'''simple docstring'''




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a   :	Dict 							=   get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 		__A       (__UpperCAmelCase ,					unittest.TestCase   ):
       snake_case       :List[str]				    =					GPTSwaTokenizer
       snake_case       :Dict				    =					False
       snake_case       :int				    =					True
       snake_case       :List[Any]				    =					False






       def 			_snake_case						(      self      ):
              super().setUp()

              # We have a SentencePiece fixture for testing
              __UpperCAmelCase  :				List[str]       				=		GPTSwaTokenizer(__SCREAMING_SNAKE_CASE				,     eos_token="<unk>"				,     bos_token="<unk>"				,     pad_token="<unk>"      )

              tokenizer.save_pretrained(self.tmpdirname      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		"This is a test"
              __UpperCAmelCase  :				List[str]       				=		"This is a test"
              return input_text, output_text






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		"<s>"
              __UpperCAmelCase  :				Optional[Any]       				=		1

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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		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(__SCREAMING_SNAKE_CASE      )				,     20_00      )






       def 			_snake_case						(      self      ):
              self.assertEqual(self.get_tokenizer().vocab_size				,     20_00      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		GPTSwaTokenizer(__SCREAMING_SNAKE_CASE      )

              __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.tokenize("This is a test"      )
              self.assertListEqual(__SCREAMING_SNAKE_CASE				,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

              self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )				,     [4_65, 2_87, 2_65, 6_31, 8_42]      )

              __UpperCAmelCase  :				int       				=		tokenizer.tokenize("I was born in 92000, and this is falsé."      )
              # fmt: off
              self.assertListEqual(
                  __SCREAMING_SNAKE_CASE				,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]				,     )
              # fmt: on

              __UpperCAmelCase  :				Tuple       				=		tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )
              self.assertListEqual(
                  __SCREAMING_SNAKE_CASE				,     [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]				,     )

              __UpperCAmelCase  :				List[str]       				=		tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE      )
              # fmt: off
              self.assertListEqual(
                  __SCREAMING_SNAKE_CASE				,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
              # fmt: on






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		GPTSwaTokenizer(__SCREAMING_SNAKE_CASE      )
              __UpperCAmelCase  :				Optional[int]       				=		["This is a test", "I was born in 92000, and this is falsé."]
              __UpperCAmelCase  :				Optional[Any]       				=		[
                  [4_65, 2_87, 2_65, 6_31, 8_42],
                  [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
              ]

              # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
              for text, expected_ids in zip(__SCREAMING_SNAKE_CASE				,     __SCREAMING_SNAKE_CASE      ):
                     self.assertListEqual(tokenizer.encode_fast(__SCREAMING_SNAKE_CASE      )				,     __SCREAMING_SNAKE_CASE      )

              # Test that decode_fast returns the input text
              for text, token_ids in zip(__SCREAMING_SNAKE_CASE				,     __SCREAMING_SNAKE_CASE      ):
                     self.assertEqual(tokenizer.decode_fast(__SCREAMING_SNAKE_CASE      )				,     __SCREAMING_SNAKE_CASE      )







       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		[
                  "<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')",
                  "Hey there, how are you doing this fine day?",
                  "This is a text with a trailing spaces followed by a dot     .",
                  "Häj sväjs lillebrör! =)",
                  "Det är inget fel på Mr. Cool",
              ]

              # fmt: off
              __UpperCAmelCase  :				Optional[int]       				=		{"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
              # fmt: on
              self.tokenizer_integration_test_util(
                  expected_encoding=__SCREAMING_SNAKE_CASE				,     model_name="AI-Sweden/gpt-sw3-126m"				,     sequences=__SCREAMING_SNAKE_CASE				,     )







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






