





"""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 		__snake_case (		SCREAMING_SNAKE_CASE__					):





	"""simple docstring"""


	_lowerCamelCase				=					field(default="""image-classification"""							,							metadata={"""include_in_asdict_even_if_is_default""": True}					)
	_lowerCamelCase				=					Features({"""image""": Image()}					)
	_lowerCamelCase				=					Features({"""labels""": ClassLabel}					)
	_lowerCamelCase				=					"""image"""
	_lowerCamelCase				=					"""labels"""

	def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



					'''simple docstring'''




					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] ,						__lowerCamelCase  ):
									raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
					__A   :						Optional[Any]									=					copy.deepcopy(self  )
					__A   :						Union[str, Any]									=					self.label_schema.copy()
					__A   :						List[str]									=					features[self.label_column]
					__A   :						Tuple									=					label_schema
					return task_template







	@property
	def  UpperCamelCase__(   self  ):



					'''simple docstring'''




					return {
					    self.image_column: "image",
					    self.label_column: "labels",
					}











"""simple docstring"""



from typing import TYPE_CHECKING

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


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

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

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




