

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        A_		(       datasets.BuilderConfig				):
							_UpperCAmelCase    :  int						      =      10_000
							_UpperCAmelCase    :  Optional[List[str]]						      =      None
							_UpperCAmelCase    :  Optional[datasets.Features]						      =      None






class        A_		(       datasets.ArrowBasedBuilder				):
							_UpperCAmelCase    :  int						      =      ParquetConfig
							def     lowerCAmelCase     (		self		:			List[str]):
								return datasets.DatasetInfo(features=self.config.features)
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Dict):
								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				:				Dict					       = dl_manager.download_and_extract(self.config.data_files)
								if isinstance(SCREAMING_SNAKE_CASE__   ,(str, list, tuple)):
									__lowerCamelCase				:				List[str]					       = data_files
									if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				List[str]					       = [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__lowerCamelCase				:				Optional[Any]					       = [dl_manager.iter_files(SCREAMING_SNAKE_CASE__) 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(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				Union[str, Any]					       = [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__lowerCamelCase				:				Union[str, Any]					       = [dl_manager.iter_files(SCREAMING_SNAKE_CASE__) 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(SCREAMING_SNAKE_CASE__):
											with open(SCREAMING_SNAKE_CASE__   ,'rb') as f:
												__lowerCamelCase				:				int					       = datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE__))
											break
									splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__   ,gen_kwargs={'files': files}))
								return splits
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			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				:				Optional[int]					       = table_cast(SCREAMING_SNAKE_CASE__   ,self.info.features.arrow_schema)
								return pa_table


							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								__lowerCamelCase				:				Union[str, Any]					       = 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(SCREAMING_SNAKE_CASE__)):
									with open(SCREAMING_SNAKE_CASE__   ,'rb') as f:
										__lowerCamelCase				:				Tuple					       = pq.ParquetFile(SCREAMING_SNAKE_CASE__)
										try:
											for batch_idx, record_batch in enumerate(
											    parquet_file.iter_batches(batch_size=self.config.batch_size   ,columns=self.config.columns)):
												__lowerCamelCase				:				Tuple					       = 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(SCREAMING_SNAKE_CASE__)
										except ValueError as e:
											logger.error(F"Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE__)}: {e}")
											raise






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



