

"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  def  _a      (self							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       **_lowerCamelCase	):


        """simple docstring"""

        if tokenize_kwargs is None:
              UpperCAmelCase__			:							List[str]							=    {}

        if truncation is not None:
              if "truncation" in tokenize_kwargs:
                    raise ValueError(
                        """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""	)
              UpperCAmelCase__			:							List[Any]							=    truncation

        UpperCAmelCase__			:							Optional[int]							=    tokenize_kwargs

        UpperCAmelCase__			:							Dict							=    {}
        if return_tensors is not None:
              UpperCAmelCase__			:							str							=    return_tensors

        return preprocess_params, {}, postprocess_params



  def  _a      (self							,       _lowerCamelCase							,       **_lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    self.framework
        UpperCAmelCase__			:							List[str]							=    self.tokenizer(_lowerCamelCase							,       return_tensors=_lowerCamelCase							,       **_lowerCamelCase	)
        return model_inputs



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    self.model(**_lowerCamelCase	)
        return model_outputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=False	):


        """simple docstring"""

        if return_tensors:
              return model_outputs[0]
        if self.framework == "pt":
              return model_outputs[0].tolist()
        elif self.framework == "tf":
              return model_outputs[0].numpy().tolist()




  def __call__(self							,       *_lowerCamelCase							,       **_lowerCamelCase	):


        """simple docstring"""

        return super().__call__(*_lowerCamelCase							,       **_lowerCamelCase	)




"""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__)

