



'''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 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  ReformerTokenizer
 UpperCAmelCase							=  ReformerTokenizerFast
 UpperCAmelCase							=  True
 UpperCAmelCase							=  False
 UpperCAmelCase							=  True







 def _snake_case (							self							)		->   Tuple:
        super().setUp()

        _UpperCAmelCase  :						Tuple			     = ReformerTokenizer(a_  ,keep_accents=a_							)
        tokenizer.save_pretrained(self.tmpdirname							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						Any			     = """<s>"""
        _UpperCAmelCase  :						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 _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Any			     = 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 _snake_case (							self							)		->   Tuple:
        self.assertEqual(self.get_tokenizer().vocab_size  ,1_000							)







 def _snake_case (							self							)		->   List[str]:
        if not self.test_rust_tokenizer:
               return

        _UpperCAmelCase  :						int			     = self.get_tokenizer()
        _UpperCAmelCase  :						Optional[int]			     = self.get_rust_tokenizer()

        _UpperCAmelCase  :						str			     = """I was born in 92000, and this is falsé."""

        _UpperCAmelCase  :						str			     = tokenizer.tokenize(a_							)
        _UpperCAmelCase  :						Optional[int]			     = rust_tokenizer.tokenize(a_							)
        self.assertListEqual(a_  ,a_							)

        _UpperCAmelCase  :						List[Any]			     = tokenizer.encode(a_  ,add_special_tokens=a_							)
        _UpperCAmelCase  :						Optional[int]			     = rust_tokenizer.encode(a_  ,add_special_tokens=a_							)
        self.assertListEqual(a_  ,a_							)

        _UpperCAmelCase  :						str			     = self.get_rust_tokenizer()
        _UpperCAmelCase  :						Union[str, Any]			     = tokenizer.encode(a_							)
        _UpperCAmelCase  :						Optional[int]			     = rust_tokenizer.encode(a_							)
        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self  ,a_=15							)		->   str:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                      _UpperCAmelCase  :						Optional[int]			     = self.rust_tokenizer_class.from_pretrained(a_  ,**a_							)

                      # Simple input
                      _UpperCAmelCase  :						Dict			     = """This is a simple input"""
                      _UpperCAmelCase  :						List[Any]			     = ["""This is a simple input 1""", """This is a simple input 2"""]
                      _UpperCAmelCase  :						Union[str, Any]			     = ("""This is a simple input""", """This is a pair""")
                      _UpperCAmelCase  :						List[str]			     = [
                          ("""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 _snake_case (							self							)		->   Tuple:
        pass







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Optional[Any]			     = ReformerTokenizer(a_  ,keep_accents=a_							)

        _UpperCAmelCase  :						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]  ,)

        _UpperCAmelCase  :						Optional[int]			     = 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""",
                """.""",
            ]  ,)
        _UpperCAmelCase  :						Dict			     = 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]  ,)

        _UpperCAmelCase  :						int			     = 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 _snake_case (							self							)		->   str:
        return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)







 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Tuple			     = """Hello World!"""
        _UpperCAmelCase  :						Dict			     = [126, 32, 262, 152, 38, 72, 287]

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







 @slow
 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = (
            """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"""
        )
        _UpperCAmelCase  :						Dict			     = [
            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 _snake_case (							self							)		->   Tuple:
        import torch

        from transformers import ReformerConfig, ReformerModel

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

        _UpperCAmelCase  :						Any			     = ReformerConfig()
        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
        _UpperCAmelCase  :						List[str]			     = encoded_sequence["""input_ids"""].shape
        _UpperCAmelCase  :						List[str]			     = 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 _snake_case (							self							)		->   str:
        # fmt: off
        _UpperCAmelCase  :						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
        _UpperCAmelCase  :						Dict			     = [
            """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 __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



