


__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWDForCTC''',
		    '''SEWDForSequenceClassification''',
		    '''SEWDModel''',
		    '''SEWDPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew_d import (
				    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWDForCTC,
				    SEWDForSequenceClassification,
				    SEWDModel,
				    SEWDPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Tuple           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

