


from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_lowercase       		= [
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

for pkg in pkgs_to_check_at_runtime:
							if pkg in deps:
														if pkg == "tokenizers":
																					# must be loaded here, or else tqdm check may fail
																					from .utils import is_tokenizers_available

																					if not is_tokenizers_available():
																												continue  # not required, check version only if installed
														elif pkg == "accelerate":
																					# must be loaded here, or else tqdm check may fail
																					from .utils import is_accelerate_available

																					# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																					# Transformers with PyTorch
																					if not is_accelerate_available():
																												continue  # not required, check version only if installed

														require_version_core(deps[pkg])
							else:
														raise ValueError(f"can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py")

def   UpperCamelCase			(      snake_case__						,  snake_case__=None):
						require_version(deps[pkg]						,  snake_case__)




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  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






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

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

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= 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			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

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

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







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)