



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 						snake_case  (		SCREAMING_SNAKE_CASE_    ,unittest.TestCase     ):



   '''simple docstring'''






   snake_case_    :       List[Any]								=						FunnelTokenizer
   snake_case_    :       Tuple								=						FunnelTokenizerFast
   snake_case_    :       List[str]								=						True
   snake_case_    :       Optional[Any]								=						True

   def    UpperCamelCase_     (    self							:	Any) -> Dict:

    """simple docstring"""







    super().setUp()

    _snake_case			:     Union[str, Any] 		=    [
        """<unk>""",
        """<cls>""",
        """<sep>""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    _snake_case			:     Any 		=    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    UpperCamelCase_     (    self							:	List[str] ,						**lowerCAmelCase							:	Tuple) -> int:

    """simple docstring"""







    return FunnelTokenizer.from_pretrained(self.tmpdirname ,						**__SCREAMING_SNAKE_CASE)

   def    UpperCamelCase_     (    self							:	Union[str, Any] ,						**lowerCAmelCase							:	Union[str, Any]) -> Optional[int]:

    """simple docstring"""







    return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,						**__SCREAMING_SNAKE_CASE)

   def    UpperCamelCase_     (    self							:	Optional[Any] ,						lowerCAmelCase							:	Optional[Any]) -> Optional[int]:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    """UNwant\u00E9d,running"""
    _snake_case			:     int 		=    """unwanted, running"""
    return input_text, output_text

   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Union[str, Any]:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.tokenizer_class(self.vocab_file)

    _snake_case			:     Union[str, Any] 		=    tokenizer.tokenize("""UNwant\u00E9d,running""")
    self.assertListEqual(__SCREAMING_SNAKE_CASE ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""])
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE) ,						[7, 4, 5, 10, 8, 9])


   def    UpperCamelCase_     (    self							:	str) -> Tuple:

    """simple docstring"""







    _snake_case			:     Tuple 		=    self.get_tokenizers(do_lower_case=__SCREAMING_SNAKE_CASE)
    for tokenizer in tokenizers:
     _snake_case			:     List[str] 		=    tokenizer("""UNwant\u00E9d,running""")
     _snake_case			:     Optional[int] 		=    len(inputs["""input_ids"""]) - 1
     self.assertListEqual(inputs["""token_type_ids"""] ,						[2] + [0] * sentence_len)

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



'''simple docstring'''



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


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







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

 lowerCAmelCase__  					=      {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple=224					,							__SCREAMING_SNAKE_CASE					:       str=4					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=96					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[2, 2, 6, 2]					,							__SCREAMING_SNAKE_CASE					:       Any=[3, 6, 12, 24]					,							__SCREAMING_SNAKE_CASE					:       Dict=7					,							__SCREAMING_SNAKE_CASE					:       Dict=4.0					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.1					,							__SCREAMING_SNAKE_CASE					:       List[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       str=False					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      len(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      __SCREAMING_SNAKE_CASE     =      int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






