
import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import DonutSwinModel
					from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST

class      lowerCAmelCase__  :






		def __init__(	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int=13							,    SCREAMING_SNAKE_CASE__	:							List[Any]=32							,    SCREAMING_SNAKE_CASE__	:							str=2							,    SCREAMING_SNAKE_CASE__	:							int=3							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=16							,    SCREAMING_SNAKE_CASE__	:							Dict=[1, 2, 1]							,    SCREAMING_SNAKE_CASE__	:							Any=[2, 2, 4]							,    SCREAMING_SNAKE_CASE__	:							Tuple=2							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=2.0							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=True							,    SCREAMING_SNAKE_CASE__	:							Any=0.0							,    SCREAMING_SNAKE_CASE__	:							Any=0.0							,    SCREAMING_SNAKE_CASE__	:							Any=0.1							,    SCREAMING_SNAKE_CASE__	:							Dict="gelu"							,    SCREAMING_SNAKE_CASE__	:							Tuple=False							,    SCREAMING_SNAKE_CASE__	:							List[Any]=True							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=0.02							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=1e-5							,    SCREAMING_SNAKE_CASE__	:							Any=True							,    SCREAMING_SNAKE_CASE__	:							List[str]=None							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=True							,    SCREAMING_SNAKE_CASE__	:							Dict=10							,    SCREAMING_SNAKE_CASE__	:							Dict=8							,    )  ->							List[str]:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			image_size
						__lowerCamelCase					      =			patch_size
						__lowerCamelCase					      =			num_channels
						__lowerCamelCase					      =			embed_dim
						__lowerCamelCase					      =			depths
						__lowerCamelCase					      =			num_heads
						__lowerCamelCase					      =			window_size
						__lowerCamelCase					      =			mlp_ratio
						__lowerCamelCase					      =			qkv_bias
						__lowerCamelCase					      =			hidden_dropout_prob
						__lowerCamelCase					      =			attention_probs_dropout_prob
						__lowerCamelCase					      =			drop_path_rate
						__lowerCamelCase					      =			hidden_act
						__lowerCamelCase					      =			use_absolute_embeddings
						__lowerCamelCase					      =			patch_norm
						__lowerCamelCase					      =			layer_norm_eps
						__lowerCamelCase					      =			initializer_range
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			scope
						__lowerCamelCase					      =			use_labels
						__lowerCamelCase					      =			type_sequence_label_size
						__lowerCamelCase					      =			encoder_stride






		def 				__A (	self	:							Optional[int]						)  ->							List[str]:
						__lowerCamelCase					      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						__lowerCamelCase					      =			None
						if self.use_labels:
										__lowerCamelCase					      =			ids_tensor([self.batch_size]							,    self.type_sequence_label_size						)

						__lowerCamelCase					      =			self.get_config()

						return config, pixel_values, labels






		def 				__A (	self	:							Dict						)  ->							int:
						return DonutSwinConfig(
						    image_size=self.image_size							,    patch_size=self.patch_size							,    num_channels=self.num_channels							,    embed_dim=self.embed_dim							,    depths=self.depths							,    num_heads=self.num_heads							,    window_size=self.window_size							,    mlp_ratio=self.mlp_ratio							,    qkv_bias=self.qkv_bias							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    drop_path_rate=self.drop_path_rate							,    hidden_act=self.hidden_act							,    use_absolute_embeddings=self.use_absolute_embeddings							,    path_norm=self.patch_norm							,    layer_norm_eps=self.layer_norm_eps							,    initializer_range=self.initializer_range							,    encoder_stride=self.encoder_stride							,    )






		def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							List[Any]						)  ->							int:
						__lowerCamelCase					      =			DonutSwinModel(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
						__lowerCamelCase					      =			int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

						self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, expected_seq_len, expected_dim)						)







		def 				__A (	self	:							Union[str, Any]						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						(
						    (
						    __lowerCamelCase
						)      ,					(
						    __lowerCamelCase
						)      ,					(
						    __lowerCamelCase
						)      ,					
						)					      =			config_and_inputs
						__lowerCamelCase					      =			{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_torch
class      lowerCAmelCase__  (			__lowercase						,			__lowercase						,			unittest.TestCase    ):
		a__ :		Optional[Any]   						=	(DonutSwinModel,) if is_torch_available() else ()
		a__ :		Dict   						=	{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
		a__ :		Optional[int]   						=	True

		a__ :		Optional[int]   						=	False
		a__ :		Dict   						=	False
		a__ :		List[str]   						=	False






		def 				__A (	self	:							str						)  ->							Optional[Any]:
						__lowerCamelCase					      =			DonutSwinModelTester(self						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__							,    embed_dim=37						)






		def 				__A (	self	:							Union[str, Any]						)  ->							List[Any]:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()






		def 				__A (	self	:							Union[str, Any]						)  ->							Dict:
						return






		def 				__A (	self	:							Union[str, Any]						)  ->							Optional[Any]:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Optional[int]						)  ->							Tuple:
						# DonutSwin does not use inputs_embeds
						pass






		def 				__A (	self	:							Optional[int]						)  ->							Optional[int]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertIsInstance(model.get_input_embeddings()							,    (nn.Module)						)
										__lowerCamelCase					      =			model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__							,    nn.Linear						)						)






		def 				__A (	self	:							List[Any]						)  ->							str:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			inspect.signature(model.forward						)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										__lowerCamelCase					      =			[*signature.parameters.keys()]

										__lowerCamelCase					      =			['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[str]						)  ->							Any:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCamelCase					      =			True

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			False
										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)
										__lowerCamelCase					      =			outputs.attentions
										__lowerCamelCase					      =			len(self.model_tester.depths						)
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			config.window_size**2
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)
										__lowerCamelCase					      =			outputs.attentions
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(attentions[0].shape[-3:]						)							,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,    )
										__lowerCamelCase					      =			len(SCREAMING_SNAKE_CASE__						)

										# Check attention is always last and order is fine
										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()
										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

										if hasattr(self.model_tester							,    '''num_hidden_states_types'''						):
														__lowerCamelCase					      =			self.model_tester.num_hidden_states_types
										else:
														# also another +1 for reshaped_hidden_states
														__lowerCamelCase					      =			2
										self.assertEqual(out_len + added_hidden_states							,    len(SCREAMING_SNAKE_CASE__						)						)

										__lowerCamelCase					      =			outputs.attentions

										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]						)							,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,    )






		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							str							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							int:
						__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()

						with torch.no_grad():
										__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

						__lowerCamelCase					      =			outputs.hidden_states

						__lowerCamelCase					      =			getattr(
						    self.model_tester							,    '''expected_num_hidden_layers'''							,    len(self.model_tester.depths						) + 1						)
						self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

						# DonutSwin has a different seq_length
						__lowerCamelCase					      =			(
						    config.patch_size
						    if isinstance(config.patch_size							,    collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

						__lowerCamelCase					      =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]						)							,    [num_patches, self.model_tester.embed_dim]							,    )

						__lowerCamelCase					      =			outputs.reshaped_hidden_states
						self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			reshaped_hidden_states[0].shape
						__lowerCamelCase					      =			(
						    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    height * width						).permute(0							,    2							,    1						)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]						)							,    [num_patches, self.model_tester.embed_dim]							,    )






		def 				__A (	self	:							int						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size							,    collections.abc.Iterable						)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCamelCase					      =			True

										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							str						)  ->							Any:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCamelCase					      =			3

						__lowerCamelCase					      =			(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size							,    collections.abc.Iterable						)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						__lowerCamelCase					      =			(
						    config.patch_size
						    if isinstance(config.patch_size							,    collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

						__lowerCamelCase					      =			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						__lowerCamelCase					      =			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    (padded_height, padded_width)						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCamelCase					      =			True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    (padded_height, padded_width)						)






		@slow
		def 				__A (	self	:							int						)  ->							Optional[Any]:
						for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__lowerCamelCase					      =			DonutSwinModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							Any						)  ->							Optional[int]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			_config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(config=SCREAMING_SNAKE_CASE__						)
										for name, param in model.named_parameters():
														if "embeddings" not in name and param.requires_grad:
																		self.assertIn(
																		    ((param.data.mean() * 1e9).round() / 1e9).item()							,    [0.0, 1.0]							,    msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,    )





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,
)


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AttentionProcessor

class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							List[Any]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							Optional[Any]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							List[Any]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[int]							,    *SCREAMING_SNAKE_CASE__	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							int							,    **SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							List[str]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Tuple							,    **SCREAMING_SNAKE_CASE__	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							int:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							Any							,    **SCREAMING_SNAKE_CASE__	:							str						)  ->							Dict:
						__lowerCamelCase					      =			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'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)



