




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     List[Any]          =		['''ChineseCLIPFeatureExtractor''']
   UpperCamelCase_		:     int          =		['''ChineseCLIPImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     List[str]          =		[
       '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ChineseCLIPModel''',
       '''ChineseCLIPPreTrainedModel''',
       '''ChineseCLIPTextModel''',
       '''ChineseCLIPVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_		:     Optional[int]          =		logging.get_logger(__name__)

UpperCamelCase_		:     str          =		{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		"""ibert"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=30_522   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-12   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE="absolute"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE="none"   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= hidden_act
          _snake_case				= intermediate_size
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= max_position_embeddings
          _snake_case				= type_vocab_size
          _snake_case				= initializer_range
          _snake_case				= layer_norm_eps
          _snake_case				= position_embedding_type
          _snake_case				= quant_mode
          _snake_case				= force_dequant







class   _a							(		__lowerCAmelCase				):






     @property
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case				= {0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case				= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]       )


