


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


A  :     List[str]    					=    datasets.utils.logging.get_logger(__name__)
@dataclass
class 						_UpperCamelCase						(     datasets.BuilderConfig	):
							'''simple docstring'''







							__UpperCAmelCase		:					int          =1_0_0_0_0
							__UpperCAmelCase		:					Optional[List[str]]          =None
							__UpperCAmelCase		:					Optional[datasets.Features]          =None
class 						_UpperCamelCase						(     datasets.ArrowBasedBuilder	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =ParquetConfig

							def     snake_case       (  self			):
										return datasets.DatasetInfo(features=self.config.features			)

							def     snake_case       (  self					,     __a			):
										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       					=    dl_manager.download_and_extract(self.config.data_files			)
										if isinstance(__a					,     (str, list, tuple)			):
													__lowerCAmelCase       					=    data_files
													if isinstance(__a					,     __a			):
																__lowerCAmelCase       					=    [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													__lowerCAmelCase       					=    [dl_manager.iter_files(__a			) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,     gen_kwargs={"files": files}			)]
										__lowerCAmelCase       					=    []
										for split_name, files in data_files.items():
													if isinstance(__a					,     __a			):
																__lowerCAmelCase       					=    [files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													__lowerCAmelCase       					=    [dl_manager.iter_files(__a			) 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(__a			):
																			with open(__a					,     "rb"			) as f:
																						__lowerCAmelCase       					=    datasets.Features.from_arrow_schema(pq.read_schema(__a			)			)
																			break
													splits.append(datasets.SplitGenerator(name=__a					,     gen_kwargs={"files": files}			)			)
										return splits

							def     snake_case       (  self					,     __a			):
										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       					=    table_cast(__a					,     self.info.features.arrow_schema			)
										return pa_table



							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    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(__a			)			):
													with open(__a					,     "rb"			) as f:
																__lowerCAmelCase       					=    pq.ParquetFile(__a			)
																try:
																			for batch_idx, record_batch in enumerate(
																			    parquet_file.iter_batches(batch_size=self.config.batch_size					,     columns=self.config.columns			)			):
																						__lowerCAmelCase       					=    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(__a			)
																except ValueError as e:
																			logger.error(f"Failed to read file \'{file}\' with error {type(__a			)}: {e}"			)
																			raise








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




