


'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


__lowercase:		Union[str, Any]    						=  logging.get_logger(__name__)







class 			UpperCAmelCase			:

 def __init__(			self			:						str,       a_			:						str,       a_			:						Optional[Any]=21,       a_			:						Optional[Any]=[],       a_			:						List[str]=None       ):





    """simple docstring"""



    UpperCamelCase__			    =    model
    UpperCamelCase__			    =    trace_batch_nums
    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    collections.deque([],       a_       )
    UpperCamelCase__			    =    []
    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    False
    UpperCamelCase__			    =    "                 "

    self.analyse_model()

    self.register_forward_hook()

 def 		lowercase_    (			self			:						Tuple,       a_			:						int=None       ):





    """simple docstring"""



    if frame is not None:
       self.expand_frame(a_       )
    self.frames.append("\n".join(self.frame       )       )
    UpperCamelCase__			    =    []  # start a new frame

 def 		lowercase_    (			self			:						Any,       a_			:						Optional[int]       ):





    """simple docstring"""



    self.frame.append(a_       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    print("\n".join(self.frames       )       )
    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    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"       )
    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    {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			:						Optional[int],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



    if torch.is_tensor(a_       ):
       self.expand_frame(get_abs_min_max(a_,       a_       )       )
       if detect_overflow(a_,       a_       ):
          UpperCamelCase__			    =    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			:						Optional[int]       ):





    """simple docstring"""



    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			:						Optional[Any]       ):





    """simple docstring"""



    self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'       )

 def 		lowercase_    (			self			:						Dict,       a_			:						str,       a_			:						Optional[int],       a_			:						List[str]       ):





    """simple docstring"""



    self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'       )

    # params
    for name, p in module.named_parameters(recurse=a_       ):
       self.analyse_variable(a_,       a_       )

    # inputs
    if isinstance(a_,       a_       ):
       for i, x in enumerate(a_       ):
          self.analyse_variable(a_,       f'input[{i}]'       )
    else:
       self.analyse_variable(a_,       "input"       )

    # outputs
    if isinstance(a_,       a_       ):
       for i, x in enumerate(a_       ):
          # possibly a tuple of tuples
          if isinstance(a_,       a_       ):
             for j, y in enumerate(a_       ):
                self.analyse_variable(a_,       f'output[{i}][{j}]'       )
          else:
             self.analyse_variable(a_,       f'output[{i}]'       )
    else:
       self.analyse_variable(a_,       "output"       )

    self.save_frame()

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    self.model.apply(self._register_forward_hook       )

 def 		lowercase_    (			self			:						List[Any],       a_			:						Tuple       ):





    """simple docstring"""



    module.register_forward_hook(self.forward_hook       )

 def 		lowercase_    (			self			:						str,       a_			:						List[Any],       a_			:						Tuple,       a_			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    False

    UpperCamelCase__			    =    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
       UpperCamelCase__			    =    True

    self.create_frame(a_,       a_,       a_       )

    # 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__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Tuple )		->       Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    var.abs()
   return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					List[str] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    False
   if torch.isnan(_UpperCamelCase ).any().item():
      UpperCamelCase__			    =    True
      print(F'{ctx} has nans' )
   if torch.isinf(_UpperCamelCase ).any().item():
      UpperCamelCase__			    =    True
      print(F'{ctx} has infs' )

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      UpperCamelCase__			    =    var[torch.ge(var.abs()     ,		1_00 )]
      if naaa.numel() > 0:
         print(F'{ctx}:  n100={naaa.numel()}' )
      UpperCamelCase__			    =    var[torch.ge(var.abs()     ,		10_00 )]
      if naaaa.numel() > 0:
         print(F'{ctx}: n1000={naaaa.numel()}' )
      UpperCamelCase__			    =    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			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       str            =       'underflow_overflow'
 _lowerCamelCase			:       Any            =       'tpu_metrics_debug'




'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

        return np.mean((labels - prediction) ** 2				)







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               print("""Error: X and y have different lengths"""				)
               return
        if y.ndim != 1:
               print("""Error: Data set labels must be one dimensional"""				)
               return

        if len(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        if self.prediction is not None:
               return self.prediction
        elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                      return self.right.predict(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


if __name__ == "__main__":
   main()
   import doctest

   doctest.testmod(name="""mean_squarred_error""", verbose=True)



