

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.layoutlm.modeling_tf_layoutlm import (
	    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFLayoutLMForMaskedLM,
	    TFLayoutLMForQuestionAnswering,
	    TFLayoutLMForSequenceClassification,
	    TFLayoutLMForTokenClassification,
	    TFLayoutLMModel,
	)



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=2  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=3  ,      _lowercase=4  ,      _lowercase=None  ,      _lowercase=1_000  ,      )     -> List[Any]:
									a_						:						Dict 							= parent
									a_						:						Tuple 							= batch_size
									a_						:						Union[str, Any] 							= seq_length
									a_						:						Any 							= is_training
									a_						:						List[Any] 							= use_input_mask
									a_						:						int 							= use_token_type_ids
									a_						:						Tuple 							= use_labels
									a_						:						str 							= vocab_size
									a_						:						List[Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						List[str] 							= num_attention_heads
									a_						:						Optional[int] 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						List[Any] 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						str 							= max_position_embeddings
									a_						:						List[str] 							= type_vocab_size
									a_						:						List[Any] 							= type_sequence_label_size
									a_						:						Tuple 							= initializer_range
									a_						:						str 							= num_labels
									a_						:						Optional[Any] 							= num_choices
									a_						:						Any 							= scope
									a_						:						str 							= range_bbox





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						str 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									# convert bbox to numpy since TF does not support item assignment
									a_						:						Optional[Any] 							= ids_tensor([self.batch_size, self.seq_length, 4]  ,      self.range_bbox    ).numpy()
									# 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]:
																		a_						:						Any 							= bbox[i, j, 3]
																		a_						:						List[Any] 							= bbox[i, j, 1]
																		a_						:						Any 							= t
															if bbox[i, j, 2] < bbox[i, j, 0]:
																		a_						:						Tuple 							= bbox[i, j, 2]
																		a_						:						Optional[int] 							= bbox[i, j, 0]
																		a_						:						int 							= t
									a_						:						List[Any] 							= tf.convert_to_tensor(_lowercase    )

									a_						:						Tuple 							= None
									if self.use_input_mask:
												a_						:						Optional[int] 							= random_attention_mask([self.batch_size, self.seq_length]    )

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

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

									a_						:						Dict 							= 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  ,      )

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





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Tuple:
									a_						:						Tuple 							= TFLayoutLMModel(config=_lowercase    )

									a_						:						Any 							= model(_lowercase  ,      _lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase    )
									a_						:						List[Any] 							= model(_lowercase  ,      _lowercase  ,      token_type_ids=_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase  ,      _lowercase    )
									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 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Dict:
									a_						:						List[str] 							= TFLayoutLMForMaskedLM(config=_lowercase    )

									a_						:						Optional[Any] 							= model(_lowercase  ,      _lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      labels=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Optional[int]:
									a_						:						Optional[int] 							= self.num_labels
									a_						:						Optional[int] 							= TFLayoutLMForSequenceClassification(config=_lowercase    )

									a_						:						Any 							= model(_lowercase  ,      _lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> List[str]:
									a_						:						Optional[Any] 							= self.num_labels
									a_						:						List[str] 							= TFLayoutLMForTokenClassification(config=_lowercase    )

									a_						:						Union[str, Any] 							= model(_lowercase  ,      _lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      labels=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> List[str]:
									a_						:						Union[str, Any] 							= TFLayoutLMForQuestionAnswering(config=_lowercase    )

									a_						:						Any 							= model(_lowercase  ,      _lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase    )
									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 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Any 							= self.prepare_config_and_inputs()
									(
									    (
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					(
									    a_       
									)							,					       
									)						:						List[str] 							= config_and_inputs
									a_						:						List[str] 							= {
									    """input_ids""": input_ids,
									    """bbox""": bbox,
									    """token_type_ids""": token_type_ids,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict







@require_tf
class        A__(_a,  _a,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				str       					=			(
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_A    :				str       					=			(
						    {
						        '''feature-extraction''': TFLayoutLMModel,
						        '''fill-mask''': TFLayoutLMForMaskedLM,
						        '''text-classification''': TFLayoutLMForSequenceClassification,
						        '''token-classification''': TFLayoutLMForTokenClassification,
						        '''zero-shot''': TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_A    :				Optional[Any]       					=			False
						_A    :				Optional[Any]       					=			True
						_A    :				Dict       					=			10





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Any 							= TFLayoutLMModelTester(self    )
									a_						:						int 							= ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						List[str] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						List[str] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_lowercase    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:

									for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a_						:						int 							= TFLayoutLMModel.from_pretrained(_lowercase    )
												self.assertIsNotNone(_lowercase    )





						@unittest.skip("""Onnx compliancy broke with TF 2.10"""    )
						def 			UpperCamelCase__					(    self    )     -> Dict:
									pass







def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]])  # noqa: E231
			a_						:						Tuple 							= tf.convert_to_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],])  # noqa: E231
			a_						:						Tuple 							= tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]])  # noqa: E231
			a_						:						Union[str, Any] 							= tf.convert_to_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]])  # noqa: E231
			# these are sequence labels (i.e. at the token level)
			a_						:						int 							= tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]])  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Optional[Any] 							= TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""    )

									a_							,					a_							,					a_							,					a_							,					a_						:						Tuple 							= prepare_layoutlm_batch_inputs()

									# forward pass
									a_						:						Any 							= model(input_ids=_lowercase  ,      bbox=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase    )

									# test the sequence output on [0, :3, :3]
									a_						:						Dict 							= tf.convert_to_tensor(
									    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]  ,      )

									self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]  ,      _lowercase  ,      atol=1e-3    )    )

									# test the pooled output on [1, :3]
									a_						:						List[Any] 							= tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]    )

									self.assertTrue(np.allclose(outputs.pooler_output[1, :3]  ,      _lowercase  ,      atol=1e-3    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Any 							= TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""  ,      num_labels=2    )

									a_							,					a_							,					a_							,					a_							,					a_						:						Union[str, Any] 							= prepare_layoutlm_batch_inputs()

									# forward pass
									a_						:						Dict 							= model(
									    input_ids=_lowercase  ,      bbox=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      labels=tf.convert_to_tensor([1, 1]    )  ,      )

									# test whether we get a loss as a scalar
									a_						:						Optional[Any] 							= outputs.loss
									a_						:						str 							= (2,)
									self.assertEqual(loss.shape  ,      _lowercase    )

									# test the shape of the logits
									a_						:						List[Any] 							= outputs.logits
									a_						:						Optional[int] 							= (2, 2)
									self.assertEqual(logits.shape  ,      _lowercase    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Union[str, Any] 							= TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""  ,      num_labels=13    )

									a_							,					a_							,					a_							,					a_							,					a_						:						Any 							= prepare_layoutlm_batch_inputs()

									# forward pass
									a_						:						Optional[int] 							= model(
									    input_ids=_lowercase  ,      bbox=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      labels=_lowercase    )

									# test the shape of the logits
									a_						:						List[str] 							= outputs.logits
									a_						:						int 							= tf.convert_to_tensor((2, 25, 13)    )
									self.assertEqual(logits.shape  ,      _lowercase    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						str 							= TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""    )

									a_							,					a_							,					a_							,					a_							,					a_						:						Optional[Any] 							= prepare_layoutlm_batch_inputs()

									# forward pass
									a_						:						Optional[int] 							= model(input_ids=_lowercase  ,      bbox=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase    )

									# test the shape of the logits
									a_						:						Tuple 							= tf.convert_to_tensor((2, 25)    )
									self.assertEqual(outputs.start_logits.shape  ,      _lowercase    )
									self.assertEqual(outputs.end_logits.shape  ,      _lowercase    )










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


