



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


a_   :Optional[Any]        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}




class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""dinat"""

     _SCREAMING_SNAKE_CASE						=	{
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
     }






     def __init__(       self  :  Optional[Any], _snake_case  :  Dict=4, _snake_case  :  List[Any]=3, _snake_case  :  Tuple=6_4, _snake_case  :  int=[3, 4, 6, 5], _snake_case  :  List[str]=[2, 4, 8, 1_6], _snake_case  :  Dict=7, _snake_case  :  Tuple=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]], _snake_case  :  Any=3.0, _snake_case  :  List[str]=True, _snake_case  :  Optional[int]=0.0, _snake_case  :  Dict=0.0, _snake_case  :  Tuple=0.1, _snake_case  :  List[str]="gelu", _snake_case  :  int=0.0_2, _snake_case  :  str=1e-5, _snake_case  :  List[Any]=0.0, _snake_case  :  Optional[Any]=None, _snake_case  :  Union[str, Any]=None, **_snake_case  :  Optional[Any], )   ->int:
            super().__init__(**_snake_case					)

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











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	TransfoXLTokenizer
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Optional[int]					)   ->Any:
            super().setUp()

            snake_case__							:	Tuple   	=	[
                '<unk>',
                '[CLS]',
                '[SEP]',
                'want',
                'unwanted',
                'wa',
                'un',
                'running',
                ',',
                'low',
                'l',
            ]
            snake_case__							:	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  :  Union[str, Any], **_snake_case  :  List[Any]					)   ->Dict:
            snake_case__							:	str   	=	True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Dict:
            snake_case__							:	List[Any]   	=	'<unk> UNwanted , running'
            snake_case__							:	List[Any]   	=	'<unk> unwanted, running'
            return input_text, output_text






     def    lowercase_   (       self  :  List[Any]					)   ->Tuple:
            snake_case__							:	Dict   	=	TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=_snake_case					)

            snake_case__							:	str   	=	tokenizer.tokenize('<unk> UNwanted , running'					)
            self.assertListEqual(_snake_case, ['<unk>', 'unwanted', ',', 'running']					)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					), [0, 4, 8, 7]					)






     def    lowercase_   (       self  :  List[str]					)   ->List[Any]:
            snake_case__							:	str   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['hello', '!', 'how', 'are', 'you', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            snake_case__							:	Optional[int]   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	TransfoXLTokenizer(lower_case=_snake_case					)
            snake_case__							:	Dict   	=	'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
            snake_case__							:	List[Any]   	=	[
                'Hello',
                '(',
                'bracket',
                ')',
                'and',
                'side',
                '@-@',
                'scrolled',
                '[',
                'and',
                ']',
                'Henry',
                '\'s',
                '$',
                '5',
                '@,@',
                '000',
                'with',
                '3',
                '@.@',
                '34',
                'm',
                '.',
                'What',
                '\'s',
                'up',
                '!',
                '?',
            ]

            self.assertListEqual(tokenizer.tokenize(_snake_case					), _snake_case					)

            self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case					), _snake_case					)






     def    lowercase_   (       self  :  Dict					)   ->Any:
            snake_case__							:	Dict   	=	self.get_tokenizer()
            snake_case__							:	Optional[Any]   	=	len(_snake_case					)

            tokenizer.add_tokens(['new1', 'new2']					)
            tokenizer.move_added_token('new1', 1					)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(_snake_case					), original_len + 2					)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode('new1'					), [1]					)
            self.assertEqual(tokenizer.decode([1]					), 'new1'					)






