


'''simple docstring'''






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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}




class 						a__							(				a__							,							a__    ):





							'''simple docstring'''






							lowercase__     :  str      		= "focalnet"







							def __init__(						self   ,       lowerCamelCase_=2_24   ,       lowerCamelCase_=4   ,       lowerCamelCase_=3   ,       lowerCamelCase_=96   ,       lowerCamelCase_=False   ,       lowerCamelCase_=[1_92, 3_84, 7_68, 7_68]   ,       lowerCamelCase_=[2, 2, 6, 2]   ,       lowerCamelCase_=[2, 2, 2, 2]   ,       lowerCamelCase_=[3, 3, 3, 3]   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=4.0   ,       lowerCamelCase_=0.0   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=False   ,       lowerCamelCase_=1e-4   ,       lowerCamelCase_=False   ,       lowerCamelCase_=False   ,       lowerCamelCase_=False   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=1e-5   ,       lowerCamelCase_=32   ,       lowerCamelCase_=None   ,       lowerCamelCase_=None   ,       **lowerCamelCase_   ,       )			->				str:
														super().__init__(**lowerCamelCase_  )

														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      patch_size
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      embed_dim
														lowerCAmelCase__			  =      use_conv_embed
														lowerCAmelCase__			  =      hidden_sizes
														lowerCAmelCase__			  =      depths
														lowerCAmelCase__			  =      focal_levels
														lowerCAmelCase__			  =      focal_windows
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      mlp_ratio
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      drop_path_rate
														lowerCAmelCase__			  =      use_layerscale
														lowerCAmelCase__			  =      layerscale_value
														lowerCAmelCase__			  =      use_post_layernorm
														lowerCAmelCase__			  =      use_post_layernorm_in_modulation
														lowerCAmelCase__			  =      normalize_modulator
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      layer_norm_eps
														lowerCAmelCase__			  =      encoder_stride
														lowerCAmelCase__			  =      ['''stem'''] + [F"""stage{idx}""" for idx in range(1   ,       len(self.depths  ) + 1  )]
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      get_aligned_output_features_output_indices(
														    out_features=lowerCamelCase_   ,       out_indices=lowerCamelCase_   ,       stage_names=self.stage_names  )






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
