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


SCREAMING_SNAKE_CASE		:     Any         =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")

@require_sentencepiece
@require_tokenizers
class        UpperCamelCase						(				__a	,	unittest.TestCase				):
						a__		:Tuple      =			GPTSwaTokenizer
						a__		:Optional[int]      =			False
						a__		:Union[str, Any]      =			True
						a__		:Any      =			False





						def        A_							(self						)				->				List[Any]:
												super().setUp()

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

												tokenizer.save_pretrained(self.tmpdirname						)





						def        A_							(self			,     __UpperCamelCase						)				->				Any:
												UpperCamelCase_		:							str  =		"""This is a test"""
												UpperCamelCase_		:							Dict  =		"""This is a test"""
												return input_text, output_text





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		"""<s>"""
												UpperCamelCase_		:							Any  =		1

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





						def        A_							(self						)				->				Optional[Any]:
												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(__UpperCamelCase						)			,     2_000						)





						def        A_							(self						)				->				Dict:
												self.assertEqual(self.get_tokenizer().vocab_size			,     2_000						)





						def        A_							(self						)				->				Optional[int]:
												UpperCamelCase_		:							Tuple  =		GPTSwaTokenizer(__UpperCamelCase						)

												UpperCamelCase_		:							List[str]  =		tokenizer.tokenize("""This is a test"""						)
												self.assertListEqual(__UpperCamelCase			,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase						)			,     [465, 287, 265, 631, 842]						)

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

												UpperCamelCase_		:							Any  =		tokenizer.convert_tokens_to_ids(__UpperCamelCase						)
												self.assertListEqual(
												    __UpperCamelCase			,     [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]			,     )

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





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							List[Any]  =		GPTSwaTokenizer(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		["""This is a test""", """I was born in 92000, and this is falsé."""]
												UpperCamelCase_		:							Tuple  =		[
												    [465, 287, 265, 631, 842],
												    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
												]

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

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





						@slow
						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		[
												    """<|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_		:							str  =		{"""input_ids""": [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 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=__UpperCamelCase			,     model_name="""AI-Sweden/gpt-sw3-126m"""			,     sequences=__UpperCamelCase			,     )







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






