



'''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


lowercase_			=  logging.getLogger(__name__)






@dataclass
class __A      :



 '''simple docstring'''







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





def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  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.
          _a  ,   _a  ,   _a     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
     else:
          _a  ,   _a  ,   _a     =  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.

     _a     =  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  ,     )
     _a     =  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  ,     )
     _a     =  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
     _a     =  hasattr(model.config  ,     '''lang2id''')
     _a     =  (
         SquadDataset(
             __A  ,     tokenizer=__A  ,     is_language_sensitive=__A  ,     cache_dir=model_args.cache_dir)
         if training_args.do_train
         else None
     )
     _a     =  (
         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
     _a     =  DataCollatorWithPadding(__A  ,     pad_to_multiple_of=8) if training_args.fpaa else None

     # Initialize our Trainer
     _a     =  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 lowerCAmelCase  (__A):



     """simple docstring"""



     main()


if __name__ == "__main__":
  main()








'''simple docstring'''





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

lowercase_			=  0.25 + 0.0J
lowercase_			=  -0.4 + 0.6J
lowercase_			=  -0.1 + 0.651J
lowercase_			=  -2.0
lowercase_			=  56
lowercase_			=  2.0
lowercase_			=  666




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return numpy.exp(__A) + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return z_values * z_values + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  x.reshape((nb_pixels, 1))
     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  y.reshape((1, nb_pixels))
     return x + 1.0J * y




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A = None  ,     ):



     """simple docstring"""



     _a     =  z_a.astype('''complex64''')
     for _ in range(__A):
          _a     =  eval_function(__A  ,     __A)
          if infinity is not None:
               numpy.nan_to_num(__A  ,     copy=__A  ,     nan=__A)
               _a     =  infinity
     return z_n




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  (abs(__A)).transpose()
     _a     =  abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius)
     pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$''')
     pyplot.show()




def lowerCAmelCase  ():



     """simple docstring"""



     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in multiply''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''invalid value encountered in multiply'''  ,     )
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in absolute''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in exp''')


if __name__ == "__main__":
  lowercase_			=  prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  lowercase_			=  24
  lowercase_			=  2 * abs(c_cauliflower) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_cauliflower, escape_radius, z_final)

  lowercase_			=  64
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  161
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  12
  lowercase_			=  1_0000.0
  lowercase_			=  iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("e^z+c", c_exponential, escape_radius, z_final)



