




'''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       A   (		_SCREAMING_SNAKE_CASE					,unittest.TestCase   ):
							lowercase_          =    LayoutLMTokenizer
							lowercase_          =    LayoutLMTokenizerFast
							lowercase_          =    True
							lowercase_          =    True







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Optional[int]:


														"""simple docstring"""


														super().setUp()

														_a     					=    [
														    """[UNK]""",
														    """[CLS]""",
														    """[SEP]""",
														    """want""",
														    """##want""",
														    """##ed""",
														    """wa""",
														    """un""",
														    """runn""",
														    """##ing""",
														    """,""",
														    """low""",
														    """lowest""",
														]
														_a     					=    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        __lowerCAmelCase      (  self	:  Optional[int]							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Dict:


														"""simple docstring"""


														return LayoutLMTokenizer.from_pretrained(self.tmpdirname							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Optional[Any] )      ->							Tuple:


														"""simple docstring"""


														_a     					=    """UNwant\u00E9d,running"""
														_a     					=    """unwanted, running"""
														return input_text, output_text







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    self.tokenizer_class(self.vocab_file )

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







							def        __lowerCAmelCase      (  self	:  Any )      ->							str:


														"""simple docstring"""


														pass







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

