
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case					:							Union[str, Any]           =							{
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							List[str]           =							['LEDTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							str           =							[
						    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LEDForConditionalGeneration',
						    'LEDForQuestionAnswering',
						    'LEDForSequenceClassification',
						    'LEDModel',
						    'LEDPreTrainedModel',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Any           =							['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						__snake_case					:							List[str]           =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
   import torch


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=21 ,     _A=[] ,     _A=None	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      model
          SCREAMING_SNAKE_CASE_	=      trace_batch_nums
          SCREAMING_SNAKE_CASE_	=      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
          SCREAMING_SNAKE_CASE_	=      collections.deque([] ,     _A	)
          SCREAMING_SNAKE_CASE_	=      []
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      0
          SCREAMING_SNAKE_CASE_	=      False
          SCREAMING_SNAKE_CASE_	=      '''                 '''

          self.analyse_model()

          self.register_forward_hook()



       def     _UpperCamelCase	(							self ,     _A=None	)				->				Any:
          if frame is not None:
             self.expand_frame(_A	)
          self.frames.append('''\n'''.join(self.frame	)	)
          SCREAMING_SNAKE_CASE_	=      []  # start a new frame



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



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          print('''\n'''.join(self.frames	)	)
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							self	)				->				int:
          SCREAMING_SNAKE_CASE_	=      []



       def     _UpperCamelCase	(							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'''	)
          SCREAMING_SNAKE_CASE_	=      []



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



       def     _UpperCamelCase	(							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     _UpperCamelCase	(							self	)				->				List[Any]:
          self.expand_frame(F'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)



       def     _UpperCamelCase	(							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     _UpperCamelCase	(							self	)				->				List[Any]:
          self.model.apply(self._register_forward_hook	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          module.register_forward_hook(self.forward_hook	)




       def     _UpperCamelCase	(							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

          SCREAMING_SNAKE_CASE_	=      False

          SCREAMING_SNAKE_CASE_	=      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
             SCREAMING_SNAKE_CASE_	=      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__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      var.abs()
   return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      False
   if torch.isnan(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has nans'''						)
   if torch.isinf(__lowerCamelCase						).any().item():
      SCREAMING_SNAKE_CASE_	=      True
      print(F'''{ctx} has infs'''						)

   # if needed to monitor large elements can enable the following
   if 0:  # and detected:
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  1_00						)]
      if naaa.numel() > 0:
         print(F'''{ctx}:  n100={naaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      var[torch.ge(var.abs(),  10_00						)]
      if naaaa.numel() > 0:
         print(F'''{ctx}: n1000={naaaa.numel()}'''						)
      SCREAMING_SNAKE_CASE_	=      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__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="underflow_overflow"
       UpperCAmelCase_				       ="tpu_metrics_debug"


