


"""simple docstring"""





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     LayoutLMForMaskedLM,
     LayoutLMForQuestionAnswering,
     LayoutLMForSequenceClassification,
     LayoutLMForTokenClassification,
     LayoutLMModel,
 )



class 		snake_case_:






     def __init__(						self	:       Optional[Any]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       str=1_3       ,					UpperCamelCase_	:       Optional[Any]=7       ,					UpperCamelCase_	:       str=True       ,					UpperCamelCase_	:       Tuple=True       ,					UpperCamelCase_	:       Any=True       ,					UpperCamelCase_	:       Optional[Any]=True       ,					UpperCamelCase_	:       str=9_9       ,					UpperCamelCase_	:       Any=3_2       ,					UpperCamelCase_	:       Optional[int]=5       ,					UpperCamelCase_	:       Optional[Any]=4       ,					UpperCamelCase_	:       int=3_7       ,					UpperCamelCase_	:       str="gelu"       ,					UpperCamelCase_	:       List[Any]=0.1       ,					UpperCamelCase_	:       Optional[int]=0.1       ,					UpperCamelCase_	:       Any=5_1_2       ,					UpperCamelCase_	:       int=1_6       ,					UpperCamelCase_	:       Dict=2       ,					UpperCamelCase_	:       Any=0.02       ,					UpperCamelCase_	:       Dict=3       ,					UpperCamelCase_	:       Union[str, Any]=4       ,					UpperCamelCase_	:       Dict=None       ,					UpperCamelCase_	:       List[str]=1_0_0_0       ,					):
           lowerCAmelCase       :						Optional[int]							= parent
           lowerCAmelCase       :						int							= batch_size
           lowerCAmelCase       :						Tuple							= seq_length
           lowerCAmelCase       :						Union[str, Any]							= is_training
           lowerCAmelCase       :						int							= use_input_mask
           lowerCAmelCase       :						Dict							= use_token_type_ids
           lowerCAmelCase       :						int							= use_labels
           lowerCAmelCase       :						str							= vocab_size
           lowerCAmelCase       :						List[str]							= hidden_size
           lowerCAmelCase       :						List[str]							= num_hidden_layers
           lowerCAmelCase       :						str							= num_attention_heads
           lowerCAmelCase       :						Any							= intermediate_size
           lowerCAmelCase       :						Dict							= hidden_act
           lowerCAmelCase       :						Union[str, Any]							= hidden_dropout_prob
           lowerCAmelCase       :						Optional[Any]							= attention_probs_dropout_prob
           lowerCAmelCase       :						List[str]							= max_position_embeddings
           lowerCAmelCase       :						str							= type_vocab_size
           lowerCAmelCase       :						Any							= type_sequence_label_size
           lowerCAmelCase       :						List[str]							= initializer_range
           lowerCAmelCase       :						Union[str, Any]							= num_labels
           lowerCAmelCase       :						Any							= num_choices
           lowerCAmelCase       :						Optional[int]							= scope
           lowerCAmelCase       :						Tuple							= range_bbox






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase       :						str							= ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size					)

           lowerCAmelCase       :						Optional[Any]							= ids_tensor([self.batch_size, self.seq_length, 4]       ,					self.range_bbox					)
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]					):
                 for j in range(bbox.shape[1]					):
                       if bbox[i, j, 3] < bbox[i, j, 1]:
                             lowerCAmelCase       :						int							= bbox[i, j, 3]
                             lowerCAmelCase       :						List[str]							= bbox[i, j, 1]
                             lowerCAmelCase       :						Any							= t
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             lowerCAmelCase       :						Any							= bbox[i, j, 2]
                             lowerCAmelCase       :						List[str]							= bbox[i, j, 0]
                             lowerCAmelCase       :						str							= t

           lowerCAmelCase       :						Union[str, Any]							= None
           if self.use_input_mask:
                 lowerCAmelCase       :						Optional[Any]							= ids_tensor([self.batch_size, self.seq_length]       ,					vocab_size=2					)

           lowerCAmelCase       :						Optional[int]							= None
           if self.use_token_type_ids:
                 lowerCAmelCase       :						List[Any]							= ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size					)

           lowerCAmelCase       :						List[Any]							= None
           lowerCAmelCase       :						List[str]							= None
           lowerCAmelCase       :						Union[str, Any]							= None
           if self.use_labels:
                 lowerCAmelCase       :						List[str]							= ids_tensor([self.batch_size]       ,					self.type_sequence_label_size					)
                 lowerCAmelCase       :						List[Any]							= ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels					)
                 lowerCAmelCase       :						int							= ids_tensor([self.batch_size]       ,					self.num_choices					)

           lowerCAmelCase       :						Union[str, Any]							= self.get_config()

           return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






     def   lowerCamelCase__  (						self	:       Any					):
           return LayoutLMConfig(
               vocab_size=self.vocab_size       ,					hidden_size=self.hidden_size       ,					num_hidden_layers=self.num_hidden_layers       ,					num_attention_heads=self.num_attention_heads       ,					intermediate_size=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					max_position_embeddings=self.max_position_embeddings       ,					type_vocab_size=self.type_vocab_size       ,					initializer_range=self.initializer_range       ,					)






     def   lowerCamelCase__  (						self	:       List[str]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Any					):
           lowerCAmelCase       :						List[str]							= LayoutLMModel(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Dict							= model(__snake_case       ,					__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case					)
           lowerCAmelCase       :						Tuple							= model(__snake_case       ,					__snake_case       ,					token_type_ids=__snake_case					)
           lowerCAmelCase       :						Tuple							= model(__snake_case       ,					__snake_case					)
           self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)					)
           self.parent.assertEqual(result.pooler_output.shape       ,					(self.batch_size, self.hidden_size)					)






     def   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Dict       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Optional[Any]					):
           lowerCAmelCase       :						List[Any]							= LayoutLMForMaskedLM(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						List[Any]							= model(__snake_case       ,					__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					labels=__snake_case					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)					)






     def   lowerCamelCase__  (						self	:       Optional[int]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       Tuple					):
           lowerCAmelCase       :						List[str]							= self.num_labels
           lowerCAmelCase       :						Dict							= LayoutLMForSequenceClassification(__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Dict							= model(
               __snake_case       ,					__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					labels=__snake_case					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)					)






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       List[Any]       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Tuple					):
           lowerCAmelCase       :						Union[str, Any]							= self.num_labels
           lowerCAmelCase       :						Any							= LayoutLMForTokenClassification(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Tuple							= model(__snake_case       ,					__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					labels=__snake_case					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels)					)






     def   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						Optional[int]							= LayoutLMForQuestionAnswering(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Tuple							= model(
               __snake_case       ,					bbox=__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					start_positions=__snake_case       ,					end_positions=__snake_case       ,					)
           self.parent.assertEqual(result.start_logits.shape       ,					(self.batch_size, self.seq_length)					)
           self.parent.assertEqual(result.end_logits.shape       ,					(self.batch_size, self.seq_length)					)






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase       :						List[Any]							= self.prepare_config_and_inputs()
           (
               lowerCAmelCase    
           )       :						Optional[Any]							= config_and_inputs
           lowerCAmelCase       :						str							= {
               'input_ids': input_ids,
               'bbox': bbox,
               'token_type_ids': token_type_ids,
               'attention_mask': input_mask,
           }
           return config, inputs_dict



@require_torch
class 		snake_case_(							__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase      ):
     __UpperCamelCase	=       (
         (
             LayoutLMModel,
             LayoutLMForMaskedLM,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
             LayoutLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     __UpperCamelCase	=       (
         {
             '''document-question-answering''': LayoutLMForQuestionAnswering,
             '''feature-extraction''': LayoutLMModel,
             '''fill-mask''': LayoutLMForMaskedLM,
             '''text-classification''': LayoutLMForSequenceClassification,
             '''token-classification''': LayoutLMForTokenClassification,
             '''zero-shot''': LayoutLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __UpperCamelCase	=       True






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						Union[str, Any]							= LayoutLMModelTester(self					)
           lowerCAmelCase       :						List[str]							= ConfigTester(self       ,					config_class=__snake_case       ,					hidden_size=3_7					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           self.config_tester.run_common_tests()






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						List[str]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__snake_case					)






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						Tuple							= self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCAmelCase       :						Union[str, Any]							= type
                 self.model_tester.create_and_check_model(*__snake_case					)






     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						Optional[int]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__snake_case					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						Optional[int]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__snake_case					)






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						Tuple							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__snake_case					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						List[Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__snake_case					)






     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
     def   lowerCamelCase__  (						self	:       int					):
           pass



def   _snake_case    (    ):
      # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
      # fmt: off
      lowerCAmelCase       :						Optional[Any]							= torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]			, device=lowercase__      )  # noqa: E231
      lowerCAmelCase       :						Dict							= torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]			, device=lowercase__      )  # noqa: E231
      lowerCAmelCase       :						List[str]							= torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]			, device=lowercase__      )  # noqa: E231
      lowerCAmelCase       :						Optional[Any]							= torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]			, device=lowercase__      )  # noqa: E231
      # these are sequence labels (i.e. at the token level)
      lowerCAmelCase       :						int							= torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]			, device=lowercase__      )  # noqa: E231
      # fmt: on

      return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class 		snake_case_(							unittest.TestCase      ):






     @slow
     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						List[str]							= LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''					).to(__snake_case					)

           lowerCAmelCase       :						List[Any]							= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase       :						str							= model(input_ids=__snake_case       ,					bbox=__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case					)

           # test the sequence output on [0, :3, :3]
           lowerCAmelCase       :						Dict							= torch.tensor(
               [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]       ,					device=__snake_case       ,					)

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,					__snake_case       ,					atol=1E-3					)					)

           # test the pooled output on [1, :3]
           lowerCAmelCase       :						List[str]							= torch.tensor([-0.6_580, -0.0_214, 0.8_552]       ,					device=__snake_case					)

           self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]       ,					__snake_case       ,					atol=1E-3					)					)






     @slow
     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						List[str]							= LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''       ,					num_labels=2					).to(
               __snake_case					)

           lowerCAmelCase       :						Tuple							= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase       :						Any							= model(
               input_ids=__snake_case       ,					bbox=__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					labels=torch.tensor([1, 1]       ,					device=__snake_case					)       ,					)

           # test whether we get a loss as a scalar
           lowerCAmelCase       :						Optional[int]							= outputs.loss
           lowerCAmelCase       :						Optional[int]							= torch.Size([]					)
           self.assertEqual(loss.shape       ,					__snake_case					)

           # test the shape of the logits
           lowerCAmelCase       :						List[str]							= outputs.logits
           lowerCAmelCase       :						Tuple							= torch.Size((2, 2)					)
           self.assertEqual(logits.shape       ,					__snake_case					)






     @slow
     def   lowerCamelCase__  (						self	:       str					):
           lowerCAmelCase       :						Union[str, Any]							= LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''       ,					num_labels=1_3					).to(
               __snake_case					)

           lowerCAmelCase       :						List[str]							= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase       :						str							= model(
               input_ids=__snake_case       ,					bbox=__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case       ,					labels=__snake_case					)

           # test the loss calculation to be around 2.65
           # expected_loss = torch.tensor(2.65, device=torch_device)

           # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
           # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

           # test the shape of the logits
           lowerCAmelCase       :						Optional[int]							= outputs.logits
           lowerCAmelCase       :						Optional[Any]							= torch.Size((2, 2_5, 1_3)					)
           self.assertEqual(logits.shape       ,					__snake_case					)






     @slow
     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase       :						List[Any]							= LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''					).to(__snake_case					)

           lowerCAmelCase       :						str							= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase       :						Any							= model(input_ids=__snake_case       ,					bbox=__snake_case       ,					attention_mask=__snake_case       ,					token_type_ids=__snake_case					)

           # test the shape of the logits
           lowerCAmelCase       :						Tuple							= torch.Size((2, 2_5)					)
           self.assertEqual(outputs.start_logits.shape       ,					__snake_case					)
           self.assertEqual(outputs.end_logits.shape       ,					__snake_case					)









'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		query_dim

											return {
											    "query_dim": query_dim,
											    "cross_attention_dim": cross_attention_dim,
											    "heads": 2,
											    "dim_head": 4,
											    "added_kv_proj_dim": 6,
											    "norm_num_groups": 1,
											    "only_cross_attention": only_cross_attention,
											    "processor": AttnAddedKVProcessor(),
											}


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

											return {
											    "hidden_states": hidden_states,
											    "encoder_hidden_states": encoder_hidden_states,
											    "attention_mask": attention_mask,
											}




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is not None							)
											self.assertTrue(attn.to_v is not None							)

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is None							)
											self.assertTrue(attn.to_v is None							)

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

											_snake_case: int	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											# the initial conversion succeeds
											_snake_case: Dict	       =		DiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Any	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)
											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)





