
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     "canine"



   def __init__(self		:					Dict    ,						snake_case__		:					Dict=7_68    ,						snake_case__		:					Tuple=12    ,						snake_case__		:					Optional[int]=12    ,						snake_case__		:					Optional[Any]=30_72    ,						snake_case__		:					List[Any]="gelu"    ,						snake_case__		:					Dict=0.1    ,						snake_case__		:					Optional[Any]=0.1    ,						snake_case__		:					List[str]=1_63_84    ,						snake_case__		:					List[Any]=16    ,						snake_case__		:					List[Any]=0.02    ,						snake_case__		:					Tuple=1e-12    ,						snake_case__		:					Tuple=0    ,						snake_case__		:					Optional[int]=0XE_0_0_0    ,						snake_case__		:					Dict=0XE_0_0_1    ,						snake_case__		:					int=4    ,						snake_case__		:					Union[str, Any]=4    ,						snake_case__		:					Union[str, Any]=8    ,						snake_case__		:					List[str]=1_63_84    ,						snake_case__		:					List[str]=1_28    ,						**snake_case__		:					Optional[Any]    ,						)		->	List[str]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						bos_token_id=snake_case__    ,						eos_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Any							=      max_position_embeddings
          snake_case       :   List[Any]							=      hidden_size
          snake_case       :   Optional[int]							=      num_hidden_layers
          snake_case       :   Tuple							=      num_attention_heads
          snake_case       :   Dict							=      intermediate_size
          snake_case       :   List[str]							=      hidden_act
          snake_case       :   Tuple							=      hidden_dropout_prob
          snake_case       :   List[str]							=      attention_probs_dropout_prob
          snake_case       :   str							=      initializer_range
          snake_case       :   int							=      type_vocab_size
          snake_case       :   List[Any]							=      layer_norm_eps

          # Character config:
          snake_case       :   str							=      downsampling_rate
          snake_case       :   Dict							=      upsampling_kernel_size
          snake_case       :   List[str]							=      num_hash_functions
          snake_case       :   Optional[int]							=      num_hash_buckets
          snake_case       :   Dict							=      local_transformer_stride










"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

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


