




'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


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





class   lowerCamelCase_							:




					'''simple docstring'''






					def __init__(					self,						__UpperCamelCase,						__UpperCamelCase=21,						__UpperCamelCase=[],						__UpperCamelCase=None				):
							_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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase=None				):
							if frame is not None:
									self.expand_frame(__UpperCamelCase				)
							self.frames.append("\n".join(self.frame				)				)
							_snake_case			 = []  # start a new frame






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							self.frame.append(__UpperCamelCase				)






					def        __SCREAMING_SNAKE_CASE			(					self				):
							print("\n".join(self.frames				)				)
							_snake_case			 = []






					def        __SCREAMING_SNAKE_CASE			(					self				):
							_snake_case			 = []






					def        __SCREAMING_SNAKE_CASE			(					self				):
							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        __SCREAMING_SNAKE_CASE			(					self				):
							# 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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase				):
							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        __SCREAMING_SNAKE_CASE			(					self				):
							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				):
							self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''				)






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
							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        __SCREAMING_SNAKE_CASE			(					self				):
							self.model.apply(self._register_forward_hook				)






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							module.register_forward_hook(self.forward_hook				)






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
							# - 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 	_lowercase	(			lowercase_		,							lowercase_						):



		'''simple docstring'''







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






def 	_lowercase	(			lowercase_		,							lowercase_						):



		'''simple docstring'''







		_snake_case			 = False
		if torch.isnan(lowercase_						).any().item():
				_snake_case			 = True
				print(f'''{ctx} has nans'''						)
		if torch.isinf(lowercase_						).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   lowerCamelCase_							( UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__					=				"""underflow_overflow"""
					lowercase__					=				"""tpu_metrics_debug"""











'''simple docstring'''







import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class   lowerCamelCase_							:




					'''simple docstring'''






					lowercase__					=				42





class   lowerCamelCase_							( unittest.TestCase							):




					'''simple docstring'''






					def        __SCREAMING_SNAKE_CASE			(					self				):
							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)

							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)

							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
									_snake_case			 = SpmConverter(__UpperCamelCase				)
							self.assertEqual(len(__UpperCamelCase				),						0				)

							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)

							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
									_snake_case			 = SpmConverter(__UpperCamelCase				)
							self.assertEqual(len(__UpperCamelCase				),						1				)

							self.assertIn(
							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)





