
'''simple docstring'''





import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowercase						:      Tuple							=							torch.cuda.is_available()
lowercase						:      Union[str, Any]							=							[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowercase						:      List[Any]							=							['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowercase						:      int							=							{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       lowercase							:  int 			=   """\n""".join(snake_case__       )
       Path(snake_case__       ).open("""w"""       ).writelines(snake_case__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[str]:
       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(snake_case__      ,			f"{split}.source"       )      ,			snake_case__       )
              _dump_articles(os.path.join(snake_case__      ,			f"{split}.target"       )      ,			snake_case__       )
       return tmp_dir




class __snake_case	(   UpperCamelCase_  ):
    @classmethod
    def    _SCREAMING_SNAKE_CASE (						cls   ):



           '''simple docstring'''







           return cls
    @require_torch_multi_gpu
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   {
               """no_teacher""": True,
               """freeze_encoder""": True,
               """gpus""": 2,
               """overwrite_output_dir""": True,
               """sortish_sampler""": True,
           }
           self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE	,check_contents=__SCREAMING_SNAKE_CASE   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case=True   ):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   {
               """label_smoothing""": 0.0,
               """early_stopping_patience""": -1,
               """train_batch_size""": 1,
               """eval_batch_size""": 2,
               """max_epochs""": 2,
               """alpha_mlm""": 0.2,
               """alpha_ce""": 0.8,
               """do_predict""": True,
               """model_name_or_path""": """sshleifer/tinier_bart""",
               """teacher""": CHEAP_ARGS["""model_name_or_path"""],
               """val_check_interval""": 0.5,
           }
           default_updates.update(__SCREAMING_SNAKE_CASE   )
           lowercase							:  Tuple 			=   CHEAP_ARGS.copy()
           lowercase							:  Dict 			=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
           lowercase							:  Any 			=   self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=__SCREAMING_SNAKE_CASE	,output_dir=__SCREAMING_SNAKE_CASE	,**__SCREAMING_SNAKE_CASE   )

           def convert(snake_case	,snake_case   ):
                  if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                         return ""
                  if v is False or v is None:
                         return ""
                  if v is True:  # or len(str(v))==0:
                         return f"--{k}"
                  return f"--{k}={v}"

           lowercase							:  List[str] 			=   [x for x in (convert(__SCREAMING_SNAKE_CASE	,__SCREAMING_SNAKE_CASE   ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE   )]
           lowercase							:  List[Any] 			=   [sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args
           execute_subprocess_async(__SCREAMING_SNAKE_CASE	,env=self.get_env()   )

           lowercase							:  List[str] 			=   os.listdir(__SCREAMING_SNAKE_CASE   )
           lowercase							:  Any 			=   {os.path.basename(__SCREAMING_SNAKE_CASE   ) for p in contents}
           lowercase							:  List[Any] 			=   [p for p in contents if p.endswith("""ckpt"""   )]
           assert len(__SCREAMING_SNAKE_CASE   ) > 0

           self.assertIn("""test_generations.txt"""	,__SCREAMING_SNAKE_CASE   )
           self.assertIn("""test_results.txt"""	,__SCREAMING_SNAKE_CASE   )

           # get the following from the module, (we don't have access to `model` here)
           lowercase							:  Tuple 			=   os.path.join(__SCREAMING_SNAKE_CASE	,"""metrics.json"""   )
           lowercase							:  str 			=   """rouge2"""

           lowercase							:  List[Any] 			=   load_json(__SCREAMING_SNAKE_CASE   )
           # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
           print(__SCREAMING_SNAKE_CASE   )
           lowercase							:  Dict 			=   metrics["""val"""][-1]
           self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]	,0.01   )
           self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"]	,__SCREAMING_SNAKE_CASE   )
           self.assertEqual(len(metrics["""test"""]   )	,1   )
           lowercase							:  Any 			=   int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1   )
           self.assertEqual(len(metrics["""val"""]   )	,__SCREAMING_SNAKE_CASE   )


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


lowercase						:      Any							=							logging.get_logger(__name__)

class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=21	,snake_case=[]	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   model
           lowercase							:  List[Any] 			=   trace_batch_nums
           lowercase							:  Union[str, 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
           lowercase							:  List[Any] 			=   collections.deque([]	,snake_case   )
           lowercase							:  List[Any] 			=   []
           lowercase							:  Tuple 			=   0
           lowercase							:  Optional[Any] 			=   0
           lowercase							:  int 			=   False
           lowercase							:  List[str] 			=   """                 """

           self.analyse_model()

           self.register_forward_hook()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=None   ):



           '''simple docstring'''







           if frame is not None:
                  self.expand_frame(snake_case   )
           self.frames.append("""\n""".join(self.frame   )   )
           lowercase							:  List[str] 			=   []  # start a new frame
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           self.frame.append(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           print("""\n""".join(self.frames   )   )
           lowercase							:  Any 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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"""   )
           lowercase							:  Dict 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   {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    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



           '''simple docstring'''







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



           '''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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







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

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

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

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

           self.save_frame()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.model.apply(self._register_forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           module.register_forward_hook(self.forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   False

           lowercase							:  str 			=   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
                  lowercase							:  Tuple 			=   True

           self.create_frame(snake_case	,snake_case	,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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  str 			=   var.abs()
       return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Any 			=   False
       if torch.isnan(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Tuple 			=   True
              print(f"{ctx} has nans"       )
       if torch.isinf(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Union[str, Any] 			=   True
              print(f"{ctx} has infs"       )

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              lowercase							:  Optional[int] 			=   var[torch.ge(var.abs()      ,			100       )]
              if naaa.numel() > 0:
                     print(f"{ctx}:  n100={naaa.numel()}"       )
              lowercase							:  Union[str, Any] 			=   var[torch.ge(var.abs()      ,			1_000       )]
              if naaaa.numel() > 0:
                     print(f"{ctx}: n1000={naaaa.numel()}"       )
              lowercase							:  List[str] 			=   var[torch.ge(var.abs()      ,			10_000       )]
              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 __snake_case	(   lowerCAmelCase  ):
    _a	:		Dict=       "underflow_overflow"
    _a	:		Union[str, Any]=       "tpu_metrics_debug"
