

"""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     ,							UpperCAmelCase_     ,							UpperCAmelCase_=1_3     ,							UpperCAmelCase_=7     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=9_9     ,							UpperCAmelCase_=3_2     ,							UpperCAmelCase_=5     ,							UpperCAmelCase_=4     ,							UpperCAmelCase_=3_7     ,							UpperCAmelCase_="gelu"     ,							UpperCAmelCase_=0.1     ,							UpperCAmelCase_=0.1     ,							UpperCAmelCase_=5_1_2     ,							UpperCAmelCase_=1_6     ,							UpperCAmelCase_=2     ,							UpperCAmelCase_=0.0_2     ,							UpperCAmelCase_=3     ,							UpperCAmelCase_=4     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=1_0_0_0     ,							)			->						Optional[int]:
											__lowercase												=			parent
											__lowercase												=			batch_size
											__lowercase												=			seq_length
											__lowercase												=			is_training
											__lowercase												=			use_input_mask
											__lowercase												=			use_token_type_ids
											__lowercase												=			use_labels
											__lowercase												=			vocab_size
											__lowercase												=			hidden_size
											__lowercase												=			num_hidden_layers
											__lowercase												=			num_attention_heads
											__lowercase												=			intermediate_size
											__lowercase												=			hidden_act
											__lowercase												=			hidden_dropout_prob
											__lowercase												=			attention_probs_dropout_prob
											__lowercase												=			max_position_embeddings
											__lowercase												=			type_vocab_size
											__lowercase												=			type_sequence_label_size
											__lowercase												=			initializer_range
											__lowercase												=			num_labels
											__lowercase												=			num_choices
											__lowercase												=			scope
											__lowercase												=			range_bbox







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

											__lowercase												=			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]:
																										__lowercase												=			bbox[i, j, 3]
																										__lowercase												=			bbox[i, j, 1]
																										__lowercase												=			t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																										__lowercase												=			bbox[i, j, 2]
																										__lowercase												=			bbox[i, j, 0]
																										__lowercase												=			t

											__lowercase												=			None
											if self.use_input_mask:
																__lowercase												=			ids_tensor([self.batch_size, self.seq_length]     ,							vocab_size=2							)

											__lowercase												=			None
											if self.use_token_type_ids:
																__lowercase												=			ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size							)

											__lowercase												=			None
											__lowercase												=			None
											__lowercase												=			None
											if self.use_labels:
																__lowercase												=			ids_tensor([self.batch_size]     ,							self.type_sequence_label_size							)
																__lowercase												=			ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels							)
																__lowercase												=			ids_tensor([self.batch_size]     ,							self.num_choices							)

											__lowercase												=			self.get_config()

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







						def        _UpperCamelCase    (self							)			->						Optional[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        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Dict:
											__lowercase												=			LayoutLMModel(config=UpperCAmelCase_							)
											model.to(UpperCAmelCase_							)
											model.eval()
											__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_							)
											__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_							)
											__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_							)
											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     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						int:
											__lowercase												=			LayoutLMForMaskedLM(config=UpperCAmelCase_							)
											model.to(UpperCAmelCase_							)
											model.eval()
											__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							labels=UpperCAmelCase_							)
											self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[Any]:
											__lowercase												=			self.num_labels
											__lowercase												=			LayoutLMForSequenceClassification(UpperCAmelCase_							)
											model.to(UpperCAmelCase_							)
											model.eval()
											__lowercase												=			model(
											    UpperCAmelCase_     ,							UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							labels=UpperCAmelCase_							)
											self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[str]:
											__lowercase												=			self.num_labels
											__lowercase												=			LayoutLMForTokenClassification(config=UpperCAmelCase_							)
											model.to(UpperCAmelCase_							)
											model.eval()
											__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							labels=UpperCAmelCase_							)
											self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						Optional[Any]:
											__lowercase												=			LayoutLMForQuestionAnswering(config=UpperCAmelCase_							)
											model.to(UpperCAmelCase_							)
											model.eval()
											__lowercase												=			model(
											    UpperCAmelCase_     ,							bbox=UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							start_positions=UpperCAmelCase_     ,							end_positions=UpperCAmelCase_     ,							)
											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							)			->						Any:
											__lowercase												=			self.prepare_config_and_inputs()
											(
											    (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, (
											    __lowercase
											)						, 
											)												=			config_and_inputs
											__lowercase												=			{
											    '''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       ):
						__snake_case 			=     (
						    (
						        LayoutLMModel,
						        LayoutLMForMaskedLM,
						        LayoutLMForSequenceClassification,
						        LayoutLMForTokenClassification,
						        LayoutLMForQuestionAnswering,
						    )
						    if is_torch_available()
						    else None
						)
						__snake_case 			=     (
						    {
						        'document-question-answering': LayoutLMForQuestionAnswering,
						        'feature-extraction': LayoutLMModel,
						        'fill-mask': LayoutLMForMaskedLM,
						        'text-classification': LayoutLMForSequenceClassification,
						        'token-classification': LayoutLMForTokenClassification,
						        'zero-shot': LayoutLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__snake_case 			=     True







						def        _UpperCamelCase    (self							)			->						str:
											__lowercase												=			LayoutLMModelTester(self							)
											__lowercase												=			ConfigTester(self     ,							config_class=UpperCAmelCase_     ,							hidden_size=3_7							)







						def        _UpperCamelCase    (self							)			->						Tuple:
											self.config_tester.run_common_tests()







						def        _UpperCamelCase    (self							)			->						Union[str, Any]:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						Union[str, Any]:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																__lowercase												=			type
																self.model_tester.create_and_check_model(*UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						Dict:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						List[Any]:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_							)







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





def 				__UpperCamelCase   (    )				->			List[str]:




					"""simple docstring"""



					__lowercase												=			torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]] ,	device=a__			)  # noqa: E231
					__lowercase												=			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=a__			)  # noqa: E231
					__lowercase												=			torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]] ,	device=a__			)  # noqa: E231
					__lowercase												=			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=a__			)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					__lowercase												=			torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]] ,	device=a__			)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class 			_snake_case     (						unittest.TestCase       ):







						@slow
						def        _UpperCamelCase    (self							)			->						Any:
											__lowercase												=			LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''							).to(UpperCAmelCase_							)

											__lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase												=			prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase												=			model(input_ids=UpperCAmelCase_     ,							bbox=UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_							)

											# test the sequence output on [0, :3, :3]
											__lowercase												=			torch.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]]     ,							device=UpperCAmelCase_     ,							)

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

											# test the pooled output on [1, :3]
											__lowercase												=			torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]     ,							device=UpperCAmelCase_							)

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







						@slow
						def        _UpperCamelCase    (self							)			->						Tuple:
											# initialize model with randomly initialized sequence classification head
											__lowercase												=			LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''     ,							num_labels=2							).to(
											    UpperCAmelCase_							)

											__lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase												=			prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase												=			model(
											    input_ids=UpperCAmelCase_     ,							bbox=UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							labels=torch.tensor([1, 1]     ,							device=UpperCAmelCase_							)     ,							)

											# test whether we get a loss as a scalar
											__lowercase												=			outputs.loss
											__lowercase												=			torch.Size([]							)
											self.assertEqual(loss.shape     ,							UpperCAmelCase_							)

											# test the shape of the logits
											__lowercase												=			outputs.logits
											__lowercase												=			torch.Size((2, 2)							)
											self.assertEqual(logits.shape     ,							UpperCAmelCase_							)







						@slow
						def        _UpperCamelCase    (self							)			->						Union[str, Any]:
											# initialize model with randomly initialized token classification head
											__lowercase												=			LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''     ,							num_labels=1_3							).to(
											    UpperCAmelCase_							)

											__lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase												=			prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase												=			model(
											    input_ids=UpperCAmelCase_     ,							bbox=UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_     ,							labels=UpperCAmelCase_							)

											# 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
											__lowercase												=			outputs.logits
											__lowercase												=			torch.Size((2, 2_5, 1_3)							)
											self.assertEqual(logits.shape     ,							UpperCAmelCase_							)







						@slow
						def        _UpperCamelCase    (self							)			->						List[Any]:
											# initialize model with randomly initialized token classification head
											__lowercase												=			LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''							).to(UpperCAmelCase_							)

											__lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase												=			prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase												=			model(input_ids=UpperCAmelCase_     ,							bbox=UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_     ,							token_type_ids=UpperCAmelCase_							)

											# test the shape of the logits
											__lowercase												=			torch.Size((2, 2_5)							)
											self.assertEqual(outputs.start_logits.shape     ,							UpperCAmelCase_							)
											self.assertEqual(outputs.end_logits.shape     ,							UpperCAmelCase_							)





"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase						,			unittest.TestCase       ):
						__snake_case 			=     StableDiffusionModelEditingPipeline
						__snake_case 			=     TEXT_TO_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_BATCH_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS







						def        _UpperCamelCase    (self							)			->						int:
											torch.manual_seed(0							)
											__lowercase												=			UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)     ,							layers_per_block=2     ,							sample_size=3_2     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,							cross_attention_dim=3_2     ,							)
											__lowercase												=			DDIMScheduler()
											torch.manual_seed(0							)
											__lowercase												=			AutoencoderKL(
											    block_out_channels=[3_2, 6_4]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,							latent_channels=4     ,							)
											torch.manual_seed(0							)
											__lowercase												=			CLIPTextConfig(
											    bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=3_2     ,							intermediate_size=3_7     ,							layer_norm_eps=1e-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=1_0_0_0     ,							)
											__lowercase												=			CLIPTextModel(UpperCAmelCase_							)
											__lowercase												=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

											__lowercase												=			{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=0							)			->						List[str]:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    # Setting height and width to None to prevent OOMs on CPU.
											    '''height''': None,
											    '''width''': None,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			sd_pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1]
											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]							)

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







						def        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			'''french fries'''
											__lowercase												=			sd_pipe(**UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_							)
											__lowercase												=			output.images
											__lowercase												=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]							)

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







						def        _UpperCamelCase    (self							)			->						Dict:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			EulerAncestralDiscreteScheduler(
											    beta_start=0.0_0_0_8_5     ,							beta_end=0.0_1_2     ,							beta_schedule='''scaled_linear'''							)
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			sd_pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]							)

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







						def        _UpperCamelCase    (self							)			->						str:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			PNDMScheduler()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											# the pipeline does not expect pndm so test if it raises error.
											with self.assertRaises(UpperCAmelCase_							):
																__lowercase												=			sd_pipe(**UpperCAmelCase_							).images







						def        _UpperCamelCase    (self							)			->						Tuple:
											super().test_inference_batch_single_identical(expected_max_diff=5e-3							)







						def        _UpperCamelCase    (self							)			->						str:
											super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)



@slow
@require_torch_gpu
class 			_snake_case     (						unittest.TestCase       ):







						def        _UpperCamelCase    (self							)			->						int:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=0							)			->						int:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''CompVis/stable-diffusion-v1-4'''
											__lowercase												=			StableDiffusionModelEditingPipeline.from_pretrained(UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_							)
											pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
											pipe.enable_attention_slicing()

											__lowercase												=			self.get_inputs()
											__lowercase												=			pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

											__lowercase												=			np.array(
											    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]							)

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

											# make sure image changes after editing
											pipe.edit_model('''A pack of roses'''     ,							'''A pack of blue roses'''							)

											__lowercase												=			pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

											assert np.abs(expected_slice - image_slice							).max() > 1e-1







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowercase												=			'''CompVis/stable-diffusion-v1-4'''
											__lowercase												=			DDIMScheduler.from_pretrained(UpperCAmelCase_     ,							subfolder='''scheduler'''							)
											__lowercase												=			StableDiffusionModelEditingPipeline.from_pretrained(
											    UpperCAmelCase_     ,							scheduler=UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_							)
											__lowercase												=			pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
											pipe.enable_attention_slicing(1							)
											pipe.enable_sequential_cpu_offload()

											__lowercase												=			self.get_inputs()
											__lowercase												=			pipe(**UpperCAmelCase_							)

											__lowercase												=			torch.cuda.max_memory_allocated()
											# make sure that less than 4.4 GB is allocated
											assert mem_bytes < 4.4 * 1_0**9


