code: 
  
  
  '''simple docstring'''
  
  
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
        import torch
  
  
  A__	:      Dict				=						logging.get_logger(__name__)
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=21    ,  SCREAMING_SNAKE_CASE_=[]    ,  SCREAMING_SNAKE_CASE_=None			)       ->  List[Any]:
           __lowerCamelCase     :			Union[str, Any]              =	model
           __lowerCamelCase     :			Tuple              =	trace_batch_nums
           __lowerCamelCase     :			Dict              =	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     :			Any              =	collections.deque([]    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	[]
           __lowerCamelCase     :			Union[str, Any]              =	0
           __lowerCamelCase     :			Union[str, Any]              =	0
           __lowerCamelCase     :			Tuple              =	False
           __lowerCamelCase     :			Any              =	'                 '
  
           self.analyse_model()
  
           self.register_forward_hook()
  
  
  
  
  
  
  
      def    lowercase_       (							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     :			Union[str, Any]              =	[]  # start a new frame
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
           self.frame.append(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           print('\n'.join(self.frames			)			)
           __lowerCamelCase     :			Any              =	[]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			str              =	[]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           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     :			List[str]              =	[]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           # extract the fully qualified module names, to be able to report at run time. e.g.:
           # encoder.block.2.layer.0.SelfAttention.o
           #
           # for shared weights only the first shared module name will be registered
           __lowerCamelCase     :			Any              =	{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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
           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     :			List[str]              =	True
           elif var is None:
                self.expand_frame(f'{"None":>17} {ctx}'			)
           else:
                self.expand_frame(f'{"not a tensor":>17} {ctx}'			)
  
  
  
  
  
  
  
      def    lowercase_       (							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    lowercase_       (							self			)       ->  Dict:
           self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Any:
           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    lowercase_       (							self			)       ->  Tuple:
           self.model.apply(self._register_forward_hook			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
           module.register_forward_hook(self.forward_hook			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Any:
           # - input is a tuple of packed inputs (could be non-Tensors)
           # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
           __lowerCamelCase     :			str              =	False
  
           __lowerCamelCase     :			List[Any]              =	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     :			List[Any]              =	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     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Optional[Any]							)  ->	List[Any]:
       __lowerCamelCase     :			Dict              =	var.abs()
       return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	Optional[Any]:
       __lowerCamelCase     :			List[Any]              =	False
       if torch.isnan(UpperCAmelCase_							).any().item():
            __lowerCamelCase     :			Union[str, Any]              =	True
            print(F'{ctx} has nans'							)
       if torch.isinf(UpperCAmelCase_							).any().item():
            __lowerCamelCase     :			Tuple              =	True
            print(F'{ctx} has infs'							)
  
       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
            __lowerCamelCase     :			Optional[int]              =	var[torch.ge(var.abs()   ,					1_00							)]
            if naaa.numel() > 0:
                 print(F'{ctx}:  n100={naaa.numel()}'							)
            __lowerCamelCase     :			Optional[int]              =	var[torch.ge(var.abs()   ,					10_00							)]
            if naaaa.numel() > 0:
                 print(F'{ctx}: n1000={naaaa.numel()}'							)
            __lowerCamelCase     :			Optional[int]              =	var[torch.ge(var.abs()   ,					1_00_00							)]
            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      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[int]							  =    'underflow_overflow'
      lowerCamelCase						:      Optional[Any]							  =    'tpu_metrics_debug'
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  