





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_					= {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TapasForMaskedLM""",
          """TapasForQuestionAnswering""",
          """TapasForSequenceClassification""",
          """TapasModel""",
          """TapasPreTrainedModel""",
          """load_tf_weights_in_tapas""",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFTapasForMaskedLM""",
          """TFTapasForQuestionAnswering""",
          """TFTapasForSequenceClassification""",
          """TFTapasModel""",
          """TFTapasPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      lowercase_					= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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


__lowerCAmelCase		:				List[Any]       			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					GPTSwaTokenizer
				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					True
				SCREAMING_SNAKE_CASE_       :							Any							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							a    =			GPTSwaTokenizer(__lowerCamelCase					,      eos_token="<unk>"					,      bos_token="<unk>"					,      pad_token="<unk>"							)

							tokenizer.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     Any:
							a    =			"This is a test"
							a    =			"This is a test"
							return input_text, output_text





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     List[str]:
							a    =			"<s>"
							a    =			1

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





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							a    =			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(__lowerCamelCase							)					,      20_00							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							self.assertEqual(self.get_tokenizer().vocab_size					,      20_00							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union[str, Any]:
							a    =			GPTSwaTokenizer(__lowerCamelCase							)

							a    =			tokenizer.tokenize("This is a test"							)
							self.assertListEqual(__lowerCamelCase					,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

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

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

							a    =			tokenizer.convert_tokens_to_ids(__lowerCamelCase							)
							self.assertListEqual(
							    __lowerCamelCase					,      [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]					,      )

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





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			GPTSwaTokenizer(__lowerCamelCase							)
							a    =			["This is a test", "I was born in 92000, and this is falsé."]
							a    =			[
							    [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(__lowerCamelCase					,      __lowerCamelCase							):
										self.assertListEqual(tokenizer.encode_fast(__lowerCamelCase							)					,      __lowerCamelCase							)

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





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							a    =			[
							    "<|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
							a    =			{"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=__lowerCamelCase					,      model_name="AI-Sweden/gpt-sw3-126m"					,      sequences=__lowerCamelCase					,      )






