





'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase				:      Optional[int]						      ={
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      List[str]						      =["BloomTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      Tuple						      =[
      "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BloomForCausalLM",
      "BloomModel",
      "BloomPreTrainedModel",
      "BloomForSequenceClassification",
      "BloomForTokenClassification",
      "BloomForQuestionAnswering",
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  __lowerCAmelCase				:      Union[str, Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''

# Copyright 2023 The HuggingFace 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


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



