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

@require_sentencepiece
@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							ReformerTokenizer
   __lowerCAmelCase				=							ReformerTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							True
   def      __magic_name__     (     self							):
          super().setUp()

          lowercase       :							Optional[int]									=				ReformerTokenizer(_a      ,       keep_accents=_a							)
          tokenizer.save_pretrained(self.tmpdirname							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"<s>"
          lowercase       :							Dict									=				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a							)      ,       _a							)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							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      __magic_name__     (     self							):
          self.assertEqual(self.get_tokenizer().vocab_size      ,       1_000							)
   def      __magic_name__     (     self							):
          if not self.test_rust_tokenizer:
                 return

          lowercase       :							int									=				self.get_tokenizer()
          lowercase       :							List[str]									=				self.get_rust_tokenizer()

          lowercase       :							List[str]									=				"I was born in 92000, and this is falsé."

          lowercase       :							Optional[int]									=				tokenizer.tokenize(_a							)
          lowercase       :							Union[str, Any]									=				rust_tokenizer.tokenize(_a							)
          self.assertListEqual(_a      ,       _a							)

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

          lowercase       :							Optional[Any]									=				self.get_rust_tokenizer()
          lowercase       :							List[str]									=				tokenizer.encode(_a							)
          lowercase       :							Union[str, Any]									=				rust_tokenizer.encode(_a							)
          self.assertListEqual(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							Any									=				self.rust_tokenizer_class.from_pretrained(_a      ,       **_a							)

                        # Simple input
                        lowercase       :							Optional[int]									=				"This is a simple input"
                        lowercase       :							Optional[int]									=				["This is a simple input 1", "This is a simple input 2"]
                        lowercase       :							Optional[Any]									=				("This is a simple input", "This is a pair")
                        lowercase       :							Optional[Any]									=				[
                            ("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      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				ReformerTokenizer(_a      ,       keep_accents=_a							)

          lowercase       :							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]      ,       )

          lowercase       :							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",
                  ".",
              ]      ,       )
          lowercase       :							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]      ,       )

          lowercase       :							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      __magic_name__     (     self							):
          return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"							)
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"Hello World!"
          lowercase       :							List[Any]									=				[126, 32, 262, 152, 38, 72, 287]

          self.assertListEqual(_a      ,       self.big_tokenizer.encode(_a							)							)
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, 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"
          )
          lowercase       :							Union[str, 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      __magic_name__     (     self							):
          import torch

          from transformers import ReformerConfig, ReformerModel

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

          lowercase       :							Optional[Any]									=				ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          lowercase       :							Tuple									=				encoded_sequence["input_ids"].shape
          lowercase       :							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      __magic_name__     (     self							):
          # fmt: off
          lowercase       :							List[str]									=				{"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
          lowercase       :							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     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Optional[Any]									=				max_position_embeddings
          lowercase       :							str									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							List[Any]									=				intermediate_size
          lowercase       :							int									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Tuple									=				initializer_range
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				use_cache

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


