
"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''upernet'''






       def __init__(  self :							int       ,      UpperCAmelCase_ :							Optional[Any]=None       ,      UpperCAmelCase_ :							Any=512       ,      UpperCAmelCase_ :							Union[str, Any]=0.02       ,      UpperCAmelCase_ :							List[Any]=[1, 2, 3, 6]       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							Union[str, Any]=0.4       ,      UpperCAmelCase_ :							Union[str, Any]=384       ,      UpperCAmelCase_ :							Optional[Any]=256       ,      UpperCAmelCase_ :							Optional[int]=1       ,      UpperCAmelCase_ :							str=False       ,      UpperCAmelCase_ :							Tuple=255       ,      **UpperCAmelCase_ :							str       ,      )->  Tuple:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)

              if backbone_config is None:
                     logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                     UpperCamelCase					 =		CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]			)
              elif isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		backbone_config.get("model_type"			)
                     UpperCamelCase					 =		CONFIG_MAPPING[backbone_model_type]
                     UpperCamelCase					 =		config_class.from_dict(UpperCAmelCase_			)

              UpperCamelCase					 =		backbone_config
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		pool_scales
              UpperCamelCase					 =		use_auxiliary_head
              UpperCamelCase					 =		auxiliary_loss_weight
              UpperCamelCase					 =		auxiliary_in_channels
              UpperCamelCase					 =		auxiliary_channels
              UpperCamelCase					 =		auxiliary_num_convs
              UpperCamelCase					 =		auxiliary_concat_input
              UpperCamelCase					 =		loss_ignore_index






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		copy.deepcopy(self.__dict__			)
              UpperCamelCase					 =		self.backbone_config.to_dict()
              UpperCamelCase					 =		self.__class__.model_type
              return output







"""simple docstring"""


import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			FunnelTokenizer
       UpperCamelCase_		:					Any   =			FunnelTokenizerFast
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Tuple   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		[
                  "<unk>",
                  "<cls>",
                  "<sep>",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              UpperCamelCase					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      **UpperCAmelCase_ :							Optional[Any]			)->  str:







              """simple docstring"""
              return FunnelTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      **UpperCAmelCase_ :							Any			)->  Union[str, Any]:







              """simple docstring"""
              return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"UNwant\u00E9d,running"
              UpperCamelCase					 =		"unwanted, running"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file			)

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizers(do_lower_case=UpperCAmelCase_			)
              for tokenizer in tokenizers:
                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"			)
                     UpperCamelCase					 =		len(inputs["input_ids"]			) - 1
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len			)

                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"       ,      "UNwant\u00E9d,running"			)
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len + [1] * sentence_len			)





