






"""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__          = datasets.utils.logging.get_logger(__name__)


@dataclass
class 		__lowerCamelCase				( datasets.BuilderConfig			):





		'''simple docstring'''






		a_	:				int            =							1_0000
		a_	:				Optional[List[str]]            =							None
		a_	:				Optional[datasets.Features]            =							None







class 		__lowerCamelCase				( datasets.ArrowBasedBuilder			):





		'''simple docstring'''






		a_	:				Dict            =							ParquetConfig





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				return datasets.DatasetInfo(features=self.config.features	)





		def    lowerCamelCase       (   self							:	Any						,			a_							:	Optional[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_   :							List[str]									=			dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a_						,			(str, list, tuple)	):
						lowerCAmelCase_   :							str									=			data_files
						if isinstance(a_						,			a_	):
								lowerCAmelCase_   :							Optional[Any]									=			[files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						lowerCAmelCase_   :							str									=			[dl_manager.iter_files(a_	) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={"files": files}	)]
				lowerCAmelCase_   :							Optional[Any]									=			[]
				for split_name, files in data_files.items():
						if isinstance(a_						,			a_	):
								lowerCAmelCase_   :							Union[str, Any]									=			[files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						lowerCAmelCase_   :							List[str]									=			[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_   :							Dict									=			datasets.Features.from_arrow_schema(pq.read_schema(a_	)	)
										break
						splits.append(datasets.SplitGenerator(name=a_						,			gen_kwargs={"files": files}	)	)
				return splits





		def    lowerCamelCase       (   self							:	int						,			a_							:	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_   :							Tuple									=			table_cast(a_						,			self.info.features.arrow_schema	)
				return pa_table





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Dict	):
				lowerCAmelCase_   :							Optional[int]									=			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_   :							Any									=			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_   :							List[str]									=			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"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				str            =							["""image_processor""", """tokenizer"""]
		a_	:				List[str]            =							"""ViTImageProcessor"""
		a_	:				List[str]            =							("""CLIPTokenizer""", """CLIPTokenizerFast""")





		def __init__(   self							:	List[str]						,			a_							:	str=None						,			a_							:	Dict=None						,			**a_							:	List[Any]	):
				lowerCAmelCase_   :							int									=			None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
						    " instead."						,			a_						,			)
						lowerCAmelCase_   :							Union[str, Any]									=			kwargs.pop("feature_extractor"	)

				lowerCAmelCase_   :							Dict									=			image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError("You need to specify an `image_processor`."	)
				if tokenizer is None:
						raise ValueError("You need to specify a `tokenizer`."	)

				super().__init__(a_						,			a_	)





		def __call__(   self							:	Union[str, Any]						,			a_							:	Any=None						,			a_							:	Dict=None						,			a_							:	List[str]=None						,			a_							:	str=None						,			**a_							:	Any	):
				if text is None and visual_prompt is None and images is None:
						raise ValueError("You have to specify either text, visual prompt or images."	)

				if text is not None and visual_prompt is not None:
						raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."	)

				if text is not None:
						lowerCAmelCase_   :							Optional[Any]									=			self.tokenizer(a_						,			return_tensors=a_						,			**a_	)

				if visual_prompt is not None:
						lowerCAmelCase_   :							Optional[Any]									=			self.image_processor(a_						,			return_tensors=a_						,			**a_	)

				if images is not None:
						lowerCAmelCase_   :							List[str]									=			self.image_processor(a_						,			return_tensors=a_						,			**a_	)

				if visual_prompt is not None and images is not None:
						lowerCAmelCase_   :							Union[str, Any]									=			{
						    "pixel_values": image_features.pixel_values,
						    "conditional_pixel_values": prompt_features.pixel_values,
						}
						return encoding
				elif text is not None and images is not None:
						lowerCAmelCase_   :							Optional[int]									=			image_features.pixel_values
						return encoding
				elif text is not None:
						return encoding
				elif visual_prompt is not None:
						lowerCAmelCase_   :							Dict									=			{
						    "conditional_pixel_values": prompt_features.pixel_values,
						}
						return encoding
				else:
						return BatchEncoding(data=dict(**a_	)						,			tensor_type=a_	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			*a_							:	Optional[Any]						,			**a_							:	List[str]	):
				return self.tokenizer.batch_decode(*a_						,			**a_	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			*a_							:	Tuple						,			**a_							:	Tuple	):
				return self.tokenizer.decode(*a_						,			**a_	)





		@property
		def    lowerCamelCase       (   self							:	List[Any]	):
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,			a_						,			)
				return self.image_processor_class





		@property
		def    lowerCamelCase       (   self							:	Dict	):
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,			a_						,			)
				return self.image_processor
