



import copy

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


__UpperCAmelCase			  =			logging.get_logger(__name__)



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   int  	=    "upernet"
       def __init__(     self,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=[1, 2, 3, 6],     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=0.4,     SCREAMING_SNAKE_CASE_=384,     SCREAMING_SNAKE_CASE_=256,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=255,     **SCREAMING_SNAKE_CASE_,     )	->						int:
          super().__init__(**SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    int		      =      backbone_config
          UpperCamelCase       :    Optional[int]		      =      hidden_size
          UpperCamelCase       :    str		      =      initializer_range
          UpperCamelCase       :    Union[str, Any]		      =      pool_scales
          UpperCamelCase       :    Dict		      =      use_auxiliary_head
          UpperCamelCase       :    Any		      =      auxiliary_loss_weight
          UpperCamelCase       :    Dict		      =      auxiliary_in_channels
          UpperCamelCase       :    Tuple		      =      auxiliary_channels
          UpperCamelCase       :    int		      =      auxiliary_num_convs
          UpperCamelCase       :    Union[str, Any]		      =      auxiliary_concat_input
          UpperCamelCase       :    Dict		      =      loss_ignore_index






       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    Tuple		      =      copy.deepcopy(self.__dict__			)
          UpperCamelCase       :    Optional[Any]		      =      self.backbone_config.to_dict()
          UpperCamelCase       :    Dict		      =      self.__class__.model_type
          return output





"""simple docstring"""


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

							A     = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
							A     = dict(zip(A_	,range(len(A_   )   )   )   )

							A     = ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
							A     = {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

							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'	,encoding='utf-8'   ) as fp:
								fp.write(json.dumps(A_   ) + '\n'   )
							with open(self.merges_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A     = [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = tok.batch_decode(A_	,skip_special_tokens=A_	,clean_up_tokenization_spaces=A_   )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

							assert encoded[-1] == encoded_dot[0]