

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

@dataclass
class 				UpperCAmelCase_		(			datasets.BuilderConfig   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    10000
							UpperCAmelCase__       :						Optional[List[str]]	    =    None
							UpperCAmelCase__       :						Optional[datasets.Features]	    =    None



class 				UpperCAmelCase_		(			datasets.ArrowBasedBuilder   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    ParquetConfig


							def 				__lowercase				(			self       )    ->    Any:
												return datasets.DatasetInfo(features=self.config.features       )


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


							def 				__lowercase				(			self       ,							_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
																	_a	:    Optional[Any]         =		table_cast(_a       ,							self.info.features.arrow_schema       )
												return pa_table




							def 				__lowercase				(			self       ,							_a       )    ->    Union[str, Any]:
												_a	:    Tuple         =		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:
																						_a	:    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       )       ):
																																_a	:    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






import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		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       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		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       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



