






'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate




@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class      A     (   __SCREAMING_SNAKE_CASE    ):






 """simple docstring"""





 __a      :    str			  =    field(default='''question-answering-extractive''', metadata={'''include_in_asdict_even_if_is_default''': True}    )
 __a      :    ClassVar[Features]			  =    Features({'''question''': Value('''string'''    ), '''context''': Value('''string'''    )}    )
 __a      :    ClassVar[Features]			  =    Features(
     {
         '''answers''': Sequence(
             {
                 '''text''': Value('''string'''    ),
                 '''answer_start''': Value('''int32'''    ),
             }    )
     }    )
 __a      :    str			  =    "question"
 __a      :    str			  =    "context"
 __a      :    str			  =    "answers"
 @property
 def 	_UpperCAmelCase							(				self			):
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}













"""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


__magic_name__       :					Tuple						=			{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """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

		__magic_name__       :					Any						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



