

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       LxmertTokenizer
							UpperCAmelCase__			:		Optional[Any]             =       LxmertTokenizerFast
							UpperCAmelCase__			:		Any             =       True
							UpperCAmelCase__			:		Dict             =       True


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().setUp()

												_a      :       int   =						[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Tuple   =						'''UNwant\u00E9d,running'''
												_a      :       str   =						'''unwanted, running'''
												return input_text, output_text


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.tokenizer_class(self.vocab_file			)

												_a      :       str   =						tokenizer.tokenize('''UNwant\u00E9d,running'''			)
												self.assertListEqual(_a			,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a			)			,				[7, 4, 5, 1_0, 8, 9]			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.test_rust_tokenizer:
																	return

												_a      :       Optional[Any]   =						self.get_tokenizer()
												_a      :       str   =						self.get_rust_tokenizer()

												_a      :       Optional[Any]   =						'''I was born in 92000, and this is falsé.'''

												_a      :       Optional[Any]   =						tokenizer.tokenize(_a			)
												_a      :       List[Any]   =						rust_tokenizer.tokenize(_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       List[Any]   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
												_a      :       Any   =						rust_tokenizer.encode(_a			,				add_special_tokens=_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       Dict   =						self.get_rust_tokenizer()
												_a      :       Optional[int]   =						tokenizer.encode(_a			)
												_a      :       Dict   =						rust_tokenizer.encode(_a			)
												self.assertListEqual(_a			,				_a			)






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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''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 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "funnel"
							UpperCAmelCase__			:		Tuple             =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							}


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=[4, 4, 4]			,				_a=None			,				_a=2			,				_a=7_6_8			,				_a=1_2			,				_a=6_4			,				_a=3_0_7_2			,				_a="gelu_new"			,				_a=0.1			,				_a=0.1			,				_a=0.0			,				_a=0.1			,				_a=None			,				_a=1e-9			,				_a="mean"			,				_a="relative_shift"			,				_a=True			,				_a=True			,				_a=True			,				**_a			,				)       ->       List[Any]:
												_a      :       Optional[int]   =						vocab_size
												_a      :       Dict   =						block_sizes
												_a      :       Optional[int]   =						[1] * len(_a			) if block_repeats is None else block_repeats
												assert len(_a			) == len(
												    self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
												_a      :       int   =						num_decoder_layers
												_a      :       List[str]   =						d_model
												_a      :       Optional[Any]   =						n_head
												_a      :       Tuple   =						d_head
												_a      :       Dict   =						d_inner
												_a      :       List[str]   =						hidden_act
												_a      :       int   =						hidden_dropout
												_a      :       Union[str, Any]   =						attention_dropout
												_a      :       Tuple   =						activation_dropout
												_a      :       Optional[Any]   =						initializer_range
												_a      :       Dict   =						initializer_std
												_a      :       Union[str, Any]   =						layer_norm_eps
												assert pooling_type in [
												    "mean",
												    "max",
												], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
												_a      :       Any   =						pooling_type
												assert attention_type in [
												    "relative_shift",
												    "factorized",
												], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
												_a      :       Optional[Any]   =						attention_type
												_a      :       int   =						separate_cls
												_a      :       Tuple   =						truncate_seq
												_a      :       List[Any]   =						pool_q_only

												super().__init__(**_a			)


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return sum(self.block_sizes			)


							@num_hidden_layers.setter
							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												raise NotImplementedError(
												    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''			)


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return len(self.block_sizes			)




							@num_blocks.setter
							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''			)



