"""simple docstring"""


import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	List[str]  				=				OpenAIGPTTokenizer
    _snake_case  :	Union[str, Any]  				=				OpenAIGPTTokenizerFast
    _snake_case  :	List[Any]  				=				True
    _snake_case  :	str  				=				False


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _UpperCAmelCase    			=   [
          """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    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )
      _UpperCAmelCase    			=   ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

      _UpperCAmelCase    			=   os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]       )
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Dict       ):
      return "lower newer", "lower newer"


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   OpenAIGPTTokenizer(self.vocab_file ,					self.merges_file       )

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

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


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Tuple=15       ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
          _UpperCAmelCase    			=   self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )

          # Simple input
          _UpperCAmelCase    			=   """This is a simple input"""
          _UpperCAmelCase    			=   ["""This is a simple input 1""", """This is a simple input 2"""]
          _UpperCAmelCase    			=   ("""This is a simple input""", """This is a pair""")
          _UpperCAmelCase    			=   [
              ("""This is a simple input 1""", """This is a simple input 2"""),
              ("""This is a simple pair 1""", """This is a simple pair 2"""),
          ]

          # Simple input tests
          self.assertRaises(__lowerCAmelCase ,					tokenizer_r.encode ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length"""       )

          # Simple input
          self.assertRaises(__lowerCAmelCase ,					tokenizer_r.encode_plus ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length"""       )

          # Simple input
          self.assertRaises(
              __lowerCAmelCase ,					tokenizer_r.batch_encode_plus ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length""" ,					)

          # Pair input
          self.assertRaises(__lowerCAmelCase ,					tokenizer_r.encode ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length"""       )

          # Pair input
          self.assertRaises(__lowerCAmelCase ,					tokenizer_r.encode_plus ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length"""       )

          # Pair input
          self.assertRaises(
              __lowerCAmelCase ,					tokenizer_r.batch_encode_plus ,					__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					padding="""max_length""" ,					)


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      pass



@require_ftfy
@require_spacy
@require_tokenizers
class 							a					(		lowerCAmelCase_ ):
    pass

"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'dpt'


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	str=12 ,					__lowerCAmelCase						:	Optional[int]=12 ,					__lowerCAmelCase						:	Dict=3072 ,					__lowerCAmelCase						:	List[Any]="gelu" ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	str=0.0 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[Any]=1e-1_2 ,					__lowerCAmelCase						:	List[str]=384 ,					__lowerCAmelCase						:	Tuple=16 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[Any]=True ,					__lowerCAmelCase						:	Optional[Any]=[2, 5, 8, 11] ,					__lowerCAmelCase						:	Any="project" ,					__lowerCAmelCase						:	Dict=[4, 2, 1, 0.5] ,					__lowerCAmelCase						:	int=[96, 192, 384, 768] ,					__lowerCAmelCase						:	Dict=256 ,					__lowerCAmelCase						:	Optional[Any]=-1 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	List[Any]=0.4 ,					__lowerCAmelCase						:	List[str]=255 ,					__lowerCAmelCase						:	List[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=[1, 1024, 24, 24] ,					__lowerCAmelCase						:	List[Any]=[0, 1] ,					__lowerCAmelCase						:	Dict=None ,					**__lowerCAmelCase						:	str ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   is_hybrid

      if self.is_hybrid:
        if backbone_config is None:
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   {
              """global_padding""": """same""",
              """layer_type""": """bottleneck""",
              """depths""": [3, 4, 9],
              """out_features""": ["""stage1""", """stage2""", """stage3"""],
              """embedding_dynamic_padding""": True,
          }
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          _UpperCAmelCase    			=   backbone_config
        else:
          raise ValueError(
              f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

        _UpperCAmelCase    			=   backbone_featmap_shape
        _UpperCAmelCase    			=   neck_ignore_stages

        if readout_type != "project":
          raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""       )
      else:
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   []

      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   layer_norm_eps
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   qkv_bias
      _UpperCAmelCase    			=   backbone_out_indices
      if readout_type not in ["ignore", "add", "project"]:
        raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""       )
      _UpperCAmelCase    			=   readout_type
      _UpperCAmelCase    			=   reassemble_factors
      _UpperCAmelCase    			=   neck_hidden_sizes
      _UpperCAmelCase    			=   fusion_hidden_size
      _UpperCAmelCase    			=   head_in_index
      _UpperCAmelCase    			=   use_batch_norm_in_fusion_residual
      # auxiliary head attributes (semantic segmentation)
      _UpperCAmelCase    			=   use_auxiliary_head
      _UpperCAmelCase    			=   auxiliary_loss_weight
      _UpperCAmelCase    			=   semantic_loss_ignore_index
      _UpperCAmelCase    			=   semantic_classifier_dropout


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   copy.deepcopy(self.__dict__       )

      if output["backbone_config"] is not None:
        _UpperCAmelCase    			=   self.backbone_config.to_dict()

      _UpperCAmelCase    			=   self.__class__.model_type
      return output
