"""simple docstring"""


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
 import torch


__A       			=      logging.get_logger(__name__)






class 					a					:



  def __init__(		self   :    List[str]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Union[str, Any]=21						,  lowerCamelCase_   :    int=[]						,  lowerCamelCase_   :    Union[str, Any]=None     )      -> int:
      __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([]						,  lowerCamelCase_     )
      __a			      =						[]
      __a			      =						0
      __a			      =						0
      __a			      =						False
      __a			      =						"""                 """

      self.analyse_model()

      self.register_forward_hook()



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    List[Any]=None     )      -> List[Any]:
      if frame is not None:
          self.expand_frame(lowerCamelCase_     )
      self.frames.append("""\n""".join(self.frame     )     )
      __a			      =						[]  # start a new frame



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Optional[Any]     )      -> Union[str, Any]:
      self.frame.append(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int     )      -> List[Any]:
      print("""\n""".join(self.frames     )     )
      __a			      =						[]



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> int:
      __a			      =						[]



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Dict:
      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        lowerCAmelCase_				(		self   :    Tuple     )      -> Union[str, 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
      __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        lowerCAmelCase_				(		self   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Optional[Any]     )      -> Tuple:
      if torch.is_tensor(lowerCamelCase_     ):
          self.expand_frame(get_abs_min_max(lowerCamelCase_						,  lowerCamelCase_     )     )
          if detect_overflow(lowerCamelCase_						,  lowerCamelCase_     ):
              __a			      =						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   :    Dict     )      -> 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        lowerCAmelCase_				(		self   :    Tuple     )      -> str:
      self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""     )



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any     )      -> Dict:
      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   :    List[str]     )      -> Optional[int]:
      self.model.apply(self._register_forward_hook     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    Tuple     )      -> Optional[int]:
      module.register_forward_hook(self.forward_hook     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Dict     )      -> List[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

      __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(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 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Dict						):
    __a			      =						var.abs()
    return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple						):
    __a			      =						False
    if torch.isnan(_lowerCAmelCase						).any().item():
        __a			      =						True
        print(f"""{ctx} has nans"""						)
    if torch.isinf(_lowerCAmelCase						).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()				, 1000						)]
        if naaaa.numel() > 0:
            print(f"""{ctx}: n1000={naaaa.numel()}"""						)
        __a			      =						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 					a					(				A_							):
  A_ :				str 			=							'''underflow_overflow'''
  A_ :				Dict 			=							'''tpu_metrics_debug'''

"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
