





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


_UpperCAmelCase       : Tuple			     =       datasets.utils.logging.get_logger(__name__)

@dataclass
class      lowerCAmelCase							(      datasets.BuilderConfig    ):
 UpperCAmelCase__									=	1_00_00
 UpperCAmelCase__									=	None
 UpperCAmelCase__									=	None


class      lowerCAmelCase							(      datasets.ArrowBasedBuilder    ):
 UpperCAmelCase__									=	ParquetConfig
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     return datasets.DatasetInfo(features=self.config.features			)
 def   A_			(   self						:   List[Any]    ,  UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     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}"""			)
     lowerCamelCase__					:   int      				=	dl_manager.download_and_extract(self.config.data_files			)
     if isinstance(UpperCAmelCase    ,  (str, list, tuple)			):
         lowerCamelCase__					:   Union[str, Any]      				=	data_files
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[Any]      				=	[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__					:   Optional[Any]      				=	[dl_manager.iter_files(UpperCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,  gen_kwargs={'files': files}			)]
     lowerCamelCase__					:   str      				=	[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
             lowerCamelCase__					:   Dict      				=	[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__					:   Optional[int]      				=	[dl_manager.iter_files(UpperCAmelCase			) 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(UpperCAmelCase			):
                 with open(UpperCAmelCase    ,  'rb'			) as f:
                     lowerCamelCase__					:   Tuple      				=	datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase			)			)
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase    ,  gen_kwargs={'files': files}			)			)
     return splits
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   pa.Table			)    ->			pa.Table:
     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
         lowerCamelCase__					:   List[Any]      				=	table_cast(UpperCAmelCase    ,  self.info.features.arrow_schema			)
     return pa_table







 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   int			)    ->			Optional[Any]:
     lowerCamelCase__					:   List[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(UpperCAmelCase			)			):
         with open(UpperCAmelCase    ,  'rb'			) as f:
             lowerCamelCase__					:   int      				=	pq.ParquetFile(UpperCAmelCase			)
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size    ,  columns=self.config.columns			)			):
                     lowerCamelCase__					:   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(UpperCAmelCase			)
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase			)}: {e}"""			)
                 raise










from ..utils import DummyObject, requires_backends

class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Tuple    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Optional[int]			)    ->			List[str]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[str]			)    ->			int:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Any    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   str    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Dict			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Dict			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[str]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   int			)    ->			Dict:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[int]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[str]			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Tuple    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)





