
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					List[Any] ,      lowerCAmelCase      :					Tuple=21 ,      lowerCAmelCase      :					int=[] ,      lowerCAmelCase      :					Optional[Any]=None					):
											lowercase       :    Union[str, Any]											=  model
											lowercase       :    Union[str, Any]											=  trace_batch_nums
											lowercase       :    Optional[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       :    int											=  collections.deque([] ,      _lowerCAmelCase					)
											lowercase       :    str											=  []
											lowercase       :    Optional[int]											=  0
											lowercase       :    int											=  0
											lowercase       :    int											=  False
											lowercase       :    List[str]											=  """                 """

											self.analyse_model()

											self.register_forward_hook()

					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					Optional[Any]=None					):
											if frame is not None:
																	self.expand_frame(_lowerCAmelCase					)
											self.frames.append('''\n'''.join(self.frame					)					)
											lowercase       :    str											=  []  # start a new frame

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					List[Any]					):
											self.frame.append(_lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Dict					):
											print('''\n'''.join(self.frames					)					)
											lowercase       :    List[Any]											=  []

					def        _lowerCAmelCase	(			self      :					Any					):
											lowercase       :    Optional[int]											=  []

					def        _lowerCAmelCase	(			self      :					Union[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'''					)
											lowercase       :    Any											=  []

					def        _lowerCAmelCase	(			self      :					Tuple					):
											# 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
											lowercase       :    str											=  {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      :					Any ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Optional[int]					):
											if torch.is_tensor(_lowerCAmelCase					):
																	self.expand_frame(get_abs_min_max(_lowerCAmelCase ,      _lowerCAmelCase					)					)
																	if detect_overflow(_lowerCAmelCase ,      _lowerCAmelCase					):
																							lowercase       :    Optional[int]											=  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      :					Union[str, Any]					):
											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      :					Any					):
											self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''					)

					def        _lowerCAmelCase	(			self      :					List[Any] ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					Union[str, Any] ,      lowerCAmelCase      :					Optional[Any]					):
											self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''					)

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

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

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

											self.save_frame()

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											self.model.apply(self._register_forward_hook					)

					def        _lowerCAmelCase	(			self      :					str ,      lowerCAmelCase      :					List[str]					):
											module.register_forward_hook(self.forward_hook					)




					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					Any ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					List[str]					):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowercase       :    Union[str, Any]											=  False

											lowercase       :    List[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       :    Dict											=  True

											self.create_frame(_lowerCAmelCase ,      _lowerCAmelCase ,      _lowerCAmelCase					)

											# 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_      (			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				Optional[Any]       ):
						lowercase       :    Dict											=  var.abs()
						return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				int       ):
						lowercase       :    str											=  False
						if torch.isnan(__lowerCamelCase       ).any().item():
												lowercase       :    List[Any]											=  True
												print(f'''{ctx} has nans'''       )
						if torch.isinf(__lowerCamelCase       ).any().item():
												lowercase       :    List[str]											=  True
												print(f'''{ctx} has infs'''       )

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												lowercase       :    Dict											=  var[torch.ge(var.abs()							,			1_00       )]
												if naaa.numel() > 0:
																		print(f'''{ctx}:  n100={naaa.numel()}'''       )
												lowercase       :    Tuple											=  var[torch.ge(var.abs()							,			10_00       )]
												if naaaa.numel() > 0:
																		print(f'''{ctx}: n1000={naaaa.numel()}'''       )
												lowercase       :    Optional[int]											=  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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: Optional[Any]			  =				"""underflow_overflow"""
					a__: Union[str, Any]			  =				"""tpu_metrics_debug"""




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

											self.register_modules(unet=lowerCAmelCase ,      scheduler=lowerCAmelCase					)




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


