


"""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


A  :     List[Any]    					=    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		:					Optional[Any]          =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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A  :     Union[str, Any]    					=    {
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A  :     Optional[int]    					=    [
	    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XCLIPModel",
	    "XCLIPPreTrainedModel",
	    "XCLIPTextModel",
	    "XCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	A  :     Union[str, Any]    					=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




