"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json'''
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "cpmant"







       def __init__(				self,							lowerCAmelCase__ = 3_0720,							lowerCAmelCase__ = 4096,							lowerCAmelCase__ = 32,							lowerCAmelCase__ = 128,							lowerCAmelCase__ = 1_0240,							lowerCAmelCase__ = 48,							lowerCAmelCase__ = 0.0,							lowerCAmelCase__ = 512,							lowerCAmelCase__ = 2048,							lowerCAmelCase__ = 1e-6,							lowerCAmelCase__ = 1.0,							lowerCAmelCase__ = 32,							lowerCAmelCase__ = 32,							lowerCAmelCase__ = 32,							lowerCAmelCase__ = True,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      prompt_types
            snake_case_       =      prompt_length
            snake_case_       =      segment_types
            snake_case_       =      hidden_size
            snake_case_       =      num_attention_heads
            snake_case_       =      dim_head
            snake_case_       =      dim_ff
            snake_case_       =      num_hidden_layers
            snake_case_       =      position_bias_num_buckets
            snake_case_       =      position_bias_max_distance
            snake_case_       =      dropout_p
            snake_case_       =      eps
            snake_case_       =      use_cache
            snake_case_       =      vocab_size
            snake_case_       =      init_std

"""simple docstring"""


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
    import torch


__UpperCamelCase								=    logging.get_logger(__name__)




class 				UpperCamelCase      :







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__=21,							lowerCAmelCase__=[],							lowerCAmelCase__=None)   ->   Optional[Any]:
            snake_case_       =      model
            snake_case_       =      trace_batch_nums
            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
            snake_case_       =      collections.deque([],							lowerCAmelCase__)
            snake_case_       =      []
            snake_case_       =      0
            snake_case_       =      0
            snake_case_       =      False
            snake_case_       =      '                 '

            self.analyse_model()

            self.register_forward_hook()







       def 	a_		(				self,							lowerCAmelCase__=None)   ->   Optional[int]:
            if frame is not None:
                 self.expand_frame(lowerCAmelCase__)
            self.frames.append('\n'.join(self.frame))
            snake_case_       =      []  # start a new frame







       def 	a_		(				self,							lowerCAmelCase__)   ->   List[str]:
            self.frame.append(lowerCAmelCase__)







       def 	a_		(				self)   ->   List[str]:
            print('\n'.join(self.frames))
            snake_case_       =      []







       def 	a_		(				self)   ->   Tuple:
            snake_case_       =      []







       def 	a_		(				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')
            snake_case_       =      []







       def 	a_		(				self)   ->   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
            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 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   str:
            if torch.is_tensor(lowerCAmelCase__):
                 self.expand_frame(get_abs_min_max(lowerCAmelCase__,							lowerCAmelCase__))
                 if detect_overflow(lowerCAmelCase__,							lowerCAmelCase__):
                      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 	a_		(				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 	a_		(				self)   ->   Dict:
            self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n')







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   List[str]:
            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 	a_		(				self)   ->   List[Any]:
            self.model.apply(self._register_forward_hook)







       def 	a_		(				self,							lowerCAmelCase__)   ->   int:
            module.register_forward_hook(self.forward_hook)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Optional[int]:
            # - input is a tuple of packed inputs (could be non-Tensors)
            # - output could be a Tensor or a tuple of Tensors and non-Tensors

            snake_case_       =      False

            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
                 snake_case_       =      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					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[int]:
     snake_case_       =      var.abs()
     return f'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Union[str, Any]:
     snake_case_       =      False
     if torch.isnan(UpperCAmelCase      ).any().item():
          snake_case_       =      True
          print(f'{ctx} has nans'      )
     if torch.isinf(UpperCAmelCase      ).any().item():
          snake_case_       =      True
          print(f'{ctx} has infs'      )

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          snake_case_       =      var[torch.ge(var.abs()		,      100      )]
          if naaa.numel() > 0:
               print(f'{ctx}:  n100={naaa.numel()}'      )
          snake_case_       =      var[torch.ge(var.abs()		,      1000      )]
          if naaaa.numel() > 0:
               print(f'{ctx}: n1000={naaaa.numel()}'      )
          snake_case_       =      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 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "underflow_overflow"
       SCREAMING_SNAKE_CASE_				=   "tpu_metrics_debug"

