



'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.get_logger(__name__)




class 				__lowerCAmelCase:






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=21      ,       SCREAMING_SNAKE_CASE  :							str=[]      ,       SCREAMING_SNAKE_CASE  :							Dict=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      model
					SCREAMING_SNAKE_CASE_						:List[Any]										=      trace_batch_nums
					SCREAMING_SNAKE_CASE_						:str										=      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
					SCREAMING_SNAKE_CASE_						:Any										=      collections.deque([]      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      []
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      0
					SCREAMING_SNAKE_CASE_						:List[Any]										=      False
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      '                 '

					self.analyse_model()

					self.register_forward_hook()






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Any=None	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					self.frame.append(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					print('\n'.join(self.frames	)	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Tuple	):




					"""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'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      {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  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					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	):
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      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  :							Tuple	):




					"""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  :							int	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							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=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 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					self.model.apply(self._register_forward_hook	)






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					module.register_forward_hook(self.forward_hook	)






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      False

					SCREAMING_SNAKE_CASE_						:Tuple										=      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
							SCREAMING_SNAKE_CASE_						:List[str]										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      var.abs()
		return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      False
		if torch.isnan(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:Tuple										=      True
				print(F'{ctx} has nans'     )
		if torch.isinf(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:List[str]										=      True
				print(F'{ctx} has infs'     )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		100     )]
				if naaa.numel() > 0:
						print(F'{ctx}:  n100={naaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		1000     )]
				if naaaa.numel() > 0:
						print(F'{ctx}: n1000={naaaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      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(							lowerCAmelCase__      ):
			__snake_case  :		List[Any]		=      'underflow_overflow'
			__snake_case  :		Optional[int]		=      'tpu_metrics_debug'


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir						)
        and os.listdir(training_args.output_dir						)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."						)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,						datefmt="%m/%d/%Y %H:%M:%S"					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1						)					,						training_args.fpaa					,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank						):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"					,						A__						)

    # Prepare Question-Answering task
    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case						=   AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
    snake_case						=   AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

    # Get datasets
    snake_case						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # Data collator
    snake_case						=   DataCollatorWithPadding(A__					,						pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

