
'''simple docstring'''



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,
)


UpperCamelCase__      :		str       							=  logging.getLogger(__name__)

@dataclass
class    _a				:





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
       SCREAMING_SNAKE_CASE      =  field(
           default=_lowerCamelCase  ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'})
       SCREAMING_SNAKE_CASE      =  field(
           default=_lowerCamelCase  ,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
       SCREAMING_SNAKE_CASE      =  field(
           default=_lowerCamelCase  ,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,					)
       SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Whether tp freeze the encoder.'})
       SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Whether  to freeze the embeddings.'})






@dataclass
class    _a				:





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  field(
           metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
       SCREAMING_SNAKE_CASE      =  field(
           default='summarization'  ,					metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}  ,					)
       SCREAMING_SNAKE_CASE      =  field(
           default=10_24  ,					metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }  ,					)
       SCREAMING_SNAKE_CASE      =  field(
           default=1_28  ,					metadata={
               'help': (
                   'The maximum total sequence length for target text after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }  ,					)
       SCREAMING_SNAKE_CASE      =  field(
           default=1_42  ,					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``.'
               )
           }  ,					)
       SCREAMING_SNAKE_CASE      =  field(
           default=1_42  ,					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.'
               )
           }  ,					)
       SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# training examples. -1 means use all.'})
       SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# validation examples. -1 means use all.'})
       SCREAMING_SNAKE_CASE      =  field(default=-1  ,					metadata={'help': '# test examples. -1 means use all.'})
       SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Source language id for translation.'})
       SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': 'Target language id for translation.'})
       SCREAMING_SNAKE_CASE      =  field(default=_lowerCamelCase  ,					metadata={'help': '# num_beams to use for evaluation.'})
       SCREAMING_SNAKE_CASE      =  field(
           default=_lowerCamelCase  ,					metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}  ,					)






def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   logger.info(F"***** {split} metrics *****"						)
   for key in sorted(metrics.keys()						):
      logger.info(F"  {key} = {metrics[key]}"						)
   save_json(SCREAMING_SNAKE_CASE_    ,      os.path.join(SCREAMING_SNAKE_CASE_    ,      F"{split}_results.json"						)						)
def    lowerCAmelCase_	(				)     ->     Optional[int]:




   """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.

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

   check_output_dir(SCREAMING_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"""    ,      SCREAMING_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.

   _SCREAMING_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    ,      )

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

   _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    ,      )
   _SCREAMING_SNAKE_CASE      =				AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path    ,      from_tf=""".ckpt""" in model_args.model_name_or_path    ,      config=SCREAMING_SNAKE_CASE_    ,      cache_dir=model_args.cache_dir    ,      )

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

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

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         _SCREAMING_SNAKE_CASE      =				tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

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

   _SCREAMING_SNAKE_CASE      =				SeqaSeqDataset

   # Get datasets
   _SCREAMING_SNAKE_CASE      =				(
       dataset_class(
           SCREAMING_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
   )
   _SCREAMING_SNAKE_CASE      =				(
       dataset_class(
           SCREAMING_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
   )
   _SCREAMING_SNAKE_CASE      =				(
       dataset_class(
           SCREAMING_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
   _SCREAMING_SNAKE_CASE      =				(
       build_compute_metrics_fn(data_args.task    ,      SCREAMING_SNAKE_CASE_						) if training_args.predict_with_generate else None
   )
   _SCREAMING_SNAKE_CASE      =				SeqaSeqTrainer(
       model=SCREAMING_SNAKE_CASE_    ,      args=SCREAMING_SNAKE_CASE_    ,      data_args=SCREAMING_SNAKE_CASE_    ,      train_dataset=SCREAMING_SNAKE_CASE_    ,      eval_dataset=SCREAMING_SNAKE_CASE_    ,      data_collator=SeqaSeqDataCollator(
           SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      model.config.decoder_start_token_id    ,      training_args.tpu_num_cores						)    ,      compute_metrics=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      )

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

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

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("""train"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
         all_metrics.update(SCREAMING_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 ***"""						)

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

      if trainer.is_world_process_zero():
         handle_metrics("""val"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
         all_metrics.update(SCREAMING_SNAKE_CASE_						)

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

      _SCREAMING_SNAKE_CASE      =				trainer.predict(test_dataset=SCREAMING_SNAKE_CASE_    ,      metric_key_prefix="""test"""						)
      _SCREAMING_SNAKE_CASE      =				test_output.metrics
      _SCREAMING_SNAKE_CASE      =				data_args.n_test

      if trainer.is_world_process_zero():
         _SCREAMING_SNAKE_CASE      =				round(metrics["""test_loss"""]    ,      4						)
         handle_metrics("""test"""    ,      SCREAMING_SNAKE_CASE_    ,      training_args.output_dir						)
         all_metrics.update(SCREAMING_SNAKE_CASE_						)

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

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

   return all_metrics
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[str]:




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


if __name__ == "__main__":
     main()








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






