


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


_snake_case					:		List[str]      =    get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")

@require_sentencepiece
@require_tokenizers
class     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  GPTSwaTokenizer
							__UpperCAmelCase   :							str					  =  False
							__UpperCAmelCase   :							Tuple					  =  True
							__UpperCAmelCase   :							Dict					  =  False

							def    __snake_case (							self	:  int    )     ->   Dict:
														super().setUp()

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

														tokenizer.save_pretrained(self.tmpdirname    )

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

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

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

							def    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														__snake_case		:      str										=       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    )   ,     2000    )

							def    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														self.assertEqual(self.get_tokenizer().vocab_size   ,     2000    )

							def    __snake_case (							self	:  Tuple    )     ->   str:
														__snake_case		:      Union[str, Any]										=       GPTSwaTokenizer(lowerCamelCase    )

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

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

														__snake_case		:      List[Any]										=       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

														__snake_case		:      List[Any]										=       tokenizer.convert_tokens_to_ids(lowerCamelCase    )
														self.assertListEqual(
														    lowerCamelCase   ,     [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]   ,     )

														__snake_case		:      int										=       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    __snake_case (							self	:  int    )     ->   int:
														__snake_case		:      Tuple										=       GPTSwaTokenizer(lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       ["This is a test", "I was born in 92000, and this is falsé."]
														__snake_case		:      Union[str, Any]										=       [
														    [465, 287, 265, 631, 842],
														    [262, 272, 1525, 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(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    __snake_case (							self	:  List[Any]    )     ->   int:
														__snake_case		:      List[Any]										=       [
														    "<|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
														__snake_case		:      Dict										=       {"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 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   ,     )





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

