
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,
)


A  :			Optional[Any]     				=				AttentionProcessor







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   str:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )







class     __A(				a    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					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'''	,  _snake_case	,  standard_warn=_snake_case       )
								super().__init__(*_snake_case	,  **_snake_case       )








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch