




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):
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						"""hf-internal-testing/tiny-random-t5"""
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(UpperCamelCase_   )
             __lowerCamelCase   =						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_   )

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

             __lowerCamelCase   =						model.to_bettertransformer()

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

             __lowerCamelCase   =						model.generate(**UpperCamelCase_   )

             __lowerCamelCase   =						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_   )

                    __lowerCamelCase   =						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_   )

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

                    __lowerCamelCase   =						model_reloaded.generate(**UpperCamelCase_   )
                    self.assertTrue(torch.allclose(UpperCamelCase_    ,       UpperCamelCase_   )   )




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						"""hf-internal-testing/tiny-random-t5"""
             __lowerCamelCase   =						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						model.to_bettertransformer()

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

                    __lowerCamelCase   =						model.reverse_bettertransformer()
                    model.save_pretrained(UpperCamelCase_   )






"""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)
