



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 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''hf-internal-testing/tiny-random-t5'''
         _lowerCAmelCase 			=  AutoTokenizer.from_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

         _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(**_lowerCAmelCase			)

         _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(_lowerCAmelCase			)

               _lowerCAmelCase 			=  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

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

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''hf-internal-testing/tiny-random-t5'''
         _lowerCAmelCase 			=  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

         _lowerCAmelCase 			=  model.to_bettertransformer()

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

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




# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

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