


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
    import torch


lowerCAmelCase__						=			logging.get_logger(__name__)







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=2_1			,						__lowerCamelCase=[]			,						__lowerCamelCase=None)      ->    Tuple:
            _A				:	Dict       =			model
            _A				:	Tuple       =			trace_batch_nums
            _A				:	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
            _A				:	List[Any]       =			collections.deque([]			,						__lowerCamelCase)
            _A				:	Union[str, Any]       =			[]
            _A				:	List[str]       =			0
            _A				:	Tuple       =			0
            _A				:	Any       =			False
            _A				:	int       =			"                 "

            self.analyse_model()

            self.register_forward_hook()


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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Dict:
            self.frame.append(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Any:
            print("\n".join(self.frames))
            _A				:	Any       =			[]


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            _A				:	List[Any]       =			[]


     def 			_lowerCamelCase			(						self)      ->    List[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")
            _A				:	str       =			[]


     def 			_lowerCamelCase			(						self)      ->    List[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				:	List[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			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            if torch.is_tensor(__lowerCamelCase):
                   self.expand_frame(get_abs_min_max(__lowerCamelCase			,						__lowerCamelCase))
                   if detect_overflow(__lowerCamelCase			,						__lowerCamelCase):
                          _A				:	Union[str, Any]       =			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:
            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)      ->    str:
            self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n")


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    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 			_lowerCamelCase			(						self)      ->    Dict:
            self.model.apply(self._register_forward_hook)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[Any]:
            module.register_forward_hook(self.forward_hook)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Dict:
            # - 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				:	int       =			False

            _A				:	Union[str, Any]       =			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				:	Any       =			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__					:  Optional[int]    ,							UpperCamelCase__					:  Dict						):
       _A				:	Any       =			var.abs()
       return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
def 							_UpperCAmelCase    (UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  List[str]						):
       _A				:	int       =			False
       if torch.isnan(UpperCamelCase__						).any().item():
              _A				:	Union[str, Any]       =			True
              print(f"{ctx} has nans"						)
       if torch.isinf(UpperCamelCase__						).any().item():
              _A				:	int       =			True
              print(f"{ctx} has infs"						)

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              _A				:	Optional[int]       =			var[torch.ge(var.abs()    ,							100						)]
              if naaa.numel() > 0:
                     print(f"{ctx}:  n100={naaa.numel()}"						)
              _A				:	Optional[Any]       =			var[torch.ge(var.abs()    ,							1000						)]
              if naaaa.numel() > 0:
                     print(f"{ctx}: n1000={naaaa.numel()}"						)
              _A				:	Optional[int]       =			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 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"underflow_overflow"
     __SCREAMING_SNAKE_CASE        =			"tpu_metrics_debug"




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
