from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


_snake_case			=						AttentionProcessor




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				List[Any]	,  *__lowerCamelCase:				Any	,  **__lowerCamelCase:				List[str]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			int         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Tuple	,  *__lowerCamelCase:				List[str]	,  **__lowerCamelCase:				int     )    ->				Tuple:
												__UpperCAmelCase						:			Tuple         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Union[str, Any]	,  *__lowerCamelCase:				Optional[int]	,  **__lowerCamelCase:				Optional[Any]     )    ->				int:
												__UpperCAmelCase						:			int         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Optional[Any]	,  *__lowerCamelCase:				List[str]	,  **__lowerCamelCase:				int     )    ->				Dict:
												__UpperCAmelCase						:			List[Any]         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Optional[int]	,  *__lowerCamelCase:				Dict	,  **__lowerCamelCase:				Optional[int]     )    ->				Optional[int]:
												__UpperCAmelCase						:			Any         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Optional[int]	,  *__lowerCamelCase:				Tuple	,  **__lowerCamelCase:				Union[str, Any]     )    ->				Tuple:
												__UpperCAmelCase						:			List[Any]         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				Tuple	,  *__lowerCamelCase:				List[str]	,  **__lowerCamelCase:				Tuple     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Dict         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




class 					_snake_case						(						_lowercase				):
					def __init__(  self:				str	,  *__lowerCamelCase:				Tuple	,  **__lowerCamelCase:				str     )    ->				Tuple:
												__UpperCAmelCase						:			List[Any]         =   f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'     )     )} instead.'''
												deprecate("cross_attention"	,  "0.20.0"	,  __lowerCamelCase	,  standard_warn=__lowerCamelCase     )
												super().__init__(*__lowerCamelCase	,  **__lowerCamelCase     )




import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


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




@dataclass
class 					_snake_case						(						datasets.BuilderConfig				):
					lowerCamelCase__:		Optional[datasets.Features]  =     None




class 					_snake_case						(						datasets.ArrowBasedBuilder				):
					lowerCamelCase__:		Union[str, Any]  =     ArrowConfig
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												return datasets.DatasetInfo(features=self.config.features     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				Tuple     )    ->				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}'''     )
												__UpperCAmelCase						:			Dict         =   dl_manager.download_and_extract(self.config.data_files     )
												if isinstance(__lowerCamelCase	,  (str, list, tuple)     ):
																			__UpperCAmelCase						:			str         =   data_files
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			List[str]         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) for file in files]
																			return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"files": files}     )]
												__UpperCAmelCase						:			Union[str, Any]         =   []
												for split_name, files in data_files.items():
																			if isinstance(__lowerCamelCase	,  __lowerCamelCase     ):
																										__UpperCAmelCase						:			Tuple         =   [files]
																			# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																			__UpperCAmelCase						:			List[Any]         =   [dl_manager.iter_files(__lowerCamelCase     ) 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(__lowerCamelCase     ):
																																	with open(__lowerCamelCase	,  "rb"     ) as f:
																																								__UpperCAmelCase						:			Union[str, Any]         =   datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowerCamelCase     ).schema     )
																																	break
																			splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,  gen_kwargs={"files": files}     )     )
												return splits
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				pa.Table     )    ->				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
																			__UpperCAmelCase						:			List[str]         =   table_cast(__lowerCamelCase	,  self.info.features.arrow_schema     )
												return pa_table





					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Any     )    ->				Any:
												for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase     )     ):
																			with open(__lowerCamelCase	,  "rb"     ) as f:
																										try:
																																	for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowerCamelCase     )     ):
																																								__UpperCAmelCase						:			Optional[int]         =   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(__lowerCamelCase     )
																										except ValueError as e:
																																	logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase     )}: {e}'''     )
																																	raise




