


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					:				Optional[Any]       =      logging.getLogger(__name__)

@dataclass
class   lowerCamelCase						:
       _UpperCamelCase     		=   field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
       _UpperCamelCase     		=   field(
           default=SCREAMING_SNAKE_CASE,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
       _UpperCamelCase     		=   field(
           default=SCREAMING_SNAKE_CASE,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
       _UpperCamelCase     		=   field(default=SCREAMING_SNAKE_CASE,      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     		=   field(
           default=SCREAMING_SNAKE_CASE,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},      )







def  _UpperCAmelCase						( )		->Dict:


 '''simple docstring'''
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 snake_case :			Any							=   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 :			Any							=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
 else:
  snake_case	,       snake_case	,       snake_case :			Optional[int]							=   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 :			Any							=   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 :			Dict							=   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 :			Union[str, Any]							=   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 :			List[str]							=   hasattr(model.config			,  '''lang2id'''							)
 snake_case :			Any							=   (
     SquadDataset(
         __lowerCamelCase			,  tokenizer=__lowerCamelCase			,  is_language_sensitive=__lowerCamelCase			,  cache_dir=model_args.cache_dir							)
     if training_args.do_train
     else None
 )
 snake_case :			int							=   (
     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 :			Tuple							=   DataCollatorWithPadding(__lowerCamelCase			,  pad_to_multiple_of=8							) if training_args.fpaa else None

 # Initialize our Trainer
 snake_case :			Optional[Any]							=   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							)		->int:


 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
  main()










from typing import Any

import numpy as np






def  _UpperCAmelCase						( __lowerCamelCase							)		->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 :			int							=   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							)		->Any:


 '''simple docstring'''
 snake_case :			Dict							=   np.random.randn(__lowerCamelCase			,  __lowerCamelCase							)
 snake_case :			List[Any]							=   np.dot(__lowerCamelCase			,  random_matrix.T							)
 assert _is_matrix_spd(__lowerCamelCase							)
 return spd_matrix






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase = 1000			,  __lowerCamelCase = 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 :			Any							=   np.zeros((np.shape(__lowerCamelCase							)[0], 1)							)
 snake_case :			Optional[int]							=   np.copy(__lowerCamelCase							)
 snake_case :			Optional[Any]							=   np.copy(__lowerCamelCase							)

 # Set initial errors in solution guess and residual.
 snake_case :			Optional[Any]							=   1E9
 snake_case :			List[Any]							=   1E9
 snake_case :			List[str]							=   1E9

 # Set iteration counter to threshold number of iterations.
 snake_case :			List[str]							=   0

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

  # The main algorithm.

  # Update search direction magnitude.
  snake_case :			List[str]							=   np.dot(ra.T			,  __lowerCamelCase							) / np.dot(pa.T			,  __lowerCamelCase							)
  # Update solution guess.
  snake_case :			Optional[int]							=   xa + alpha * pa
  # Calculate new residual.
  snake_case :			str							=   ra - alpha * w
  # Calculate new Krylov subspace scale.
  snake_case :			List[str]							=   np.dot(r.T			,  __lowerCamelCase							) / np.dot(ra.T			,  __lowerCamelCase							)
  # Calculate new A conjuage search direction.
  snake_case :			List[Any]							=   r + beta * pa

  # Calculate errors.
  snake_case :			List[Any]							=   np.linalg.norm(r - ra							)
  snake_case :			Any							=   np.linalg.norm(x - xa							)
  snake_case :			str							=   np.maximum(__lowerCamelCase			,  __lowerCamelCase							)

  # Update variables.
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			List[str]							=   np.copy(__lowerCamelCase							)

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

 return x






def  _UpperCAmelCase						( )		->None:


 '''simple docstring'''
 snake_case :			str							=   3
 snake_case :			Dict							=   _create_spd_matrix(__lowerCamelCase							)
 snake_case :			str							=   np.random.randn(__lowerCamelCase			,  1							)
 snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

 # Numpy solution.
 snake_case :			Any							=   np.linalg.solve(__lowerCamelCase			,  __lowerCamelCase							)

 # Our implementation.
 snake_case :			List[str]							=   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()






