code: import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
  	import torch
  
  
  lowercase__    :       Any						=			logging.get_logger(__name__)
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=21	,    __SCREAMING_SNAKE_CASE=[]	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[int]:
  								lowerCAmelCase				=	model
  								lowerCAmelCase				=	trace_batch_nums
  								lowerCAmelCase				=	abort_after_batch_num
  
  								# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
  								lowerCAmelCase				=	collections.deque([]	,    __SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	[]
  								lowerCAmelCase				=	0
  								lowerCAmelCase				=	0
  								lowerCAmelCase				=	False
  								lowerCAmelCase				=	'''                 '''
  
  								self.analyse_model()
  
  								self.register_forward_hook()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE=None       )		->Union[str, Any]:
  
  								if frame is not None:
  															self.expand_frame(__SCREAMING_SNAKE_CASE       )
  								self.frames.append('''\n'''.join(self.frame       )       )
  								lowerCAmelCase				=	[]  # start a new frame
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Tuple:
  								self.frame.append(__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								print('''\n'''.join(self.frames       )       )
  								lowerCAmelCase				=	[]
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								lowerCAmelCase				=	[]
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								print(F"\nDetected inf/nan during batch_number={self.batch_number}"       )
  								print(F"Last {len(self.frames       )} forward frames:"       )
  								print(F"{'abs min':8} {'abs max':8} metadata"       )
  								print('''\n'''.join(self.frames       )       )
  								print('''\n\n'''       )
  								lowerCAmelCase				=	[]
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								lowerCAmelCase				=	{m: name for name, m in self.model.named_modules()}
  								# self.longest_module_name = max(len(v) for v in self.module_names.values())
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Optional[Any]:
  
  								if torch.is_tensor(__SCREAMING_SNAKE_CASE       ):
  															self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )
  															if detect_overflow(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  																						lowerCAmelCase				=	True
  								elif var is None:
  															self.expand_frame(F"{'None':>17} {ctx}"       )
  								else:
  															self.expand_frame(F"{'not a tensor':>17} {ctx}"       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"       )
  								self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Tuple:
  								self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"       )
  
  								# params
  								for name, p in module.named_parameters(recurse=__SCREAMING_SNAKE_CASE       ):
  															self.analyse_variable(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  								# inputs
  								if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  															for i, x in enumerate(__SCREAMING_SNAKE_CASE       ):
  																						self.analyse_variable(__SCREAMING_SNAKE_CASE	,    F"input[{i}]"       )
  								else:
  															self.analyse_variable(__SCREAMING_SNAKE_CASE	,    '''input'''       )
  
  								# outputs
  								if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  															for i, x in enumerate(__SCREAMING_SNAKE_CASE       ):
  																						# possibly a tuple of tuples
  																						if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  																													for j, y in enumerate(__SCREAMING_SNAKE_CASE       ):
  																																				self.analyse_variable(__SCREAMING_SNAKE_CASE	,    F"output[{i}][{j}]"       )
  																						else:
  																													self.analyse_variable(__SCREAMING_SNAKE_CASE	,    F"output[{i}]"       )
  								else:
  															self.analyse_variable(__SCREAMING_SNAKE_CASE	,    '''output'''       )
  
  								self.save_frame()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								self.model.apply(self._register_forward_hook       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
  								module.register_forward_hook(self.forward_hook       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Optional[int]:
  								lowerCAmelCase				=	False
  
  								lowerCAmelCase				=	True if self.batch_number in self.trace_batch_nums else False
  								if trace_mode:
  															self.reset_saved_frames()
  
  								if self.total_calls == 0:
  															self.batch_start_frame()
  								self.total_calls += 1
  
  								# count batch numbers - the very first forward hook of the batch will be called when the
  								# batch completes - i.e. it gets called very last - we know this batch has finished
  								if module == self.model:
  															self.batch_number += 1
  															lowerCAmelCase				=	True
  
  								self.create_frame(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  								# if last_frame_of_batch:
  								#     self.batch_end_frame()
  
  								if trace_mode:
  															self.trace_frames()
  
  								if last_frame_of_batch:
  															self.batch_start_frame()
  
  								if self.detected_overflow and not trace_mode:
  															self.dump_saved_frames()
  
  															# now we can abort, as it's pointless to continue running
  															raise ValueError(
  															    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
  															    '''Please scroll up above this traceback to see the activation values prior to this event.'''       )
  
  								# abort after certain batch if requested to do so
  								if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
  															raise ValueError(
  															    F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
  															    F" `abort_after_batch_num={self.abort_after_batch_num}` arg"       )
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	var.abs()
  							return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
  							lowerCAmelCase				=	False
  							if torch.isnan(snake_case__							).any().item():
  														lowerCAmelCase				=	True
  														print(f"{ctx} has nans"							)
  							if torch.isinf(snake_case__							).any().item():
  														lowerCAmelCase				=	True
  														print(f"{ctx} has infs"							)
  
  							# if needed to monitor large elements can enable the following
  							if 0:  # and detected:
  														lowerCAmelCase				=	var[torch.ge(var.abs()     ,						1_0_0							)]
  														if naaa.numel() > 0:
  																					print(f"{ctx}:  n100={naaa.numel()}"							)
  														lowerCAmelCase				=	var[torch.ge(var.abs()     ,						1_0_0_0							)]
  														if naaaa.numel() > 0:
  																					print(f"{ctx}: n1000={naaaa.numel()}"							)
  														lowerCAmelCase				=	var[torch.ge(var.abs()     ,						1_0_0_0_0							)]
  														if naaaaa.numel() > 0:
  																					print(f"{ctx}: n10000={naaaaa.numel()}"							)
  
  							if 0:
  														print(f"min={var.min():9.2e} max={var.max():9.2e}"							)
  
  							if 0:
  														print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"							)
  
  							return detected
  
  
  
  
  
  
  class      lowercase_						(						lowerCAmelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      str     =       """underflow_overflow"""
  	UpperCAmelCase_						:      List[Any]     =       """tpu_metrics_debug"""
  
  
  
  
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))
  
  
  
  