





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def lowerCamelCase__    (							snake_case_	:  dict       )      ->				tuple:
   return (data["data"], data["target"])






def lowerCamelCase__    (							snake_case_	:  np.ndarray       ,      snake_case_	:  np.ndarray       ,      snake_case_	:  np.ndarray       )      ->				np.ndarray:
   __snake_case							       =   XGBRegressor(verbosity=0       ,      random_state=42       )
   xgb.fit(snake_case_       ,      snake_case_       )
   # Predict target for test data
   __snake_case							       =   xgb.predict(snake_case_       )
   __snake_case							       =   predictions.reshape(len(snake_case_       )       ,      1       )
   return predictions






def lowerCamelCase__    (							)      ->				None:
   __snake_case							       =   fetch_california_housing()
   __snake_case       ,	__snake_case							       =   data_handling(snake_case_       )
   __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   train_test_split(
       snake_case_       ,      snake_case_       ,      test_size=0.25       ,      random_state=1       )
   __snake_case							       =   xgboost(snake_case_       ,      snake_case_       ,      snake_case_       )
   # Error printing
   print(f"""Mean Absolute Error : {mean_absolute_error(snake_case_       ,      snake_case_       )}"""       )
   print(f"""Mean Square Error  : {mean_squared_error(snake_case_       ,      snake_case_       )}"""       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()








import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


snake_case_				=			logging.getLogger(__name__)

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






@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str							=    field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
      A_						:				Optional[str]							=    field(
          default='summarization'						,      metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}						,      )
      A_						:				Optional[int]							=    field(
          default=1_024						,      metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=128						,      metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# training examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# validation examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# test examples. -1 means use all.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Source language id for translation.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Target language id for translation.'}  )
      A_						:				Optional[int]							=    field(default=_UpperCAmelCase						,      metadata={'help': '# num_beams to use for evaluation.'}  )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}						,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Dict       )      ->				str:
   logger.info(f"""***** {split} metrics *****"""       )
   for key in sorted(metrics.keys()       ):
      logger.info(f"""  {key} = {metrics[key]}"""       )
   save_json(snake_case_       ,      os.path.join(snake_case_       ,      f"""{split}_results.json"""       )       )






def lowerCamelCase__    (							)      ->				Optional[Any]:
   # 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							       =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

   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()

   check_output_dir(snake_case_       )

   # 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.parallel_mode == ParallelMode.DISTRIBUTED       )       ,      training_args.fpaa       ,      )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # 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()
   logger.info('''Training/evaluation parameters %s'''       ,      snake_case_       )

   # Set seed
   set_seed(training_args.seed       )

   # 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							       =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
   for p in extra_model_params:
      if getattr(snake_case_       ,      snake_case_       ,      snake_case_       ):
         assert hasattr(snake_case_       ,      snake_case_       ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
         setattr(snake_case_       ,      snake_case_       ,      getattr(snake_case_       ,      snake_case_       )       )

   __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       ,      )
   __snake_case							       =   AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path       ,      from_tf='''.ckpt''' in model_args.model_name_or_path       ,      config=snake_case_       ,      cache_dir=model_args.cache_dir       ,      )

   # use task specific params
   use_task_specific_params(snake_case_       ,      data_args.task       )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      __snake_case							       =   model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(snake_case_       ,      (MBartTokenizer, MBartTokenizerFast)       ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(snake_case_       ,      snake_case_       ):
         __snake_case							       =   tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         __snake_case							       =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

   if model_args.freeze_embeds:
      freeze_embeds(snake_case_       )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()       )
      assert_all_frozen(model.get_encoder()       )

   __snake_case							       =   SeqaSeqDataset

   # Get datasets
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''train'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_train       ,      max_target_length=data_args.max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_train
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''val'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_val       ,      max_target_length=data_args.val_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''test'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_test       ,      max_target_length=data_args.test_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   __snake_case							       =   (
       build_compute_metrics_fn(data_args.task       ,      snake_case_       ) if training_args.predict_with_generate else None
   )
   __snake_case							       =   SeqaSeqTrainer(
       model=snake_case_       ,      args=snake_case_       ,      data_args=snake_case_       ,      train_dataset=snake_case_       ,      eval_dataset=snake_case_       ,      data_collator=SeqaSeqDataCollator(
           snake_case_       ,      snake_case_       ,      model.config.decoder_start_token_id       ,      training_args.tpu_num_cores       )       ,      compute_metrics=snake_case_       ,      tokenizer=snake_case_       ,      )

   __snake_case							       =   {}
   # Training
   if training_args.do_train:
      logger.info('''*** Train ***'''       )

      __snake_case							       =   trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
      __snake_case							       =   train_result.metrics
      __snake_case							       =   data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('''train'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir       ,      '''trainer_state.json'''       )       )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''       )

      __snake_case							       =   trainer.evaluate(metric_key_prefix='''val'''       )
      __snake_case							       =   data_args.n_val
      __snake_case							       =   round(metrics['''val_loss''']       ,      4       )

      if trainer.is_world_process_zero():
         handle_metrics('''val'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

   if training_args.do_predict:
      logger.info('''*** Predict ***'''       )

      __snake_case							       =   trainer.predict(test_dataset=snake_case_       ,      metric_key_prefix='''test'''       )
      __snake_case							       =   test_output.metrics
      __snake_case							       =   data_args.n_test

      if trainer.is_world_process_zero():
         __snake_case							       =   round(metrics['''test_loss''']       ,      4       )
         handle_metrics('''test'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         if training_args.predict_with_generate:
            __snake_case							       =   tokenizer.batch_decode(
                test_output.predictions       ,      skip_special_tokens=snake_case_       ,      clean_up_tokenization_spaces=snake_case_       )
            __snake_case							       =   lmap(str.strip       ,      snake_case_       )
            write_txt_file(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''test_generations.txt'''       )       )

   if trainer.is_world_process_zero():
      save_json(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''all_results.json'''       )       )

   return all_metrics






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()

