"""simple docstring"""



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__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[str]           =		BioGptTokenizer
 _snake_case   :			int           =		False


 def 		a__ (				self		):
        super().setUp()

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

        __a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"]		)
        __a     				=  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  ,						lowerCamelCase		):
        __a     				=  "lower newer"
        __a     				=  "lower newer"
        return input_text, output_text


 def 		a__ (				self		):
        __a     				=  BioGptTokenizer(self.vocab_file  ,						self.merges_file		)

        __a     				=  "lower"
        __a     				=  ["low", "er</w>"]
        __a     				=  tokenizer.tokenize(lowerCamelCase		)
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)

        __a     				=  tokens + ["<unk>"]
        __a     				=  [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)  ,						lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        __a     				=  BioGptTokenizer.from_pretrained("microsoft/biogpt"		)

        __a     				=  tokenizer.encode("sequence builders"  ,						add_special_tokens=lowerCamelCase		)
        __a     				=  tokenizer.encode("multi-sequence build"  ,						add_special_tokens=lowerCamelCase		)

        __a     				=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
        __a     				=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase  ,						lowerCamelCase		)

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

"""simple docstring"""



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


SCREAMING_SNAKE_CASE__:List[str]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class 				snake_case__     (   snake_case_,		snake_case_   ):
 _snake_case   :			List[Any]           =		"""focalnet"""


 def __init__(				self  ,						lowerCamelCase=224  ,						lowerCamelCase=4  ,						lowerCamelCase=3  ,						lowerCamelCase=96  ,						lowerCamelCase=False  ,						lowerCamelCase=[192, 384, 768, 768]  ,						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  ,						):
        super().__init__(**lowerCamelCase		)

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