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


__magic_name__    							=							logging.get_logger(__name__)

__magic_name__    							=							{
    '''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 					__SCREAMING_SNAKE_CASE       (       UpperCamelCase):






       """simple docstring"""

       __UpperCAmelCase       =					"canine"







       def __init__( self			,		_UpperCAmelCase=768			,		_UpperCAmelCase=12			,		_UpperCAmelCase=12			,		_UpperCAmelCase=3_072			,		_UpperCAmelCase="gelu"			,		_UpperCAmelCase=0.1			,		_UpperCAmelCase=0.1			,		_UpperCAmelCase=16_384			,		_UpperCAmelCase=16			,		_UpperCAmelCase=0.02			,		_UpperCAmelCase=1E-12			,		_UpperCAmelCase=0			,		_UpperCAmelCase=0xE000			,		_UpperCAmelCase=0xE001			,		_UpperCAmelCase=4			,		_UpperCAmelCase=4			,		_UpperCAmelCase=8			,		_UpperCAmelCase=16_384			,		_UpperCAmelCase=128			,		**_UpperCAmelCase			,		):
          super().__init__(pad_token_id=_lowerCAmelCase			,		bos_token_id=_lowerCAmelCase			,		eos_token_id=_lowerCAmelCase			,		**_lowerCAmelCase				)

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

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










'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE									=				{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MCTCTForCTC",
           "MCTCTModel",
           "MCTCTPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


