






'''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       		=      {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase       		=      [
		    '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       		=      _LazyModule(__name__, globals()['__file__'], _import_structure)












'''simple docstring'''







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

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




