


import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 	__lowercase    (datasets.BuilderConfig  ):





							"""simple docstring"""







							_UpperCAmelCase      =						None







class 	__lowercase    (datasets.ArrowBasedBuilder  ):





							"""simple docstring"""







							_UpperCAmelCase      =						PandasConfig






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return datasets.DatasetInfo(features=self.config.features       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										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}'''       )
										SCREAMING_SNAKE_CASE_		:     str					=					dl_manager.download_and_extract(self.config.data_files       )
										if isinstance(lowerCAmelCase__			,						(str, list, tuple)       ):
													SCREAMING_SNAKE_CASE_		:     int					=					data_files
													if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
																SCREAMING_SNAKE_CASE_		:     int					=					[files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[dl_manager.iter_files(lowerCAmelCase__       ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,						gen_kwargs={'files': files}       )]
										SCREAMING_SNAKE_CASE_		:     Dict					=					[]
										for split_name, files in data_files.items():
													if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
																SCREAMING_SNAKE_CASE_		:     str					=					[files]
													# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[dl_manager.iter_files(lowerCAmelCase__       ) for file in files]
													splits.append(datasets.SplitGenerator(name=lowerCAmelCase__			,						gen_kwargs={'files': files}       )       )
										return splits






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if self.config.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
													SCREAMING_SNAKE_CASE_		:     List[str]					=					table_cast(lowerCAmelCase__			,						self.config.features.arrow_schema       )
										return pa_table






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										for i, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__       )       ):
													with open(lowerCAmelCase__			,						'rb'       ) as f:
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pa.Table.from_pandas(pd.read_pickle(lowerCAmelCase__       )       )
																yield i, self._cast_table(lowerCAmelCase__       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      Union[str, Any]								={
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Dict								=[
				    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Blip2Model',
				    'Blip2QFormerModel',
				    'Blip2PreTrainedModel',
				    'Blip2ForConditionalGeneration',
				    'Blip2VisionModel',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

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





