
'''simple docstring'''





from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       seed(1       )
       return [gauss(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ) for _ in range(SCREAMING_SNAKE_CASE__       )]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       return [k for k in range(SCREAMING_SNAKE_CASE__       ) for _ in range(instance_count[k]       )]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       return sum(SCREAMING_SNAKE_CASE__       ) / instance_count







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       return instance_count / total_count







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       lowercase							:  Optional[int] 			=   []  # An empty list to store all squared differences
       # iterate over number of elements in items
       for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
              # for loop iterates over number of elements in inner layer of items
              for j in range(len(items[i]       )       ):
                     # appending squared differences to 'squared_diff' list
                     squared_diff.append((items[i][j] - means[i]) ** 2       )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
       lowercase							:  Optional[Any] 			=   len(SCREAMING_SNAKE_CASE__       )  # Number of classes in dataset
       return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       lowercase							:  Tuple 			=   []
       # for loop iterates over number of elements in list
       for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
              # for loop iterates over number of inner items of each element
              for j in range(len(x_items[i]       )       ):
                     lowercase							:  int 			=   []  # to store all discriminant values of each item as a list
                     # for loop iterates over number of classes we have in our dataset
                     for k in range(len(SCREAMING_SNAKE_CASE__       )       ):
                            # appending values of discriminants for each class to 'temp' list
                            temp.append(
                                x_items[i][j] * (means[k] / variance)
                                - (means[k] ** 2 / (2 * variance))
                                + log(probabilities[k]       )       )
                     # appending discriminant values of each item to 'results' list
                     results.append(SCREAMING_SNAKE_CASE__       )

       return [result.index(max(SCREAMING_SNAKE_CASE__       )       ) for result in results]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       lowercase							:  Any 			=   sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ) if i == j       )
       # percentage of accuracy equals to number of correct predictions divided by number
       # of all data and multiplied by 100
       return (correct / len(SCREAMING_SNAKE_CASE__       )) * 100


lowercase						:      List[str]							=							TypeVar("""num""")







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ = lambda SCREAMING_SNAKE_CASE__       : True      ,			SCREAMING_SNAKE_CASE__ = None      ,			)				->					num:
       while True:
              try:
                     lowercase							:  Tuple 			=   input_type(input(SCREAMING_SNAKE_CASE__       ).strip() or default       )
                     if condition(SCREAMING_SNAKE_CASE__       ):
                            return user_input
                     else:
                            print(f"{user_input}: {err_msg}"       )
                            continue
              except ValueError:
                     print(
                         f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"       )







def 		_snake_case(						)				->					Tuple:
       while True:
              print(""" Linear Discriminant Analysis """.center(50      ,			"""*"""       )       )
              print("""*""" * 50      ,			"""\n"""       )
              print("""First of all we should specify the number of classes that"""       )
              print("""we want to generate as training dataset"""       )
              # Trying to get number of classes
              lowercase							:  Tuple 			=   valid_input(
                  input_type=SCREAMING_SNAKE_CASE__      ,			condition=lambda SCREAMING_SNAKE_CASE__       : x > 0      ,			input_msg="""Enter the number of classes (Data Groupings): """      ,			err_msg="""Number of classes should be positive!"""      ,			)

              print("""-""" * 100       )

              # Trying to get the value of standard deviation
              lowercase							:  int 			=   valid_input(
                  input_type=SCREAMING_SNAKE_CASE__      ,			condition=lambda SCREAMING_SNAKE_CASE__       : x >= 0      ,			input_msg=(
                      """Enter the value of standard deviation"""
                      """(Default value is 1.0 for all classes): """
                  )      ,			err_msg="""Standard deviation should not be negative!"""      ,			default="""1.0"""      ,			)

              print("""-""" * 100       )

              # Trying to get number of instances in classes and theirs means to generate
              # dataset
              lowercase							:  Optional[int] 			=   []  # An empty list to store instance counts of classes in dataset
              for i in range(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  List[str] 			=   valid_input(
                         input_type=SCREAMING_SNAKE_CASE__      ,			condition=lambda SCREAMING_SNAKE_CASE__       : x > 0      ,			input_msg=(f"Enter The number of instances for class_{i+1}: ")      ,			err_msg="""Number of instances should be positive!"""      ,			)
                     counts.append(SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              # An empty list to store values of user-entered means of classes
              lowercase							:  List[str] 			=   []
              for a in range(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  Optional[int] 			=   valid_input(
                         input_type=SCREAMING_SNAKE_CASE__      ,			input_msg=(f"Enter the value of mean for class_{a+1}: ")      ,			err_msg="""This is an invalid value."""      ,			)
                     user_means.append(SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              print("""Standard deviation: """      ,			SCREAMING_SNAKE_CASE__       )
              # print out the number of instances in classes in separated line
              for i, count in enumerate(SCREAMING_SNAKE_CASE__      ,			1       ):
                     print(f"Number of instances in class_{i} is: {count}"       )
              print("""-""" * 100       )

              # print out mean values of classes separated line
              for i, user_mean in enumerate(SCREAMING_SNAKE_CASE__      ,			1       ):
                     print(f"Mean of class_{i} is: {user_mean}"       )
              print("""-""" * 100       )

              # Generating training dataset drawn from gaussian distribution
              lowercase							:  Dict 			=   [
                  gaussian_distribution(user_means[j]      ,			SCREAMING_SNAKE_CASE__      ,			counts[j]       )
                  for j in range(SCREAMING_SNAKE_CASE__       )
              ]
              print("""Generated Normal Distribution: \n"""      ,			SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              # Generating Ys to detecting corresponding classes
              lowercase							:  List[str] 			=   y_generator(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
              print("""Generated Corresponding Ys: \n"""      ,			SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              # Calculating the value of actual mean for each class
              lowercase							:  List[Any] 			=   [calculate_mean(counts[k]      ,			x[k]       ) for k in range(SCREAMING_SNAKE_CASE__       )]
              # for loop iterates over number of elements in 'actual_means' list and print
              # out them in separated line
              for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE__      ,			1       ):
                     print(f"Actual(Real) mean of class_{i} is: {actual_mean}"       )
              print("""-""" * 100       )

              # Calculating the value of probabilities for each class
              lowercase							:  Dict 			=   [
                  calculate_probabilities(counts[i]      ,			sum(SCREAMING_SNAKE_CASE__       )       ) for i in range(SCREAMING_SNAKE_CASE__       )
              ]

              # for loop iterates over number of elements in 'probabilities' list and print
              # out them in separated line
              for i, probability in enumerate(SCREAMING_SNAKE_CASE__      ,			1       ):
                     print(f"Probability of class_{i} is: {probability}"       )
              print("""-""" * 100       )

              # Calculating the values of variance for each class
              lowercase							:  Dict 			=   calculate_variance(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			sum(SCREAMING_SNAKE_CASE__       )       )
              print("""Variance: """      ,			SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              # Predicting Y values
              # storing predicted Y values in 'pre_indexes' variable
              lowercase							:  Tuple 			=   predict_y_values(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
              print("""-""" * 100       )

              # Calculating Accuracy of the model
              print(f"Accuracy: {accuracy(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )}"       )
              print("""-""" * 100       )
              print(""" DONE """.center(100      ,			"""+"""       )       )

              if input("""Press any key to restart or 'q' for quit: """       ).strip().lower() == "q":
                     print("""\n""" + """GoodBye!""".center(100      ,			"""-"""       ) + """\n"""       )
                     break
              system("""cls""" if name == """nt""" else """clear"""       )  # noqa: S605


if __name__ == "__main__":
  main()


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


lowercase						:      Any							=							logging.get_logger(__name__)

class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=21	,snake_case=[]	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   model
           lowercase							:  List[Any] 			=   trace_batch_nums
           lowercase							:  Union[str, Any] 			=   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
           lowercase							:  List[Any] 			=   collections.deque([]	,snake_case   )
           lowercase							:  List[Any] 			=   []
           lowercase							:  Tuple 			=   0
           lowercase							:  Optional[Any] 			=   0
           lowercase							:  int 			=   False
           lowercase							:  List[str] 			=   """                 """

           self.analyse_model()

           self.register_forward_hook()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=None   ):



           '''simple docstring'''







           if frame is not None:
                  self.expand_frame(snake_case   )
           self.frames.append("""\n""".join(self.frame   )   )
           lowercase							:  List[str] 			=   []  # start a new frame
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           self.frame.append(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           print("""\n""".join(self.frames   )   )
           lowercase							:  Any 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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"""   )
           lowercase							:  Dict 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   {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	,snake_case	,snake_case   ):



           '''simple docstring'''







           if torch.is_tensor(snake_case   ):
                  self.expand_frame(get_abs_min_max(snake_case	,snake_case   )   )
                  if detect_overflow(snake_case	,snake_case   ):
                         lowercase							:  str 			=   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   ):



           '''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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







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

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

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

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

           self.save_frame()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.model.apply(self._register_forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           module.register_forward_hook(self.forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   False

           lowercase							:  str 			=   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
                  lowercase							:  Tuple 			=   True

           self.create_frame(snake_case	,snake_case	,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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  str 			=   var.abs()
       return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Any 			=   False
       if torch.isnan(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Tuple 			=   True
              print(f"{ctx} has nans"       )
       if torch.isinf(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Union[str, Any] 			=   True
              print(f"{ctx} has infs"       )

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              lowercase							:  Optional[int] 			=   var[torch.ge(var.abs()      ,			100       )]
              if naaa.numel() > 0:
                     print(f"{ctx}:  n100={naaa.numel()}"       )
              lowercase							:  Union[str, Any] 			=   var[torch.ge(var.abs()      ,			1_000       )]
              if naaaa.numel() > 0:
                     print(f"{ctx}: n1000={naaaa.numel()}"       )
              lowercase							:  List[str] 			=   var[torch.ge(var.abs()      ,			10_000       )]
              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 __snake_case	(   lowerCAmelCase  ):
    _a	:		Dict=       "underflow_overflow"
    _a	:		Union[str, Any]=       "tpu_metrics_debug"
