



"""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


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






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

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

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






							def snake_case_  (   self      ):
									a_     :      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_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

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

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

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

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

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

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






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|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_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 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=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									# Common config
									a_     :      Optional[int] 						=     vocab_size
									a_     :      List[Any] 						=     max_position_embeddings
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      Optional[Any] 						=     retriever_proj_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      Tuple 						=     num_candidates
									a_     :      str 						=     intermediate_size
									a_     :      Optional[int] 						=     hidden_act
									a_     :      List[str] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      Tuple 						=     initializer_range
									a_     :      Tuple 						=     type_vocab_size
									a_     :      str 						=     layer_norm_eps

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size