





"""simple docstring"""


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A_     :      Dict								=logging.getLogger(__name__)





def SCREAMING_SNAKE_CASE_   (       )->		Union[str, Any]:
       _lowerCamelCase    	=					argparse.ArgumentParser(description='PyTorch Transformer Language Model'		)
       parser.add_argument('--model_name'						,  type=snake_case						,  default='transfo-xl-wt103'						,  help='pretrained model name'		)
       parser.add_argument(
           '--split'						,  type=snake_case						,  default='test'						,  choices=['all', 'valid', 'test']						,  help='which split to evaluate'		)
       parser.add_argument('--batch_size'						,  type=snake_case						,  default=10						,  help='batch size'		)
       parser.add_argument('--tgt_len'						,  type=snake_case						,  default=128						,  help='number of tokens to predict'		)
       parser.add_argument('--ext_len'						,  type=snake_case						,  default=0						,  help='length of the extended context'		)
       parser.add_argument('--mem_len'						,  type=snake_case						,  default=1_600						,  help='length of the retained previous heads'		)
       parser.add_argument('--clamp_len'						,  type=snake_case						,  default=1_000						,  help='max positional embedding index'		)
       parser.add_argument('--no_cuda'						,  action='store_true'						,  help='Do not use CUDA even though CUA is available'		)
       parser.add_argument('--work_dir'						,  type=snake_case						,  required=snake_case						,  help='path to the work_dir'		)
       parser.add_argument('--no_log'						,  action='store_true'						,  help='do not log the eval result'		)
       parser.add_argument('--same_length'						,  action='store_true'						,  help='set same length attention with masking'		)
       parser.add_argument('--server_ip'						,  type=snake_case						,  default=''						,  help='Can be used for distant debugging.'		)
       parser.add_argument('--server_port'						,  type=snake_case						,  default=''						,  help='Can be used for distant debugging.'		)
       _lowerCamelCase    	=					parser.parse_args()
       assert args.ext_len >= 0, "extended context length must be non-negative"

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print('Waiting for debugger attach'		)
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,  redirect_output=snake_case		)
              ptvsd.wait_for_attach()

       _lowerCamelCase    	=					torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'		)
       logger.info('device: {}'.format(snake_case		)		)

       # Load a pre-processed dataset
       # You can also build the corpus yourself using TransfoXLCorpus methods
       # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
       # and tokenizing the dataset
       # The pre-processed corpus is a convertion (using the conversion script )
       _lowerCamelCase    	=					TransfoXLCorpus.from_pretrained(args.model_name		)

       _lowerCamelCase    	=					corpus.get_iterator('valid'						,  args.batch_size						,  args.tgt_len						,  device=snake_case						,  ext_len=args.ext_len		)
       _lowerCamelCase    	=					corpus.get_iterator('test'						,  args.batch_size						,  args.tgt_len						,  device=snake_case						,  ext_len=args.ext_len		)

       # Load a pre-trained model
       _lowerCamelCase    	=					TransfoXLLMHeadModel.from_pretrained(args.model_name		)
       model.to(snake_case		)

       logger.info(
           'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
               args.batch_size						,  args.tgt_len						,  args.ext_len						,  args.mem_len						,  args.clamp_len		)		)

       model.reset_memory_length(args.mem_len		)
       if args.clamp_len > 0:
              _lowerCamelCase    	=					args.clamp_len
       if args.same_length:
              _lowerCamelCase    	=					True

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(snake_case   :					str		):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              _lowerCamelCase	,	_lowerCamelCase    	=					0, 0.0
              _lowerCamelCase    	=					time.time()
              with torch.no_grad():
                     _lowerCamelCase    	=					None
                     for idx, (data, target, seq_len) in enumerate(snake_case		):
                            _lowerCamelCase    	=					model(snake_case						,  lm_labels=snake_case						,  mems=snake_case		)
                            _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase    	=					ret
                            _lowerCamelCase    	=					loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     _lowerCamelCase    	=					time.time() - start_time
              logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(snake_case						,  1_000 * total_time / (idx + 1)		)		)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              _lowerCamelCase    	=					evaluate(snake_case		)
              _lowerCamelCase    	=					evaluate(snake_case		)
       elif args.split == "valid":
              _lowerCamelCase    	=					evaluate(snake_case		)
              _lowerCamelCase    	=					None
       elif args.split == "test":
              _lowerCamelCase    	=					evaluate(snake_case		)
              _lowerCamelCase    	=					None

       def format_log(snake_case   :					Any						,  snake_case   :					int		):
              _lowerCamelCase    	=					'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(snake_case						,  snake_case						,  math.exp(snake_case		)		)
              return log_str

       _lowerCamelCase    	=					''
       if valid_loss is not None:
              log_str += format_log(snake_case						,  'valid'		)
       if test_loss is not None:
              log_str += format_log(snake_case						,  'test'		)

       logger.info('=' * 100		)
       logger.info(snake_case		)
       logger.info('=' * 100		)


if __name__ == "__main__":
      main()













"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






