

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
       import torch


lowerCamelCase									= logging.get_logger(__name__)







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_=21,   lowercase_=[],   lowercase_=None	)							->   Any:






     """simple docstring"""






     a__  =model
     a__  =trace_batch_nums
     a__  =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__  =collections.deque([],   lowercase_	)
     a__  =[]
     a__  =0
     a__  =0
     a__  =False
     a__  ="""                 """

     self.analyse_model()

     self.register_forward_hook()



   def 			_UpperCAmelCase			(    self,   lowercase_=None	)							->   Dict:






     """simple docstring"""






     if frame is not None:
       self.expand_frame(lowercase_	)
     self.frames.append('''\n'''.join(self.frame	)	)
     a__  =[]  # start a new frame



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Any:






     """simple docstring"""






     self.frame.append(lowercase_	)



   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     print('''\n'''.join(self.frames	)	)
     a__  =[]



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     a__  =[]



   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """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'''	)
     a__  =[]



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     a__  ={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 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Union[str, Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """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 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_	)							->   List[Any]:






     """simple docstring"""






     self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""	)

     # params
     for name, p in module.named_parameters(recurse=lowercase_	):
       self.analyse_variable(lowercase_,   lowercase_	)

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

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

     self.save_frame()



   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     self.model.apply(self._register_forward_hook	)



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     module.register_forward_hook(self.forward_hook	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =False

     a__  =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__  =True

     self.create_frame(lowercase_,   lowercase_,   lowercase_	)

     # 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__    (      _A				:			Optional[int]     ,				_A				:			List[Any]							):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =False
  if torch.isnan(_A							).any().item():
    a__  =True
    print(F"""{ctx} has nans"""							)
  if torch.isinf(_A							).any().item():
    a__  =True
    print(F"""{ctx} has infs"""							)

  # if needed to monitor large elements can enable the following
  if 0:  # and detected:
    a__  =var[torch.ge(var.abs()     ,				1_00							)]
    if naaa.numel() > 0:
      print(F"""{ctx}:  n100={naaa.numel()}"""							)
    a__  =var[torch.ge(var.abs()     ,				10_00							)]
    if naaaa.numel() > 0:
      print(F"""{ctx}: n1000={naaaa.numel()}"""							)
    a__  =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 						__magic_name__      (							UpperCAmelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     Dict    =       'underflow_overflow'
   lowerCamelCase__			:     List[str]    =       'tpu_metrics_debug'


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  a__  =min(re.search('''^ +'''     ,				_A							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
  return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A							)]							)


lowerCamelCase									= '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCamelCase									= '''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCamelCase									= '''\
# Begin of markdown

Some cool dataset card
'''







class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =Path(lowercase_	) / '''README.md'''
       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertEqual(lowercase_,   {}	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
     with self.assertRaises(lowercase_	):
       DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)
