

"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=  logging.get_logger(__name__)

_A							=  {
    """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       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  'canine'



  def __init__(self							,       _lowerCamelCase=768							,       _lowerCamelCase=12							,       _lowerCamelCase=12							,       _lowerCamelCase=3072							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=0.1							,       _lowerCamelCase=0.1							,       _lowerCamelCase=16384							,       _lowerCamelCase=16							,       _lowerCamelCase=0.02							,       _lowerCamelCase=1e-12							,       _lowerCamelCase=0							,       _lowerCamelCase=0xE000							,       _lowerCamelCase=0xE001							,       _lowerCamelCase=4							,       _lowerCamelCase=4							,       _lowerCamelCase=8							,       _lowerCamelCase=16384							,       _lowerCamelCase=128							,       **_lowerCamelCase							,       ):


        """simple docstring"""

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

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

        # Character config:
        UpperCAmelCase__			:							Tuple							=    downsampling_rate
        UpperCAmelCase__			:							Any							=    upsampling_kernel_size
        UpperCAmelCase__			:							str							=    num_hash_functions
        UpperCAmelCase__			:							List[str]							=    num_hash_buckets
        UpperCAmelCase__			:							List[Any]							=    local_transformer_stride




"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A							=  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CpmAntForCausalLM""",
        """CpmAntModel""",
        """CpmAntPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

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

