





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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     "hf-internal-testing/tiny-random-t5"
														UpperCamelCase__			       =     AutoTokenizer.from_pretrained(lowercase_					)
														UpperCamelCase__			       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_					)

														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(**lowercase_					)

														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(lowercase_					)

																					UpperCamelCase__			       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_					)

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

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







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     "hf-internal-testing/tiny-random-t5"
														UpperCamelCase__			       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_					)

														UpperCamelCase__			       =     model.to_bettertransformer()

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

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











from typing import TYPE_CHECKING

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


UpperCamelCase__     =	{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SwiftFormerForImageClassification",
					    "SwiftFormerModel",
					    "SwiftFormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

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




