
           )
           SCREAMING_SNAKE_CASE__   :      int      		=	[
               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 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           SCREAMING_SNAKE_CASE__   :      Dict      		=	list(self.big_tokenizer.get_vocab().keys()  )[:10]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	""" """.join(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	self.big_tokenizer.encode_plus(_a	,    return_tensors="""pt"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.big_tokenizer.batch_encode_plus([sequence, sequence]	,    return_tensors="""pt"""  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoded_sequence["""input_ids"""].shape
           SCREAMING_SNAKE_CASE__   :      Any      		=	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 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""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
           SCREAMING_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 typing import Mapping

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


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




