
"""simple docstring"""
import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowercase_       :						Tuple     					=	[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							int=None,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Any=None				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = True
  while ask_again:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = input(lowerCamelCase__				)
    try:
      if default is not None and len(lowerCamelCase__				) == 0:
        return default
      return convert_value(lowerCamelCase__				) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict=[],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							str=0				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				int							    = BulletMenu(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = menu.run(default_choice=lowerCamelCase__				)
  return convert_value(lowerCamelCase__				) if convert_value is not None else result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = int(lowerCamelCase__				)
  return DynamoBackend(DYNAMO_BACKENDS[value]				).value





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = int(lowerCamelCase__				)
  return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(lowerCamelCase__				)
  return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  return {"yes": True, "no": False}[value.lower()]

class 			UpperCamelCase      (							argparse.RawDescriptionHelpFormatter  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = super()._format_usage(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = usage.replace("<command> [<args>] "  ,     ""				)
       return usage





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


lowercase_       :						List[str]     					=	datasets.utils.logging.get_logger(__name__)
@dataclass
class 			UpperCamelCase      (							datasets.BuilderConfig  ):
     A__      =			10000
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							datasets.ArrowBasedBuilder  ):
     A__      =			ParquetConfig
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return datasets.DatasetInfo(features=self.config.features				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if not self.config.data_files:
         raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(snake_case__  ,     (str, list, tuple)				):
         _SCREAMING_SNAKE_CASE   :				int							    = data_files
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [dl_manager.iter_files(snake_case__				) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,     gen_kwargs={"files": files}				)]
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       for split_name, files in data_files.items():
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _SCREAMING_SNAKE_CASE   :				Tuple							    = [dl_manager.iter_files(snake_case__				) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(snake_case__				):
             with open(snake_case__  ,     "rb"				) as f:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = datasets.Features.from_arrow_schema(pq.read_schema(snake_case__				)				)
             break
         splits.append(datasets.SplitGenerator(name=snake_case__  ,     gen_kwargs={"files": files}				)				)
       return splits
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = table_cast(snake_case__  ,     self.info.features.arrow_schema				)
       return pa_table




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema				) != sorted(self.config.columns				):
           raise ValueError(
               F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''				)
       for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
         with open(snake_case__  ,     "rb"				) as f:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pq.ParquetFile(snake_case__				)
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size  ,     columns=self.config.columns				)				):
               _SCREAMING_SNAKE_CASE   :				int							    = pa.Table.from_batches([record_batch]				)
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F'''{file_idx}_{batch_idx}''', self._cast_table(snake_case__				)
           except ValueError as e:
             logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case__				)}: {e}'''				)
             raise



