

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCAmelCase_	     =     logging.get_logger(__name__)





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Optional[int]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Optional[int]=2_1							,       _UpperCamelCase			:    str=[]							,       _UpperCamelCase			:    List[str]=None							)				->Optional[int]:
									snake_case_       =     model
									snake_case_       =     trace_batch_nums
									snake_case_       =     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
									snake_case_       =     collections.deque([]							,       _UpperCamelCase							)
									snake_case_       =     []
									snake_case_       =     0
									snake_case_       =     0
									snake_case_       =     False
									snake_case_       =     '''                 '''

									self.analyse_model()

									self.register_forward_hook()







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    Dict=None							)				->Optional[Any]:
									if frame is not None:
															self.expand_frame(_UpperCamelCase							)
									self.frames.append('''\n'''.join(self.frame							)							)
									snake_case_       =     []  # start a new frame







			def     snake_case__(					self			:    str							,       _UpperCamelCase			:    Dict							)				->List[Any]:
									self.frame.append(_UpperCamelCase							)







			def     snake_case__(					self			:    Any							)				->Tuple:
									print('''\n'''.join(self.frames							)							)
									snake_case_       =     []







			def     snake_case__(					self			:    Dict							)				->Optional[Any]:
									snake_case_       =     []







			def     snake_case__(					self			:    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'''							)
									snake_case_       =     []







			def     snake_case__(					self			:    Tuple							)				->Optional[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
									snake_case_       =     {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     snake_case__(					self			:    Any							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    List[str]							)				->Optional[int]:
									if torch.is_tensor(_UpperCamelCase							):
															self.expand_frame(get_abs_min_max(_UpperCamelCase							,       _UpperCamelCase							)							)
															if detect_overflow(_UpperCamelCase							,       _UpperCamelCase							):
																					snake_case_       =     True
									elif var is None:
															self.expand_frame(f'''{'None':>17} {ctx}'''							)
									else:
															self.expand_frame(f'''{'not a tensor':>17} {ctx}'''							)







			def     snake_case__(					self			:    List[Any]							)				->Optional[int]:
									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     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''							)







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    List[Any]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Any							)				->Any:
									self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''							)

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

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

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

									self.save_frame()







			def     snake_case__(					self			:    Optional[Any]							)				->Tuple:
									self.model.apply(self._register_forward_hook							)







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    str							)				->Tuple:
									module.register_forward_hook(self.forward_hook							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    Any							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    List[str]							)				->Union[str, 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

									snake_case_       =     False

									snake_case_       =     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
															snake_case_       =     True

									self.create_frame(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							)

									# 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 (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     var.abs()
						return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     False
						if torch.isnan(SCREAMING_SNAKE_CASE__	).any().item():
												snake_case_       =     True
												print(F'''{ctx} has nans'''	)
						if torch.isinf(SCREAMING_SNAKE_CASE__	).any().item():
												snake_case_       =     True
												print(F'''{ctx} has infs'''	)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												snake_case_       =     var[torch.ge(var.abs()  ,		100	)]
												if naaa.numel() > 0:
																		print(F'''{ctx}:  n100={naaa.numel()}'''	)
												snake_case_       =     var[torch.ge(var.abs()  ,		1000	)]
												if naaaa.numel() > 0:
																		print(F'''{ctx}: n1000={naaaa.numel()}'''	)
												snake_case_       =     var[torch.ge(var.abs()  ,		10000	)]
												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_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Any						      =    "underflow_overflow"
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    "tpu_metrics_debug"


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->List[Any]:
									snake_case_       =     0
									# max greyscale value for #FFFFFF
									snake_case_       =     int(self.get_greyscale(2_5_5							,       2_5_5							,       2_5_5							)							)

									if not self.min_threshold < threshold < self.max_threshold:
															snake_case_       =     f'''Factor value should be from 0 to {self.max_threshold}'''
															raise ValueError(_UpperCamelCase							)

									snake_case_       =     input_img
									snake_case_       =     threshold
									snake_case_,   snake_case_       =     self.input_img.shape[1], self.input_img.shape[0]

									# error table size (+4 columns and +1 row) greater than input image because of
									# lack of if statements
									snake_case_       =     [
									    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
									]
									snake_case_       =     np.ones((self.width, self.height, 3)							,       np.uinta							) * 2_5_5







			@classmethod
			def     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def     snake_case__(					self			:    Any							)				->None:
									for y in range(self.height							):
															for x in range(self.width							):
																					snake_case_       =     int(self.get_greyscale(*self.input_img[y][x]							)							)
																					if self.threshold > greyscale + self.error_table[y][x]:
																											snake_case_       =     (0, 0, 0)
																											snake_case_       =     greyscale + self.error_table[x][y]
																					else:
																											snake_case_       =     (2_5_5, 2_5_5, 2_5_5)
																											snake_case_       =     greyscale + self.error_table[x][y] - 2_5_5
																					self.error_table[y][x + 1] += int(8 / 3_2 * current_error							)
																					self.error_table[y][x + 2] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x] += int(8 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error							)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					lowerCAmelCase_	     =     [
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_26, 1_30, 1_40)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    f"""Original image with dithering threshold: {burkes.threshold}""",
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()