



"""simple docstring"""




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


UpperCAmelCase				:							List[str]								=logging.get_logger(__name__)


class  _lowercase				:
				'''simple docstring'''







				def __init__(	self       ,	snake_case__       ,	snake_case__=21       ,	snake_case__=[]       ,	snake_case__=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([]       ,	snake_case__      )
									UpperCamelCase_											=				[]
									UpperCamelCase_											=				0
									UpperCamelCase_											=				0
									UpperCamelCase_											=				False
									UpperCamelCase_											=				"                 "

									self.analyse_model()

									self.register_forward_hook()

				def  _lowerCamelCase   (	self       ,	snake_case__=None      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


									self.frame.append(snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				[]

				def  _lowerCamelCase   (	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"      )
									UpperCamelCase_											=				[]

				def  _lowerCamelCase   (	self      ):

									'''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  _lowerCamelCase   (	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__      ):
																			UpperCamelCase_											=				True
									elif var is None:
														self.expand_frame(F"""{'None':>17} {ctx}"""      )
									else:
														self.expand_frame(F"""{'not a tensor':>17} {ctx}"""      )

				def  _lowerCamelCase   (	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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									self.model.apply(self._register_forward_hook      )

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


									module.register_forward_hook(self.forward_hook      )

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''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(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 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				var.abs()
					return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				False
					if torch.isnan(_lowerCAmelCase).any().item():
										UpperCamelCase_											=				True
										print(f"""{ctx} has nans""")
					if torch.isinf(_lowerCAmelCase).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  _lowercase				(a_ ):
				'''simple docstring'''







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





from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
	from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
