



'''simple docstring'''

from typing import List

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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    List[str]   				=					'efficientformer'







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       List[int] = [3, 2, 6, 4]	,_UpperCAmelCase					:       List[int] = [48, 96, 224, 448]	,_UpperCAmelCase					:       List[bool] = [True, True, True, True]	,_UpperCAmelCase					:       int = 448	,_UpperCAmelCase					:       int = 32	,_UpperCAmelCase					:       int = 4	,_UpperCAmelCase					:       int = 7	,_UpperCAmelCase					:       int = 5	,_UpperCAmelCase					:       int = 8	,_UpperCAmelCase					:       int = 4	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       int = 16	,_UpperCAmelCase					:       int = 3	,_UpperCAmelCase					:       int = 3	,_UpperCAmelCase					:       int = 3	,_UpperCAmelCase					:       int = 2	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       bool = True	,_UpperCAmelCase					:       bool = True	,_UpperCAmelCase					:       float = 1E-5	,_UpperCAmelCase					:       str = "gelu"	,_UpperCAmelCase					:       float = 0.02	,_UpperCAmelCase					:       float = 1E-12	,_UpperCAmelCase					:       int = 224	,_UpperCAmelCase					:       float = 1E-05	,**_UpperCAmelCase					:       Union[str, Any]	,):
									super().__init__(**_UpperCAmelCase		)

									_a     :      Optional[Any]        =							hidden_act
									_a     :      int        =							hidden_dropout_prob
									_a     :      Optional[int]        =							hidden_sizes
									_a     :      int        =							num_hidden_layers
									_a     :      Optional[Any]        =							num_attention_heads
									_a     :      Union[str, Any]        =							initializer_range
									_a     :      List[str]        =							layer_norm_eps
									_a     :      List[str]        =							patch_size
									_a     :      Tuple        =							num_channels
									_a     :      Optional[Any]        =							depths
									_a     :      str        =							mlp_expansion_ratio
									_a     :      Dict        =							downsamples
									_a     :      List[str]        =							dim
									_a     :      str        =							key_dim
									_a     :      str        =							attention_ratio
									_a     :      int        =							resolution
									_a     :      List[Any]        =							pool_size
									_a     :      Any        =							downsample_patch_size
									_a     :      str        =							downsample_stride
									_a     :      Tuple        =							downsample_pad
									_a     :      List[str]        =							drop_path_rate
									_a     :      List[Any]        =							num_metaad_blocks
									_a     :      str        =							distillation
									_a     :      Union[str, Any]        =							use_layer_scale
									_a     :      Any        =							layer_scale_init_value
									_a     :      List[Any]        =							image_size
									_a     :      List[Any]        =							batch_norm_eps









'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							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					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




