


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase  :		List[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Dict		     =	{
    '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
    '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
    '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
    '''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
    '''funnel-transformer/intermediate''': (
        '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
    ),
    '''funnel-transformer/intermediate-base''': (
        '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
    ),
    '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
    '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
    '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
    '''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}



class  lowerCAmelCase				(				a__							):
	_lowerCamelCase   :					Dict			      = """funnel"""
	_lowerCamelCase   :					Any			      = {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	}



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=[4, 4, 4] ,					snake_case__=None ,					snake_case__=2 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=64 ,					snake_case__=3072 ,					snake_case__="gelu_new" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=0.0 ,					snake_case__=0.1 ,					snake_case__=None ,					snake_case__=1e-9 ,					snake_case__="mean" ,					snake_case__="relative_shift" ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					**snake_case__ ,					):
		lowerCAmelCase					:    Tuple      							= vocab_size
		lowerCAmelCase					:    List[Any]      							= block_sizes
		lowerCAmelCase					:    Dict      							= [1] * len(lowerCAmelCase__    ) if block_repeats is None else block_repeats
		assert len(lowerCAmelCase__    ) == len(
		    self.block_repeats    ), "`block_sizes` and `block_repeats` should have the same length."
		lowerCAmelCase					:    Dict      							= num_decoder_layers
		lowerCAmelCase					:    Optional[int]      							= d_model
		lowerCAmelCase					:    str      							= n_head
		lowerCAmelCase					:    Optional[Any]      							= d_head
		lowerCAmelCase					:    Union[str, Any]      							= d_inner
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    List[str]      							= hidden_dropout
		lowerCAmelCase					:    Any      							= attention_dropout
		lowerCAmelCase					:    Optional[int]      							= activation_dropout
		lowerCAmelCase					:    List[str]      							= initializer_range
		lowerCAmelCase					:    List[str]      							= initializer_std
		lowerCAmelCase					:    Any      							= layer_norm_eps
		assert pooling_type in [
		    "mean",
		    "max",
		], f"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
		lowerCAmelCase					:    Optional[int]      							= pooling_type
		assert attention_type in [
		    "relative_shift",
		    "factorized",
		], f"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
		lowerCAmelCase					:    int      							= attention_type
		lowerCAmelCase					:    Optional[Any]      							= separate_cls
		lowerCAmelCase					:    List[str]      							= truncate_seq
		lowerCAmelCase					:    str      							= pool_q_only

		super().__init__(**lowerCAmelCase__    )



	@property
	def 				lowercase     (					self    ):
		return sum(self.block_sizes    )



	@num_hidden_layers.setter
	def 				lowercase     (					self ,					snake_case__    ):
		raise NotImplementedError(
		    'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'    )



	@property
	def 				lowercase     (					self    ):
		return len(self.block_sizes    )



	@num_blocks.setter
	def 				lowercase     (					self ,					snake_case__    ):
		raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.'    )




'''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


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Tuple			      = GPTSwaTokenizer
	_lowerCamelCase   :					str			      = False
	_lowerCamelCase   :					Dict			      = True
	_lowerCamelCase   :					Optional[Any]			      = False



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

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    Tuple      							= GPTSwaTokenizer(snake_case__ ,					eos_token='<unk>' ,					bos_token='<unk>' ,					pad_token='<unk>'    )

		tokenizer.save_pretrained(self.tmpdirname    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		return input_text, output_text



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= '<s>'
		lowerCAmelCase					:    Optional[int]      							= 1

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= 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(snake_case__    ) ,					2000    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					2000    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= GPTSwaTokenizer(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.tokenize('This is a test'    )
		self.assertListEqual(snake_case__ ,					['▁This', '▁is', '▁a', '▁t', 'est']    )

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

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

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

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= GPTSwaTokenizer(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= ['This is a test', 'I was born in 92000, and this is falsé.']
		lowerCAmelCase					:    Tuple      							= [
		    [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(snake_case__ ,					snake_case__    ):
			self.assertListEqual(tokenizer.encode_fast(snake_case__    ) ,					snake_case__    )

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



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    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
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 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=snake_case__ ,					model_name='AI-Sweden/gpt-sw3-126m' ,					sequences=snake_case__ ,					)
