






import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__UpperCAmelCase      				=					logging.getLogger(__name__)
@dataclass
class 					a_:



     """simple docstring"""



     __snake_case						:							Dict       			=field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
     __snake_case						:							List[Any]       			=field(
         default=lowerCAmelCase_	,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
     __snake_case						:							Dict       			=field(
         default='''NER'''	,    metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}					)
     __snake_case						:							List[Any]       			=field(
         default=lowerCAmelCase_	,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
     __snake_case						:							List[str]       			=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.
     __snake_case						:							str       			=field(
         default=lowerCAmelCase_	,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,    )

@dataclass
class 					a_:



     """simple docstring"""



     __snake_case						:							Tuple       			=field(
         metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}					)
     __snake_case						:							int       			=field(
         metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					)
     __snake_case						:							List[Any]       			=field(
         default=1_28	,    metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }	,    )
     __snake_case						:							Union[str, Any]       			=field(
         default=lowerCAmelCase_	,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)





def 		A_			(		)							->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
 SCREAMING_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.'	)

 SCREAMING_SNAKE_CASE							  =						import_module('tasks'	)

 try:
  SCREAMING_SNAKE_CASE							  =						getattr(lowercase_					,       model_args.task_type	)
  SCREAMING_SNAKE_CASE							  =						token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
      f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''	)

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,       datefmt='%m/%d/%Y %H:%M:%S'					,       level=logging.INFO					,       )
 logger.info(
     'n_replicas: %s, distributed training: %s, 16-bits training: %s'					,       training_args.n_replicas					,       bool(training_args.n_replicas > 1	)					,       training_args.fpaa					,       )
 logger.info('Training/evaluation parameters %s'					,       lowercase_	)

 # Prepare Token Classification task
 SCREAMING_SNAKE_CASE							  =						token_classification_task.get_labels(data_args.labels	)
 SCREAMING_SNAKE_CASE							  =						dict(enumerate(lowercase_	)	)
 SCREAMING_SNAKE_CASE							  =						len(lowercase_	)

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

 SCREAMING_SNAKE_CASE							  =						AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path					,       num_labels=lowercase_					,       idalabel=lowercase_					,       labelaid={label: i for i, label in enumerate(lowercase_	)}					,       cache_dir=model_args.cache_dir					,       )
 SCREAMING_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=model_args.use_fast					,       )

 with training_args.strategy.scope():
  SCREAMING_SNAKE_CASE							  =						TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path					,       from_pt=bool('.bin' in model_args.model_name_or_path	)					,       config=lowercase_					,       cache_dir=model_args.cache_dir					,       )

 # Get datasets
 SCREAMING_SNAKE_CASE							  =						(
     TFTokenClassificationDataset(
         token_classification_task=lowercase_					,       data_dir=data_args.data_dir					,       tokenizer=lowercase_					,       labels=lowercase_					,       model_type=config.model_type					,       max_seq_length=data_args.max_seq_length					,       overwrite_cache=data_args.overwrite_cache					,       mode=Split.train					,       )
     if training_args.do_train
     else None
 )
 SCREAMING_SNAKE_CASE							  =						(
     TFTokenClassificationDataset(
         token_classification_task=lowercase_					,       data_dir=data_args.data_dir					,       tokenizer=lowercase_					,       labels=lowercase_					,       model_type=config.model_type					,       max_seq_length=data_args.max_seq_length					,       overwrite_cache=data_args.overwrite_cache					,       mode=Split.dev					,       )
     if training_args.do_eval
     else None
 )

 def align_predictions(lowercase_					,       lowercase_	) -> Tuple[List[int], List[int]]:
  SCREAMING_SNAKE_CASE							  =						np.argmax(lowercase_					,       axis=2	)
  SCREAMING_SNAKE_CASE							  =						preds.shape
  SCREAMING_SNAKE_CASE							  =						[[] for _ in range(lowercase_	)]
  SCREAMING_SNAKE_CASE							  =						[[] for _ in range(lowercase_	)]

  for i in range(lowercase_	):
   for j in range(lowercase_	):
    if label_ids[i, j] != -1_0_0:
     out_label_list[i].append(label_map[label_ids[i][j]]	)
     preds_list[i].append(label_map[preds[i][j]]	)

  return preds_list, out_label_list

 def compute_metrics(lowercase_	) -> Dict:
  SCREAMING_SNAKE_CASE							  =						align_predictions(p.predictions					,       p.label_ids	)

  return {
      "precision": precision_score(lowercase_					,       lowercase_	),
      "recall": recall_score(lowercase_					,       lowercase_	),
      "f1": fa_score(lowercase_					,       lowercase_	),
  }

 # Initialize our Trainer
 SCREAMING_SNAKE_CASE							  =						TFTrainer(
     model=lowercase_					,       args=lowercase_					,       train_dataset=train_dataset.get_dataset() if train_dataset else None					,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,       compute_metrics=lowercase_					,       )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir	)

 # Evaluation
 SCREAMING_SNAKE_CASE							  =						{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'	)

  SCREAMING_SNAKE_CASE							  =						trainer.evaluate()
  SCREAMING_SNAKE_CASE							  =						os.path.join(training_args.output_dir					,       'eval_results.txt'	)

  with open(lowercase_					,       'w'	) as writer:
   logger.info('***** Eval results *****'	)

   for key, value in result.items():
    logger.info('  %s = %s'					,       lowercase_					,       lowercase_	)
    writer.write('%s = %s\n' % (key, value)	)

   results.update(lowercase_	)

    # Predict
 if training_args.do_predict:
  SCREAMING_SNAKE_CASE							  =						TFTokenClassificationDataset(
      token_classification_task=lowercase_					,       data_dir=data_args.data_dir					,       tokenizer=lowercase_					,       labels=lowercase_					,       model_type=config.model_type					,       max_seq_length=data_args.max_seq_length					,       overwrite_cache=data_args.overwrite_cache					,       mode=Split.test					,       )

  SCREAMING_SNAKE_CASE							  =						trainer.predict(test_dataset.get_dataset()	)
  SCREAMING_SNAKE_CASE							  =						align_predictions(lowercase_					,       lowercase_	)
  SCREAMING_SNAKE_CASE							  =						classification_report(lowercase_					,       lowercase_	)

  logger.info('\n%s'					,       lowercase_	)

  SCREAMING_SNAKE_CASE							  =						os.path.join(training_args.output_dir					,       'test_results.txt'	)

  with open(lowercase_					,       'w'	) as writer:
   writer.write('%s\n' % report	)

  # Save predictions
  SCREAMING_SNAKE_CASE							  =						os.path.join(training_args.output_dir					,       'test_predictions.txt'	)

  with open(lowercase_					,       'w'	) as writer:
   with open(os.path.join(data_args.data_dir					,       'test.txt'	)					,       'r'	) as f:
    SCREAMING_SNAKE_CASE							  =						0

    for line in f:
     if line.startswith('-DOCSTART-'	) or line == "" or line == "\n":
      writer.write(lowercase_	)

      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      SCREAMING_SNAKE_CASE							  =						line.split()[0] + ' ' + preds_list[example_id].pop(0	) + '\n'

      writer.write(lowercase_	)
     else:
      logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'					,       line.split()[0]	)

 return results


if __name__ == "__main__":
      main()












from datetime import date

import requests

__UpperCAmelCase      				=							"https://api.carbonintensity.org.uk/intensity"





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						requests.get(lowercase_	).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def 		A_			(		lowercase_					,       lowercase_	)							->list:







 """simple docstring"""

 return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
            print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'{fetch_last_half_hour() = }')




