






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 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __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 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __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						)








from typing import TYPE_CHECKING

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


__lowerCAmelCase					   =       {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BloomTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BloomForCausalLM',
							    'BloomModel',
							    'BloomPreTrainedModel',
							    'BloomForSequenceClassification',
							    'BloomForTokenClassification',
							    'BloomForQuestionAnswering',
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)