


'''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"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		requests.get(UpperCAmelCase__		,      stream=UpperCAmelCase__      )

						with open(f'{path}/{img_name}'		,      """wb+"""      ) as img_file:
												shutil.copyfileobj(response.raw		,      UpperCAmelCase__      )
						del response
						return apod_data


def 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


if __name__ == "__main__":
						print(save_apod("""YOUR API KEY"""))
						UpperCamelCase_							:							Optional[Any]				 =		get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
						print(apollo_aaaa_items[0]["""data"""][0]["""description"""])

