


'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


__lowercase:		Union[str, Any]    						=  logging.get_logger(__name__)







class 			UpperCAmelCase			:

 def __init__(			self			:						str,       a_			:						str,       a_			:						Optional[Any]=21,       a_			:						Optional[Any]=[],       a_			:						List[str]=None       ):





    """simple docstring"""



    UpperCamelCase__			    =    model
    UpperCamelCase__			    =    trace_batch_nums
    UpperCamelCase__			    =    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
    UpperCamelCase__			    =    collections.deque([],       a_       )
    UpperCamelCase__			    =    []
    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    0
    UpperCamelCase__			    =    False
    UpperCamelCase__			    =    "                 "

    self.analyse_model()

    self.register_forward_hook()

 def 		lowercase_    (			self			:						Tuple,       a_			:						int=None       ):





    """simple docstring"""



    if frame is not None:
       self.expand_frame(a_       )
    self.frames.append("\n".join(self.frame       )       )
    UpperCamelCase__			    =    []  # start a new frame

 def 		lowercase_    (			self			:						Any,       a_			:						Optional[int]       ):





    """simple docstring"""



    self.frame.append(a_       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    print("\n".join(self.frames       )       )
    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    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"       )
    UpperCamelCase__			    =    []

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    {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 		lowercase_    (			self			:						Optional[int],       a_			:						Union[str, Any],       a_			:						List[str]       ):





    """simple docstring"""



    if torch.is_tensor(a_       ):
       self.expand_frame(get_abs_min_max(a_,       a_       )       )
       if detect_overflow(a_,       a_       ):
          UpperCamelCase__			    =    True
    elif var is None:
       self.expand_frame(f'{"None":>17} {ctx}'       )
    else:
       self.expand_frame(f'{"not a tensor":>17} {ctx}'       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'       )

 def 		lowercase_    (			self			:						Dict,       a_			:						str,       a_			:						Optional[int],       a_			:						List[str]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    self.model.apply(self._register_forward_hook       )

 def 		lowercase_    (			self			:						List[Any],       a_			:						Tuple       ):





    """simple docstring"""



    module.register_forward_hook(self.forward_hook       )

 def 		lowercase_    (			self			:						str,       a_			:						List[Any],       a_			:						Tuple,       a_			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    False

    UpperCamelCase__			    =    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
       UpperCamelCase__			    =    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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					Tuple )		->       Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    var.abs()
   return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					List[str] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    False
   if torch.isnan(_UpperCamelCase ).any().item():
      UpperCamelCase__			    =    True
      print(F'{ctx} has nans' )
   if torch.isinf(_UpperCamelCase ).any().item():
      UpperCamelCase__			    =    True
      print(F'{ctx} has infs' )

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

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextV2ForImageClassification",
						    "ConvNextV2Model",
						    "ConvNextV2PreTrainedModel",
						    "ConvNextV2Backbone",
						]


if TYPE_CHECKING:
						from .configuration_convnextva import (
						    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConvNextVaConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnextva import (
												    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextVaBackbone,
												    ConvNextVaForImageClassification,
												    ConvNextVaModel,
												    ConvNextVaPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure)





