
"""simple docstring"""



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCAmelCase__          = {
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Tuple:
  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts		)
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config		)
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights		)

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any]		)     ->					str:
  if args.student_type == "roberta":
    _snake_case					     =       False
  elif args.student_type == "gpt2":
    _snake_case					     =       False
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Union[str, Any]		)     ->					str:
  if args.student_type == "roberta":
    _snake_case					     =       False
def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       argparse.ArgumentParser(description='''Training'''		)
  parser.add_argument('''--force''' ,					action='''store_true''' ,					help='''Overwrite dump_path if it already exists.'''		)

  parser.add_argument(
      '''--dump_path''' ,					type=_lowerCAmelCase ,					required=_lowerCAmelCase ,					help='''The output directory (log, checkpoints, parameters, etc.)'''		)
  parser.add_argument(
      '''--data_file''' ,					type=_lowerCAmelCase ,					required=_lowerCAmelCase ,					help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.''' ,					)

  parser.add_argument(
      '''--student_type''' ,					type=_lowerCAmelCase ,					choices=['''distilbert''', '''roberta''', '''gpt2'''] ,					required=_lowerCAmelCase ,					help='''The student type (DistilBERT, RoBERTa).''' ,					)
  parser.add_argument('''--student_config''' ,					type=_lowerCAmelCase ,					required=_lowerCAmelCase ,					help='''Path to the student configuration.'''		)
  parser.add_argument(
      '''--student_pretrained_weights''' ,					default=_lowerCAmelCase ,					type=_lowerCAmelCase ,					help='''Load student initialization checkpoint.'''		)

  parser.add_argument(
      '''--teacher_type''' ,					choices=['''bert''', '''roberta''', '''gpt2'''] ,					required=_lowerCAmelCase ,					help='''Teacher type (BERT, RoBERTa).'''		)
  parser.add_argument('''--teacher_name''' ,					type=_lowerCAmelCase ,					required=_lowerCAmelCase ,					help='''The teacher model.'''		)

  parser.add_argument('''--temperature''' ,					default=2.0 ,					type=_lowerCAmelCase ,					help='''Temperature for the softmax temperature.'''		)
  parser.add_argument(
      '''--alpha_ce''' ,					default=0.5 ,					type=_lowerCAmelCase ,					help='''Linear weight for the distillation loss. Must be >=0.'''		)
  parser.add_argument(
      '''--alpha_mlm''' ,					default=0.0 ,					type=_lowerCAmelCase ,					help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.''' ,					)
  parser.add_argument('''--alpha_clm''' ,					default=0.5 ,					type=_lowerCAmelCase ,					help='''Linear weight for the CLM loss. Must be >=0.'''		)
  parser.add_argument('''--alpha_mse''' ,					default=0.0 ,					type=_lowerCAmelCase ,					help='''Linear weight of the MSE loss. Must be >=0.'''		)
  parser.add_argument(
      '''--alpha_cos''' ,					default=0.0 ,					type=_lowerCAmelCase ,					help='''Linear weight of the cosine embedding loss. Must be >=0.'''		)

  parser.add_argument(
      '''--mlm''' ,					action='''store_true''' ,					help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''		)
  parser.add_argument(
      '''--mlm_mask_prop''' ,					default=0.15 ,					type=_lowerCAmelCase ,					help='''Proportion of tokens for which we need to make a prediction.''' ,					)
  parser.add_argument('''--word_mask''' ,					default=0.8 ,					type=_lowerCAmelCase ,					help='''Proportion of tokens to mask out.'''		)
  parser.add_argument('''--word_keep''' ,					default=0.1 ,					type=_lowerCAmelCase ,					help='''Proportion of tokens to keep.'''		)
  parser.add_argument('''--word_rand''' ,					default=0.1 ,					type=_lowerCAmelCase ,					help='''Proportion of tokens to randomly replace.'''		)
  parser.add_argument(
      '''--mlm_smoothing''' ,					default=0.7 ,					type=_lowerCAmelCase ,					help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).''' ,					)
  parser.add_argument('''--token_counts''' ,					type=_lowerCAmelCase ,					help='''The token counts in the data_file for MLM.'''		)

  parser.add_argument(
      '''--restrict_ce_to_mask''' ,					action='''store_true''' ,					help='''If true, compute the distillation loss only the [MLM] prediction distribution.''' ,					)
  parser.add_argument(
      '''--freeze_pos_embs''' ,					action='''store_true''' ,					help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.''' ,					)
  parser.add_argument(
      '''--freeze_token_type_embds''' ,					action='''store_true''' ,					help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.''' ,					)

  parser.add_argument('''--n_epoch''' ,					type=_lowerCAmelCase ,					default=3 ,					help='''Number of pass on the whole dataset.'''		)
  parser.add_argument('''--batch_size''' ,					type=_lowerCAmelCase ,					default=5 ,					help='''Batch size (for each process).'''		)
  parser.add_argument(
      '''--group_by_size''' ,					action='''store_false''' ,					help='''If true, group sequences that have similar length into the same batch. Default is true.''' ,					)

  parser.add_argument(
      '''--gradient_accumulation_steps''' ,					type=_lowerCAmelCase ,					default=50 ,					help='''Gradient accumulation for larger training batches.''' ,					)
  parser.add_argument('''--warmup_prop''' ,					default=0.05 ,					type=_lowerCAmelCase ,					help='''Linear warmup proportion.'''		)
  parser.add_argument('''--weight_decay''' ,					default=0.0 ,					type=_lowerCAmelCase ,					help='''Weight decay if we apply some.'''		)
  parser.add_argument('''--learning_rate''' ,					default=5E-4 ,					type=_lowerCAmelCase ,					help='''The initial learning rate for Adam.'''		)
  parser.add_argument('''--adam_epsilon''' ,					default=1E-6 ,					type=_lowerCAmelCase ,					help='''Epsilon for Adam optimizer.'''		)
  parser.add_argument('''--max_grad_norm''' ,					default=5.0 ,					type=_lowerCAmelCase ,					help='''Max gradient norm.'''		)
  parser.add_argument('''--initializer_range''' ,					default=0.02 ,					type=_lowerCAmelCase ,					help='''Random initialization range.'''		)

  parser.add_argument(
      '''--fp16''' ,					action='''store_true''' ,					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' ,					)
  parser.add_argument(
      '''--fp16_opt_level''' ,					type=_lowerCAmelCase ,					default='''O1''' ,					help=(
          '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
          '''See details at https://nvidia.github.io/apex/amp.html'''
      ) ,					)
  parser.add_argument('''--n_gpu''' ,					type=_lowerCAmelCase ,					default=1 ,					help='''Number of GPUs in the node.'''		)
  parser.add_argument('''--local_rank''' ,					type=_lowerCAmelCase ,					default=-1 ,					help='''Distributed training - Local rank'''		)
  parser.add_argument('''--seed''' ,					type=_lowerCAmelCase ,					default=56 ,					help='''Random seed'''		)

  parser.add_argument('''--log_interval''' ,					type=_lowerCAmelCase ,					default=5_00 ,					help='''Tensorboard logging interval.'''		)
  parser.add_argument('''--checkpoint_interval''' ,					type=_lowerCAmelCase ,					default=40_00 ,					help='''Checkpoint interval.'''		)
  _snake_case					     =       parser.parse_args()
  sanity_checks(_lowerCAmelCase		)

  # ARGS #
  init_gpu_params(_lowerCAmelCase		)
  set_seed(_lowerCAmelCase		)
  if args.is_master:
    if os.path.exists(args.dump_path		):
      if not args.force:
        raise ValueError(
            f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
            ''' itUse `--force` if you want to overwrite it'''		)
      else:
        shutil.rmtree(args.dump_path		)

    if not os.path.exists(args.dump_path		):
      os.makedirs(args.dump_path		)
    logger.info(f'''Experiment will be dumped and logged in {args.dump_path}'''		)

    # SAVE PARAMS #
    logger.info(f'''Param: {args}'''		)
    with open(os.path.join(args.dump_path ,					'''parameters.json'''		) ,					'''w'''		) as f:
      json.dump(vars(_lowerCAmelCase		) ,					_lowerCAmelCase ,					indent=4		)
    git_log(args.dump_path		)

  _snake_case					     =       MODEL_CLASSES[args.student_type]
  _snake_case					     =       MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _snake_case					     =       teacher_tokenizer_class.from_pretrained(args.teacher_name		)
  _snake_case					     =       {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _snake_case					     =       tokenizer.all_special_tokens.index(_lowerCAmelCase		)
    _snake_case					     =       tokenizer.all_special_ids[idx]
  logger.info(f'''Special tokens {special_tok_ids}'''		)
  _snake_case					     =       special_tok_ids
  _snake_case					     =       tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'''Loading data from {args.data_file}'''		)
  with open(args.data_file ,					'''rb'''		) as fp:
    _snake_case					     =       pickle.load(_lowerCAmelCase		)

  if args.mlm:
    logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''		)
    with open(args.token_counts ,					'''rb'''		) as fp:
      _snake_case					     =       pickle.load(_lowerCAmelCase		)

    _snake_case					     =       np.maximum(_lowerCAmelCase ,					1		) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _snake_case					     =       0.0  # do not predict special tokens
    _snake_case					     =       torch.from_numpy(_lowerCAmelCase		)
  else:
    _snake_case					     =       None

  _snake_case					     =       LmSeqsDataset(params=_lowerCAmelCase ,					data=_lowerCAmelCase		)
  logger.info('''Data loader created.'''		)

  # STUDENT #
  logger.info(f'''Loading student config from {args.student_config}'''		)
  _snake_case					     =       student_config_class.from_pretrained(args.student_config		)
  _snake_case					     =       True

  if args.student_pretrained_weights is not None:
    logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''		)
    _snake_case					     =       student_model_class.from_pretrained(args.student_pretrained_weights ,					config=_lowerCAmelCase		)
  else:
    _snake_case					     =       student_model_class(_lowerCAmelCase		)

  if args.n_gpu > 0:
    student.to(f'''cuda:{args.local_rank}'''		)
  logger.info('''Student loaded.'''		)

  # TEACHER #
  _snake_case					     =       teacher_model_class.from_pretrained(args.teacher_name ,					output_hidden_states=_lowerCAmelCase		)
  if args.n_gpu > 0:
    teacher.to(f'''cuda:{args.local_rank}'''		)
  logger.info(f'''Teacher loaded from {args.teacher_name}.'''		)

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(_lowerCAmelCase ,					_lowerCAmelCase		)
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(_lowerCAmelCase ,					_lowerCAmelCase		)

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0		) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _snake_case					     =       Distiller(
      params=_lowerCAmelCase ,					dataset=_lowerCAmelCase ,					token_probs=_lowerCAmelCase ,					student=_lowerCAmelCase ,					teacher=_lowerCAmelCase		)
  distiller.train()
  logger.info('''Let\'s go get some drinks.'''		)


if __name__ == "__main__":
 main()








'''simple docstring'''






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   Optional[int]:
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							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  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[int]  )      ->   Dict:
							UpperCAmelCase :       Dict 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[Any] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase :       str 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase   ,      keep_in_memory=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


@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  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							UpperCAmelCase :       Any 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Tuple 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Any 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       List[Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Dict 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize(
    '''features'''   ,      [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]   ,      )
def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Tuple  )      ->   Tuple:
							UpperCAmelCase :       Optional[Any] 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Optional[int] 							=		{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
							UpperCAmelCase :       int 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       Any 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.num_rows == 2
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_3", "col_1", "col_2"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
							UpperCAmelCase :       Tuple 							=		{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
							UpperCAmelCase :       List[str] 							=		features.copy()
							UpperCAmelCase :       Union[str, Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[str] 							=		JsonDatasetReader(_lowerCAmelCase   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.num_rows == 2
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_2", "col_3", "col_1"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''split'''   ,      [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[str]  )      ->   Optional[Any]:
							UpperCAmelCase :       Any 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[Any] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       List[Any] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase   ,      split=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert dataset.split == split if split else "train"


@pytest.mark.parametrize('''path_type'''   ,      [str, list]  )
def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Any  )      ->   Dict:
							if issubclass(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       str 							=		jsonl_path
							elif issubclass(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       Dict 							=		[jsonl_path]
							UpperCAmelCase :       int 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Dict 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[int] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_dataset(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict=("train",)  )      ->   Union[str, Any]:
							assert isinstance(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for split in splits:
														UpperCAmelCase :       List[str] 							=		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  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str  )      ->   Any:
							UpperCAmelCase :       Optional[Any] 							=		tmp_path / '''cache'''
							UpperCAmelCase :       List[str] 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase :       Optional[int] 							=		JsonDatasetReader({'''train''': jsonl_path}   ,      cache_dir=_lowerCAmelCase   ,      keep_in_memory=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase  )


@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  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       Dict 							=		tmp_path / '''cache'''
							UpperCAmelCase :       Dict 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[int] 							=		features.copy() if features else default_expected_features
							UpperCAmelCase :       Union[str, Any] 							=		(
							    Features({feature: Value(_lowerCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							UpperCAmelCase :       Tuple 							=		JsonDatasetReader({'''train''': jsonl_path}   ,      features=_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase  )


@pytest.mark.parametrize('''split'''   ,      [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							if split:
														UpperCAmelCase :       Optional[int] 							=		{split: jsonl_path}
							else:
														UpperCAmelCase :       Any 							=		'''train'''
														UpperCAmelCase :       Any 							=		{'''train''': jsonl_path, '''test''': jsonl_path}
							UpperCAmelCase :       Tuple 							=		tmp_path / '''cache'''
							UpperCAmelCase :       int 							=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase :       Optional[Any] 							=		JsonDatasetReader(_lowerCAmelCase   ,      cache_dir=_lowerCAmelCase  ).read()
							_check_json_datasetdict(_lowerCAmelCase   ,      _lowerCAmelCase   ,      splits=list(path.keys()  )  )
							assert all(dataset[split].split == split for split in path.keys()  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   str:
							return [json.loads(_lowerCAmelCase  ) for line in buffer]




class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	@pytest.mark.parametrize('''lines, load_json_function'''		,			[(True, load_json_lines), (False, load_json)]		)
	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Optional[int]		)			->		Dict:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Union[str, Any] 							=		load_json_function(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								assert isinstance(exported_content[0]		,			__snake_case		)
								assert len(__snake_case		) == 10







	@pytest.mark.parametrize(
	    '''orient, container, keys, len_at'''		,			[
	        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
	        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
	        ('''index''', dict, set('''0123456789'''		), None),
	        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
	        ('''values''', list, None, None),
	        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
	    ]		,			)
	def      A	(						self			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Optional[Any]		)			->		List[Any]:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			orient=__snake_case		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Union[str, Any] 							=		load_json(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								if keys:
															if container is dict:
																						assert exported_content.keys() == keys
															else:
																						assert exported_content[0].keys() == keys
								else:
															assert not hasattr(__snake_case		,			'''keys'''		) and not hasattr(exported_content[0]		,			'''keys'''		)
								if len_at:
															assert len(exported_content[len_at]		) == 10
								else:
															assert len(__snake_case		) == 10







	@pytest.mark.parametrize('''lines, load_json_function'''		,			[(True, load_json_lines), (False, load_json)]		)
	def      A	(						self			:     str		,			__snake_case			:     str		,			__snake_case			:     str		,			__snake_case			:     int		)			->		Any:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			num_proc=2		).write()
															buffer.seek(0		)
															UpperCAmelCase :       Any 							=		load_json_function(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								assert isinstance(exported_content[0]		,			__snake_case		)
								assert len(__snake_case		) == 10







	@pytest.mark.parametrize(
	    '''orient, container, keys, len_at'''		,			[
	        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
	        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
	        ('''index''', dict, set('''0123456789'''		), None),
	        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
	        ('''values''', list, None, None),
	        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
	    ]		,			)
	def      A	(						self			:     Any		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		)			->		Any:
								with io.BytesIO() as buffer:
															JsonDatasetWriter(__snake_case		,			__snake_case		,			lines=__snake_case		,			orient=__snake_case		,			num_proc=2		).write()
															buffer.seek(0		)
															UpperCAmelCase :       List[str] 							=		load_json(__snake_case		)
								assert isinstance(__snake_case		,			__snake_case		)
								if keys:
															if container is dict:
																						assert exported_content.keys() == keys
															else:
																						assert exported_content[0].keys() == keys
								else:
															assert not hasattr(__snake_case		,			'''keys'''		) and not hasattr(exported_content[0]		,			'''keys'''		)
								if len_at:
															assert len(exported_content[len_at]		) == 10
								else:
															assert len(__snake_case		) == 10







	def      A	(						self			:     List[Any]		,			__snake_case			:     str		)			->		Dict:
								with pytest.raises(__snake_case		):
															with io.BytesIO() as buffer:
																						JsonDatasetWriter(__snake_case		,			__snake_case		,			num_proc=0		)







	@pytest.mark.parametrize('''compression, extension'''		,			[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]		)
	def      A	(						self			:     Optional[int]		,			__snake_case			:     Any		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Dict		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		tmp_path_factory.mktemp('''data'''		) / F"""test.json.{extension}"""
								UpperCAmelCase :       List[Any] 							=		str(shared_datadir / F"""test_file.json.{extension}"""		)
								JsonDatasetWriter(__snake_case		,			__snake_case		,			compression=__snake_case		).write()

								with fsspec.open(__snake_case		,			'''rb'''		,			compression='''infer'''		) as f:
															UpperCAmelCase :       str 							=		f.read()
								with fsspec.open(__snake_case		,			'''rb'''		,			compression='''infer'''		) as f:
															UpperCAmelCase :       Optional[int] 							=		f.read()
								assert exported_content == original_content





