

'''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 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			'hf-internal-testing/tiny-random-t5'
      UpperCAmelCase	:     str						 =			AutoTokenizer.from_pretrained(lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			AutoModelForSeqaSeqLM.from_pretrained(lowercase_	)

      UpperCAmelCase	:     Dict						 =			tokenizer('This is me'					,      return_tensors='pt'	)

      UpperCAmelCase	:     Dict						 =			model.to_bettertransformer()

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

      UpperCAmelCase	:     List[Any]						 =			model.generate(**lowercase_	)

      UpperCAmelCase	:     Dict						 =			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	:     int						 =			AutoModelForSeqaSeqLM.from_pretrained(lowercase_	)

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

       UpperCAmelCase	:     List[str]						 =			model_reloaded.generate(**lowercase_	)
       self.assertTrue(torch.allclose(lowercase_					,      lowercase_	)	)




     def        UpperCAmelCase_	(		self			:					str	)						->     Optional[Any]:
      UpperCAmelCase	:     Tuple						 =			'hf-internal-testing/tiny-random-t5'
      UpperCAmelCase	:     List[str]						 =			AutoModelForSeqaSeqLM.from_pretrained(lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			model.to_bettertransformer()

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

       UpperCAmelCase	:     Dict						 =			model.reverse_bettertransformer()
       model.save_pretrained(lowercase_	)







'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__         =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMForMultipleChoice",
     "XLMForQuestionAnswering",
     "XLMForQuestionAnsweringSimple",
     "XLMForSequenceClassification",
     "XLMForTokenClassification",
     "XLMModel",
     "XLMPreTrainedModel",
     "XLMWithLMHeadModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMForMultipleChoice",
     "TFXLMForQuestionAnsweringSimple",
     "TFXLMForSequenceClassification",
     "TFXLMForTokenClassification",
     "TFXLMMainLayer",
     "TFXLMModel",
     "TFXLMPreTrainedModel",
     "TFXLMWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

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




