





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


a		:					Tuple	      =     logging.get_logger(__name__)

a		:					List[str]	      =     {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}


class 			__UpperCamelCase							(		lowerCAmelCase__		):
							lowerCamelCase  :   Tuple    ="""data2vec-text"""






							def __init__(  self ,   lowerCAmelCase__=3_0522 ,   lowerCAmelCase__=768 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=12 ,   lowerCAmelCase__=3072 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=1E-12 ,   lowerCAmelCase__=1 ,   lowerCAmelCase__=0 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__="absolute" ,   lowerCAmelCase__=True ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ ,   )      ->					Any:
								super().__init__(pad_token_id=_lowerCamelCase ,   bos_token_id=_lowerCamelCase ,   eos_token_id=_lowerCamelCase ,   **_lowerCamelCase )

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


class 			__UpperCamelCase							(		lowerCAmelCase__		):






							@property
							def        __a  (  self )      ->					str:

								if self.task == "multiple-choice":
									a :						Optional[int]      =							{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
									a :						List[str]      =							{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ] )





"""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__)

