

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_							:					Optional[Any]		 =   {
    '''configuration_altclip''': [
        '''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AltCLIPConfig''',
        '''AltCLIPTextConfig''',
        '''AltCLIPVisionConfig''',
    ],
    '''processing_altclip''': ['''AltCLIPProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''AltCLIPPreTrainedModel''',
			    '''AltCLIPModel''',
			    '''AltCLIPTextModel''',
			    '''AltCLIPVisionModel''',
			]


if TYPE_CHECKING:
			from .configuration_altclip import (
			    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    AltCLIPConfig,
			    AltCLIPTextConfig,
			    AltCLIPVisionConfig,
			)
			from .processing_altclip import AltCLIPProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_altclip import (
						    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    AltCLIPModel,
						    AltCLIPPreTrainedModel,
						    AltCLIPTextModel,
						    AltCLIPVisionModel,
						)


else:
			import sys

			UpperCAmelCase_							:					str		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCamelCase_						:     Union[str, Any]	       =							datasets.utils.logging.get_logger(__name__)




@dataclass
class   __lowercase						(						datasets.BuilderConfig       ):
	_A       =   None
	_A       =   "utf-8"
	_A       =   None
	_A       =   None
	_A       =   True  # deprecated
	_A       =   None  # deprecated
	_A       =   10 << 20  # 10MB
	_A       =   None




class   __lowercase						(						datasets.ArrowBasedBuilder       ):
	_A       =   JsonConfig






	def       _a(self				: List[Any]    ) ->			Dict:
							if self.config.block_size is not None:
													logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"    )
													_lowercase       :	Union[str, Any]           =   self.config.block_size
							if self.config.use_threads is not True:
													logger.warning(
													    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."    )
							if self.config.newlines_in_values is not None:
													raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"    )
							return datasets.DatasetInfo(features=self.config.features    )






	def       _a(self				: Any      ,	snake_case				: Union[str, Any]    ) ->			List[str]:
							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}"""    )
							_lowercase       :	str           =   dl_manager.download_and_extract(self.config.data_files    )
							if isinstance(snake_case      ,	(str, list, tuple)    ):
													_lowercase       :	int           =   data_files
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	Union[str, Any]           =   [files]
													_lowercase       :	Union[str, Any]           =   [dl_manager.iter_files(snake_case    ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,	gen_kwargs={"files": files}    )]
							_lowercase       :	Union[str, Any]           =   []
							for split_name, files in data_files.items():
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	List[Any]           =   [files]
													_lowercase       :	Optional[Any]           =   [dl_manager.iter_files(snake_case    ) for file in files]
													splits.append(datasets.SplitGenerator(name=snake_case      ,	gen_kwargs={"files": files}    )    )
							return splits






	def       _a(self				: List[Any]      ,	snake_case				: pa.Table    ) ->			pa.Table:
							if self.config.features is not None:
													# adding missing columns
													for column_name in set(self.config.features    ) - set(pa_table.column_names    ):
																			_lowercase       :	Any           =   self.config.features.arrow_schema.field(snake_case    ).type
																			_lowercase       :	Optional[Any]           =   pa_table.append_column(snake_case      ,	pa.array([None] * len(snake_case    )      ,	type=snake_case    )    )
													# more expensive cast to support nested structures with keys in a different order
													# allows str <-> int/float or str to Audio for example
													_lowercase       :	str           =   table_cast(snake_case      ,	self.config.features.arrow_schema    )
							return pa_table






	def       _a(self				: str      ,	snake_case				: Tuple    ) ->			Tuple:
							for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case    )    ):
													# If the file is one json object and if we need to look at the list of items in one specific field
													if self.config.field is not None:
																			with open(snake_case      ,	encoding=self.config.encoding      ,	errors=self.config.encoding_errors    ) as f:
																									_lowercase       :	Any           =   json.load(snake_case    )

																			# We keep only the field we are interested in
																			_lowercase       :	List[Any]           =   dataset[self.config.field]

																			# We accept two format: a list of dicts or a dict of lists
																			if isinstance(snake_case      ,	(list, tuple)    ):
																									_lowercase       :	Optional[int]           =   set().union(*[row.keys() for row in dataset]    )
																									_lowercase       :	Union[str, Any]           =   {col: [row.get(snake_case    ) for row in dataset] for col in keys}
																			else:
																									_lowercase       :	List[Any]           =   dataset
																			_lowercase       :	int           =   pa.Table.from_pydict(snake_case    )
																			yield file_idx, self._cast_table(snake_case    )

													# If the file has one json object per line
													else:
																			with open(snake_case      ,	"rb"    ) as f:
																									_lowercase       :	Union[str, Any]           =   0
																									# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																									# Set a default minimum value of 16kB if the chunk size is really small
																									_lowercase       :	Dict           =   max(self.config.chunksize // 32      ,	16 << 10    )
																									_lowercase       :	int           =   (
																									    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
																									)
																									while True:
																															_lowercase       :	Tuple           =   f.read(self.config.chunksize    )
																															if not batch:
																																					break
																															# Finish current line
																															try:
																																					batch += f.readline()
																															except (AttributeError, io.UnsupportedOperation):
																																					batch += readline(snake_case    )
																															# PyArrow only accepts utf-8 encoded bytes
																															if self.config.encoding != "utf-8":
																																					_lowercase       :	Union[str, Any]           =   batch.decode(self.config.encoding      ,	errors=snake_case    ).encode("utf-8"    )
																															try:
																																					while True:
																																											try:
																																																	_lowercase       :	Optional[Any]           =   paj.read_json(
																																																	    io.BytesIO(snake_case    )      ,	read_options=paj.ReadOptions(block_size=snake_case    )    )
																																																	break
																																											except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																	if (
																																																	    isinstance(snake_case      ,	pa.ArrowInvalid    )
																																																	    and "straddling" not in str(snake_case    )
																																																	    or block_size > len(snake_case    )
																																																	):
																																																							raise
																																																	else:
																																																							# Increase the block size in case it was too small.
																																																							# The block size will be reset for the next file.
																																																							logger.debug(
																																																							    F"""Batch of {len(snake_case    )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""    )
																																																							block_size *= 2
																															except pa.ArrowInvalid as e:
																																					try:
																																											with open(
																																											    snake_case      ,	encoding=self.config.encoding      ,	errors=self.config.encoding_errors    ) as f:
																																																	_lowercase       :	Any           =   json.load(snake_case    )
																																					except json.JSONDecodeError:
																																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																											raise e
																																					# If possible, parse the file as a list of json objects and exit the loop
																																					if isinstance(snake_case      ,	snake_case    ):  # list is the only sequence type supported in JSON
																																											try:
																																																	_lowercase       :	int           =   set().union(*[row.keys() for row in dataset]    )
																																																	_lowercase       :	Dict           =   {col: [row.get(snake_case    ) for row in dataset] for col in keys}
																																																	_lowercase       :	str           =   pa.Table.from_pydict(snake_case    )
																																											except (pa.ArrowInvalid, AttributeError) as e:
																																																	logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																																	raise ValueError(F"""Not able to read records in the JSON file at {file}."""    ) from None
																																											yield file_idx, self._cast_table(snake_case    )
																																											break
																																					else:
																																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																											raise ValueError(
																																											    F"""Not able to read records in the JSON file at {file}. """
																																											    F"""You should probably indicate the field of the JSON file containing your records. """
																																											    F"""This JSON file contain the following fields: {str(list(dataset.keys()    )    )}. """
																																											    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """    ) from None
                        # 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 (file_idx, batch_idx), self._cast_table(snake_case    )
																															batch_idx += 1




