




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

						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''hf-internal-testing/tiny-random-t5'''
													__lowercase      =    AutoTokenizer.from_pretrained(_lowerCAmelCase				)
													__lowercase      =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase				)

													__lowercase      =    tokenizer('''This is me'''				,							return_tensors='''pt'''				)

													__lowercase      =    model.to_bettertransformer()

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

													__lowercase      =    model.generate(**_lowerCAmelCase				)

													__lowercase      =    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(_lowerCAmelCase				)

																				__lowercase      =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase				)

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

																				__lowercase      =    model_reloaded.generate(**_lowerCAmelCase				)
																				self.assertTrue(torch.allclose(_lowerCAmelCase				,							_lowerCAmelCase				)				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    '''hf-internal-testing/tiny-random-t5'''
													__lowercase      =    AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase				)

													__lowercase      =    model.to_bettertransformer()

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

																				__lowercase      =    model.reverse_bettertransformer()
																				model.save_pretrained(_lowerCAmelCase				)











from typing import TYPE_CHECKING

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


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

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






