
"""simple docstring"""






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


__UpperCAmelCase 					=    logging.get_logger(__name__)




class      _SCREAMING_SNAKE_CASE   :






			def __init__(   self			,  __A			,  __A=21			,  __A=[]			,  __A=None			)     ->	List[Any]:
						lowerCAmelCase_       :List[str]     	=    model
						lowerCAmelCase_       :Any     	=    trace_batch_nums
						lowerCAmelCase_       :Tuple     	=    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
						lowerCAmelCase_       :Union[str, Any]     	=    collections.deque([]			,  _A			)
						lowerCAmelCase_       :Union[str, Any]     	=    []
						lowerCAmelCase_       :List[Any]     	=    0
						lowerCAmelCase_       :int     	=    0
						lowerCAmelCase_       :List[str]     	=    False
						lowerCAmelCase_       :List[Any]     	=    """                 """

						self.analyse_model()

						self.register_forward_hook()






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






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						self.frame.append(_A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						print("""\n""".join(self.frames			)			)
						lowerCAmelCase_       :Tuple     	=    []






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :Union[str, Any]     	=    []






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						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"""			)
						lowerCAmelCase_       :Tuple     	=    []






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						# 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
						lowerCAmelCase_       :Optional[Any]     	=    {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			,  __A			,  __A			)     ->	Union[str, Any]:
						if torch.is_tensor(_A			):
									self.expand_frame(get_abs_min_max(_A			,  _A			)			)
									if detect_overflow(_A			,  _A			):
												lowerCAmelCase_       :str     	=    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			)     ->	List[str]:
						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			)     ->	List[Any]:
						self.expand_frame(f"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			)     ->	Union[str, Any]:
						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 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						self.model.apply(self._register_forward_hook			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Tuple:
						module.register_forward_hook(self.forward_hook			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			)     ->	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

						lowerCAmelCase_       :Optional[Any]     	=    False

						lowerCAmelCase_       :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
									lowerCAmelCase_       :Optional[Any]     	=    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 		_snake_case			(      lowercase__						:    List[Any] ,	lowercase__						:    Union[str, Any]						)    ->							Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    var.abs()
			return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""






def 		_snake_case			(      lowercase__						:    Union[str, Any] ,	lowercase__						:    int						)    ->							Union[str, Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[Any]     	=    False
			if torch.isnan(__lowerCamelCase						).any().item():
						lowerCAmelCase_       :Dict     	=    True
						print(f"""{ctx} has nans"""						)
			if torch.isinf(__lowerCamelCase						).any().item():
						lowerCAmelCase_       :Any     	=    True
						print(f"""{ctx} has infs"""						)

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						lowerCAmelCase_       :Dict     	=    var[torch.ge(var.abs() ,	1_0_0						)]
						if naaa.numel() > 0:
									print(f"""{ctx}:  n100={naaa.numel()}"""						)
						lowerCAmelCase_       :Optional[int]     	=    var[torch.ge(var.abs() ,	1_0_0_0						)]
						if naaaa.numel() > 0:
									print(f"""{ctx}: n1000={naaaa.numel()}"""						)
						lowerCAmelCase_       :List[Any]     	=    var[torch.ge(var.abs() ,	1_0_0_0_0						)]
						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      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE				):
			UpperCAmelCase_    :Optional[Any]												=		"underflow_overflow"
			UpperCAmelCase_    :Any												=		"tpu_metrics_debug"








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase   						=     {
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     [
       "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DonutSwinModel",
       "DonutSwinPreTrainedModel",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["DonutFeatureExtractor"]
   __UpperCAmelCase   						=     ["DonutImageProcessor"]


if TYPE_CHECKING:
   from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
   from .processing_donut import DonutProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_donut_swin import (
          DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          DonutSwinModel,
          DonutSwinPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_donut import DonutFeatureExtractor
      from .image_processing_donut import DonutImageProcessor

else:
   import sys

   __UpperCAmelCase   						=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


