



"""simple docstring"""
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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_ :str						=			"""hf-internal-testing/tiny-random-t5"""
											snake_case_ :str						=			AutoTokenizer.from_pretrained(snake_case						)
											snake_case_ :Tuple						=			AutoModelForSeqaSeqLM.from_pretrained(snake_case						)

											snake_case_ :Optional[int]						=			tokenizer("""This is me"""						,     return_tensors="""pt"""						)

											snake_case_ :int						=			model.to_bettertransformer()

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

											snake_case_ :Optional[int]						=			model.generate(**snake_case						)

											snake_case_ :List[str]						=			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(snake_case						)

																	snake_case_ :Union[str, Any]						=			AutoModelForSeqaSeqLM.from_pretrained(snake_case						)

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

																	snake_case_ :int						=			model_reloaded.generate(**snake_case						)
																	self.assertTrue(torch.allclose(snake_case						,     snake_case						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Union[str, Any]:
											snake_case_ :List[Any]						=			"""hf-internal-testing/tiny-random-t5"""
											snake_case_ :Optional[int]						=			AutoModelForSeqaSeqLM.from_pretrained(snake_case						)

											snake_case_ :Dict						=			model.to_bettertransformer()

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

																	snake_case_ :Union[str, Any]						=			model.reverse_bettertransformer()
																	model.save_pretrained(snake_case						)





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure)
