



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase     :							int  							=				{
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase     :							int  							=				[
	    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''ResNetForImageClassification''',
	    '''ResNetModel''',
	    '''ResNetPreTrainedModel''',
	    '''ResNetBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase     :							List[str]  							=				[
	    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFResNetForImageClassification''',
	    '''TFResNetModel''',
	    '''TFResNetPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase     :							List[str]  							=				[
	    '''FlaxResNetForImageClassification''',
	    '''FlaxResNetModel''',
	    '''FlaxResNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_resnet import (
		    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ResNetBackbone,
		    ResNetForImageClassification,
		    ResNetModel,
		    ResNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_resnet import (
		    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFResNetForImageClassification,
		    TFResNetModel,
		    TFResNetPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
	import sys

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








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch





@require_torch
@require_optimum
@slow
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

														self.assertFalse(
														    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(UpperCamelCase__      ):
																				model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



