code: from typing import List, Optional, Tuple, Union
  
  from . import logging
  from .import_utils import is_torch_available, is_torch_version
  
  
  if is_torch_available():
  	import torch
  
  lowercase__    :       Dict						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  try:
  	from torch._dynamo import allow_in_graph as maybe_allow_in_graph
  except (ImportError, ModuleNotFoundError):
  
  	def      SCREAMING_SNAKE_CASE_  (		cls							)  ->			List[Any]:
  								return cls
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = None     ,						snake_case__ = None     ,						snake_case__ = None     ,						snake_case__ = None     ,						)  ->			Any:
  							lowerCAmelCase				=	device
  							lowerCAmelCase				=	shape[0]
  
  							lowerCAmelCase				=	layout or torch.strided
  							lowerCAmelCase				=	device or torch.device('''cpu'''							)
  
  							if generator is not None:
  														lowerCAmelCase				=	generator.device.type if not isinstance(snake_case__     ,						snake_case__							) else generator[0].device.type
  														if gen_device_type != device.type and gen_device_type == "cpu":
  																					lowerCAmelCase				=	'''cpu'''
  																					if device != "mps":
  																												logger.info(
  																												    f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
  																												    f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
  																												    f" slighly speed up this function by passing a generator that was created on the {device} device."							)
  														elif gen_device_type != device.type and gen_device_type == "cuda":
  																					raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}."							)
  
  							if isinstance(snake_case__     ,						snake_case__							):
  														lowerCAmelCase				=	(1,) + shape[1:]
  														lowerCAmelCase				=	[
  														    torch.randn(snake_case__     ,						generator=generator[i]     ,						device=snake_case__     ,						dtype=snake_case__     ,						layout=snake_case__							)
  														    for i in range(snake_case__							)
  														]
  														lowerCAmelCase				=	torch.cat(snake_case__     ,						dim=0							).to(snake_case__							)
  							else:
  														lowerCAmelCase				=	torch.randn(snake_case__     ,						generator=snake_case__     ,						device=snake_case__     ,						dtype=snake_case__     ,						layout=snake_case__							).to(snake_case__							)
  
  							return latents
  
  
  
  
  
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			int:
  							if is_torch_version('''<'''     ,						'''2.0.0'''							) or not hasattr(snake_case__     ,						'''_dynamo'''							):
  														return False
  							return isinstance(snake_case__     ,						torch._dynamo.eval_frame.OptimizedModule							)
  
  
  
  
style_context: import glob
  import os
  
  from get_test_info import get_tester_classes
  
  
  if __name__ == "__main__":
  	lowercase__    :       List[Any]						=			[]
  
  	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  	lowercase__    :       Optional[int]						=			glob.glob(pattern)
  	# TODO: deal with TF/Flax too
  	lowercase__    :       Dict						=			[
  	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
  	]
  
  	for test_file in test_files:
  		lowercase__    :       Tuple						=			get_tester_classes(test_file)
  		for tester_class in tester_classes:
  			# A few tester classes don't have `parent` parameter in `__init__`.
  			# TODO: deal this better
  			try:
  				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
  			except Exception:
  				continue
  			if hasattr(tester, '''get_config'''):
  				lowercase__    :       Union[str, Any]						=			tester.get_config()
  				for k, v in config.to_dict().items():
  					if isinstance(v, int):
  						lowercase__    :       int						=			None
  						if k in ["vocab_size"]:
  							lowercase__    :       Optional[int]						=			1_0_0
  						elif k in ["max_position_embeddings"]:
  							lowercase__    :       Union[str, Any]						=			1_2_8
  						elif k in ["hidden_size", "d_model"]:
  							lowercase__    :       Optional[Any]						=			4_0
  						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
  							lowercase__    :       Dict						=			5
  						if target is not None and v > target:
  							failures.append(
  							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
  							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
  							    f' than {target}.'
  							)
  
  	if len(failures) > 0:
  		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))
  
  
  
  