




'''simple docstring'''



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


UpperCAmelCase_       :  str      =		logging.getLogger(__name__)





@dataclass
class     lowerCAmelCase   :
       __lowercase  :      str        =			field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
       __lowercase  :      Optional[str]        =			field(
           default=__lowerCAmelCase				,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
       __lowercase  :      Optional[str]        =			field(
           default=__lowerCAmelCase				,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
       __lowercase  :      bool        =			field(default=__lowerCAmelCase				,   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.
       __lowercase  :      Optional[str]        =			field(
           default=__lowerCAmelCase				,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,   )







def 			_UpperCamelCase ()->   List[Any]:




     '''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'''   ,	_lowerCamelCase	)

     # 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=_lowerCamelCase   ,	)
     __snake_case    					=  AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path   ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)   ,	config=_lowerCamelCase   ,	cache_dir=model_args.cache_dir   ,	)

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

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

     # Initialize our Trainer
     __snake_case    					=  Trainer(
         model=_lowerCamelCase   ,	args=_lowerCamelCase   ,	train_dataset=_lowerCamelCase   ,	eval_dataset=_lowerCamelCase   ,	data_collator=_lowerCamelCase   ,	)

     # 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 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]	)->   List[str]:




     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()












'''simple docstring'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






