




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
     import torch


UpperCamelCase								=     logging.get_logger(__name__)





class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[str]=21   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=[]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=None   )      ->       str:
         lowerCAmelCase__		= model
         lowerCAmelCase__		= trace_batch_nums
         lowerCAmelCase__		= 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__		= collections.deque([]   ,	SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= []
         lowerCAmelCase__		= 0
         lowerCAmelCase__		= 0
         lowerCAmelCase__		= False
         lowerCAmelCase__		= "                 "

         self.analyse_model()

         self.register_forward_hook()


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


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   )      ->       str:
         self.frame.append(SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Optional[Any]   )      ->       Any:
         print("\n".join(self.frames   )   )
         lowerCAmelCase__		= []


    def    a      (       self     :     str   )      ->       Optional[int]:
         lowerCAmelCase__		= []


    def    a      (       self     :     List[Any]   )      ->       Tuple:
         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__		= []


    def    a      (       self     :     List[str]   )      ->       List[str]:
         # 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__		= {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     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   )      ->       Optional[Any]:
         if torch.is_tensor(SCREAMING_SNAKE_CASE__   ):
              self.expand_frame(get_abs_min_max(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )   )
              if detect_overflow(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
                   lowerCAmelCase__		= 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     :     str   )      ->       List[Any]:
         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     :     Tuple   )      ->       str:
         self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'   )


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Tuple   )      ->       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=SCREAMING_SNAKE_CASE__   ):
              self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         # inputs
         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
              for i, x in enumerate(SCREAMING_SNAKE_CASE__   ):
                   self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	f'input[{i}]'   )
         else:
              self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	"input"   )

         # outputs
         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
              for i, x in enumerate(SCREAMING_SNAKE_CASE__   ):
                   # possibly a tuple of tuples
                   if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
                        for j, y in enumerate(SCREAMING_SNAKE_CASE__   ):
                             self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	f'output[{i}][{j}]'   )
                   else:
                        self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	f'output[{i}]'   )
         else:
              self.analyse_variable(SCREAMING_SNAKE_CASE__   ,	"output"   )

         self.save_frame()


    def    a      (       self     :     Dict   )      ->       Union[str, Any]:
         self.model.apply(self._register_forward_hook   )


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       List[str]:
         module.register_forward_hook(self.forward_hook   )


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Tuple   )      ->       List[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__		= False

         lowerCAmelCase__		= 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__		= True

         self.create_frame(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )

         # 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_     :					Any   ,				lowerCAmelCase_     :					Optional[int]			):
     """simple docstring"""

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



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					str			):
     """simple docstring"""

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

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          lowerCAmelCase__		= var[torch.ge(var.abs()   ,				100			)]
          if naaa.numel() > 0:
               print(F'{ctx}:  n100={naaa.numel()}'			)
          lowerCAmelCase__		= var[torch.ge(var.abs()   ,				1000			)]
          if naaaa.numel() > 0:
               print(F'{ctx}: n1000={naaaa.numel()}'			)
          lowerCAmelCase__		= var[torch.ge(var.abs()   ,				1_0000			)]
          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       (    UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      "underflow_overflow"
    snake_case__					=      "tpu_metrics_debug"






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image





class  __lowerCamelCase       (    unittest.TestCase	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= load_tool("image-captioning"   )
         self.tool.setup()
         lowerCAmelCase__		= load_tool("image-captioning"   ,	remote=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Any   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Dict   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )
