



import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=21	,	a_=[]	,	a_=None		):
											a__					   =		model
											a__					   =		trace_batch_nums
											a__					   =		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
											a__					   =		collections.deque([]	,	a_		)
											a__					   =		[]
											a__					   =		0
											a__					   =		0
											a__					   =		False
											a__					   =		"""                 """

											self.analyse_model()

											self.register_forward_hook()


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


							def _a   (				self	,	a_		):
											self.frame.append(a_		)


							def _a   (				self		):
											print("""\n""".join(self.frames		)		)
											a__					   =		[]


							def _a   (				self		):
											a__					   =		[]


							def _a   (				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"""		)
											a__					   =		[]


							def _a   (				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
											a__					   =		{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 _a   (				self	,	a_	,	a_		):
											if torch.is_tensor(a_		):
															self.expand_frame(get_abs_min_max(a_	,	a_		)		)
															if detect_overflow(a_	,	a_		):
																			a__					   =		True
											elif var is None:
															self.expand_frame(F'''{"None":>17} {ctx}'''		)
											else:
															self.expand_frame(F'''{"not a tensor":>17} {ctx}'''		)


							def _a   (				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 _a   (				self		):
											self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											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 _a   (				self		):
											self.model.apply(self._register_forward_hook		)


							def _a   (				self	,	a_		):
											module.register_forward_hook(self.forward_hook		)







							def _a   (				self	,	a_	,	a_	,	a_		):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											a__					   =		False

											a__					   =		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
															a__					   =		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       A_   (						__a  :					Union[str, Any]			,		__a  :					List[str]		):

				"""simple docstring"""
				a__					   =		var.abs()
				return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''

def       A_   (						__a  :					List[str]			,		__a  :					Optional[int]		):

				"""simple docstring"""
				a__					   =		False
				if torch.isnan(__a		).any().item():
								a__					   =		True
								print(F'''{ctx} has nans'''		)
				if torch.isinf(__a		).any().item():
								a__					   =		True
								print(F'''{ctx} has infs'''		)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								a__					   =		var[torch.ge(var.abs()			,		100		)]
								if naaa.numel() > 0:
												print(F'''{ctx}:  n100={naaa.numel()}'''		)
								a__					   =		var[torch.ge(var.abs()			,		1_000		)]
								if naaaa.numel() > 0:
												print(F'''{ctx}: n1000={naaaa.numel()}'''		)
								a__					   =		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   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   str       =       """underflow_overflow"""
							UpperCamelCase__					:   Dict       =       """tpu_metrics_debug"""



'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

