



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_ (__UpperCAmelCase , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				BioGptTokenizer
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             UpperCAmelCase_  :	Dict				      =		[
                 "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>",
             ]
             UpperCAmelCase_  :	int				      =		dict(zip(lowerCAmelCase_     ,range(len(lowerCAmelCase_))))
             UpperCAmelCase_  :	int				      =		["l o 123", "lo w 1456", "e r</w> 1789", ""]

             UpperCAmelCase_  :	Optional[int]				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"])
             UpperCAmelCase_  :	Optional[int]				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["merges_file"])
             with open(self.vocab_file     ,"w") as fp:
                   fp.write(json.dumps(lowerCAmelCase_))
             with open(self.merges_file     ,"w") as fp:
                   fp.write("\n".join(lowerCAmelCase_))



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		"lower newer"
             UpperCAmelCase_  :	List[str]				      =		"lower newer"
             return input_text, output_text



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		BioGptTokenizer(self.vocab_file     ,self.merges_file)

             UpperCAmelCase_  :	Dict				      =		"lower"
             UpperCAmelCase_  :	Optional[int]				      =		["low", "er</w>"]
             UpperCAmelCase_  :	str				      =		tokenizer.tokenize(lowerCAmelCase_)
             self.assertListEqual(lowerCAmelCase_     ,lowerCAmelCase_)

             UpperCAmelCase_  :	List[Any]				      =		tokens + ["<unk>"]
             UpperCAmelCase_  :	Tuple				      =		[14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)     ,lowerCAmelCase_)



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		BioGptTokenizer.from_pretrained("microsoft/biogpt")

             UpperCAmelCase_  :	List[str]				      =		tokenizer.encode("sequence builders"     ,add_special_tokens=lowerCAmelCase_)
             UpperCAmelCase_  :	int				      =		tokenizer.encode("multi-sequence build"     ,add_special_tokens=lowerCAmelCase_)

             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
             UpperCAmelCase_  :	Dict				      =		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_     ,lowerCAmelCase_)

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





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


__lowerCamelCase         =			logging.get_logger(__name__)

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


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""focalnet"""



       def __init__(     self     ,lowercase=224     ,lowercase=4     ,lowercase=3     ,lowercase=96     ,lowercase=False     ,lowercase=[192, 384, 768, 768]     ,lowercase=[2, 2, 6, 2]     ,lowercase=[2, 2, 2, 2]     ,lowercase=[3, 3, 3, 3]     ,lowercase="gelu"     ,lowercase=4.0     ,lowercase=0.0     ,lowercase=0.1     ,lowercase=False     ,lowercase=1E-4     ,lowercase=False     ,lowercase=False     ,lowercase=False     ,lowercase=0.02     ,lowercase=1E-5     ,lowercase=32     ,lowercase=None     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	Optional[Any]				      =		patch_size
             UpperCAmelCase_  :	Dict				      =		num_channels
             UpperCAmelCase_  :	Dict				      =		embed_dim
             UpperCAmelCase_  :	Optional[int]				      =		use_conv_embed
             UpperCAmelCase_  :	int				      =		hidden_sizes
             UpperCAmelCase_  :	Optional[int]				      =		depths
             UpperCAmelCase_  :	Optional[Any]				      =		focal_levels
             UpperCAmelCase_  :	Tuple				      =		focal_windows
             UpperCAmelCase_  :	int				      =		hidden_act
             UpperCAmelCase_  :	Any				      =		mlp_ratio
             UpperCAmelCase_  :	List[Any]				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Tuple				      =		drop_path_rate
             UpperCAmelCase_  :	List[str]				      =		use_layerscale
             UpperCAmelCase_  :	List[str]				      =		layerscale_value
             UpperCAmelCase_  :	List[str]				      =		use_post_layernorm
             UpperCAmelCase_  :	Dict				      =		use_post_layernorm_in_modulation
             UpperCAmelCase_  :	int				      =		normalize_modulator
             UpperCAmelCase_  :	Dict				      =		initializer_range
             UpperCAmelCase_  :	str				      =		layer_norm_eps
             UpperCAmelCase_  :	Union[str, Any]				      =		encoder_stride
             UpperCAmelCase_  :	str				      =		["stem"] + [F"""stage{idx}""" for idx in range(1     ,len(self.depths) + 1)]
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Any				      =		get_aligned_output_features_output_indices(
                 out_features=lowercase     ,out_indices=lowercase     ,stage_names=self.stage_names)
