


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=99		,				_UpperCAmelCase=13		,				_UpperCAmelCase=7		,				_UpperCAmelCase=9		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=False		,				_UpperCAmelCase=32		,				_UpperCAmelCase=5		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase=8		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.002		,				_UpperCAmelCase=1		,				_UpperCAmelCase=0		,				_UpperCAmelCase=0		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				encoder_seq_length
       snake_case_	=				decoder_seq_length
       # For common tests
       snake_case_	=				self.decoder_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_attention_mask
       snake_case_	=				use_labels
       snake_case_	=				vocab_size
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				d_ff
       snake_case_	=				relative_attention_num_buckets
       snake_case_	=				dropout_rate
       snake_case_	=				initializer_factor
       snake_case_	=				eos_token_id
       snake_case_	=				pad_token_id
       snake_case_	=				decoder_start_token_id
       snake_case_	=				None
       snake_case_	=				decoder_layers



 def 						UpperCamelCase__					(     self ):
       return TaConfig.from_pretrained('''google/umt5-base''' )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       if attention_mask is None:
             snake_case_	=				input_ids.ne(config.pad_token_id )
       if decoder_attention_mask is None:
             snake_case_	=				decoder_input_ids.ne(config.pad_token_id )
       if head_mask is None:
             snake_case_	=				torch.ones(config.num_hidden_layers		,				config.num_attention_heads		,				device=_UpperCAmelCase )
       if decoder_head_mask is None:
             snake_case_	=				torch.ones(config.num_decoder_layers		,				config.num_attention_heads		,				device=_UpperCAmelCase )
       if cross_attn_head_mask is None:
             snake_case_	=				torch.ones(
                 config.num_decoder_layers		,				config.num_attention_heads		,				device=_UpperCAmelCase )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.encoder_seq_length]		,				self.vocab_size )
       snake_case_	=				ids_tensor([self.batch_size, self.decoder_seq_length]		,				self.vocab_size )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for NllbMoe the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       snake_case_	=				input_ids.clamp(self.pad_token_id + 1 )
       snake_case_	=				decoder_input_ids.clamp(self.pad_token_id + 1 )

       snake_case_	=				self.get_config()
       snake_case_	=				config.num_attention_heads
       snake_case_	=				self.prepare_inputs_dict(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       return config, input_dict



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.prepare_config_and_inputs()
       return config, inputs_dict



 def 						UpperCamelCase__					(     self ):
       return TaConfig(
           vocab_size=1_66		,				d_model=self.hidden_size		,				d_ff=self.d_ff		,				d_kv=self.hidden_size // self.num_attention_heads		,				num_layers=self.num_hidden_layers		,				num_decoder_layers=self.decoder_layers		,				num_heads=self.num_attention_heads		,				relative_attention_num_buckets=self.relative_attention_num_buckets		,				dropout_rate=self.dropout_rate		,				initializer_factor=self.initializer_factor		,				eos_token_id=self.eos_token_id		,				bos_token_id=self.pad_token_id		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.decoder_start_token_id		,				)



 def 						UpperCamelCase__					(     self ):
       return TaConfig(
           vocab_size=self.vocab_size		,				d_model=self.hidden_size		,				d_ff=self.d_ff		,				d_kv=self.hidden_size // self.num_attention_heads		,				num_layers=self.num_hidden_layers		,				num_decoder_layers=self.decoder_layers		,				num_heads=self.num_attention_heads		,				relative_attention_num_buckets=self.relative_attention_num_buckets		,				dropout_rate=self.dropout_rate		,				initializer_factor=self.initializer_factor		,				eos_token_id=self.eos_token_id		,				bos_token_id=self.pad_token_id		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.decoder_start_token_id		,				)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           input_ids=_UpperCAmelCase		,				decoder_input_ids=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				decoder_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				model(input_ids=_UpperCAmelCase		,				decoder_input_ids=_UpperCAmelCase )
       snake_case_	=				result.last_hidden_state
       snake_case_	=				result.past_key_values
       snake_case_	=				result.encoder_last_hidden_state

       self.parent.assertEqual(encoder_output.size()		,				(self.batch_size, self.encoder_seq_length, self.hidden_size) )
       self.parent.assertEqual(decoder_output.size()		,				(self.batch_size, self.decoder_seq_length, self.hidden_size) )
       # There should be `num_layers` key value embeddings stored in decoder_past
       self.parent.assertEqual(len(_UpperCAmelCase )		,				config.num_layers )
       # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
       self.parent.assertEqual(len(decoder_past[0] )		,				4 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase ).get_decoder().to(_UpperCAmelCase ).eval()
       # first forward pass
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				use_cache=_UpperCAmelCase )

       self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) )
       self.parent.assertTrue(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) + 1 )

       snake_case_	,   snake_case_	=				outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case_	=				ids_tensor((self.batch_size, 1)		,				config.vocab_size )

       # append to next input_ids and
       snake_case_	=				torch.cat([input_ids, next_tokens]		,				dim=-1 )

       snake_case_	=				model(_UpperCAmelCase )['''last_hidden_state''']
       snake_case_	=				model(_UpperCAmelCase		,				past_key_values=_UpperCAmelCase )['''last_hidden_state''']

       # select random slice
       snake_case_	=				ids_tensor((1,)		,				output_from_past.shape[-1] ).item()
       snake_case_	=				output_from_no_past[:, -1, random_slice_idx].detach()
       snake_case_	=				output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				UMTaModel(config=_UpperCAmelCase ).to(_UpperCAmelCase ).half().eval()
       snake_case_	=				model(**_UpperCAmelCase )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(_UpperCAmelCase ).any().item() )







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __snake_case												=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
 __snake_case												=  (
     {
         "conversational": UMTaForConditionalGeneration,
         "feature-extraction": UMTaModel,
         "summarization": UMTaForConditionalGeneration,
         "text2text-generation": UMTaForConditionalGeneration,
         "translation": UMTaForConditionalGeneration,
         "question-answering": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __snake_case												=  True
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  True
 __snake_case												=  True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __snake_case												=  [0.8, 0.9]



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				UMTaModelTester(self )



 @unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       snake_case_	=				UMTaModel(config_and_inputs[0] ).to(_UpperCAmelCase )
       with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 _UpperCAmelCase		,				(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,				F'''{tmpdirname}/t5_test.onnx'''		,				export_params=_UpperCAmelCase		,				opset_version=9		,				input_names=['''input_ids''', '''decoder_input_ids''']		,				)



 @unittest.skipIf(torch_device == '''cpu'''		,				'''Cant do half precision''' )
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       snake_case_	=				config_and_inputs[0]
       snake_case_	=				UMTaForConditionalGeneration(_UpperCAmelCase ).eval()
       model.to(_UpperCAmelCase )

       snake_case_	=				{
           '''head_mask''': torch.zeros(config.num_layers		,				config.num_heads		,				device=_UpperCAmelCase ),
           '''decoder_head_mask''': torch.zeros(config.num_decoder_layers		,				config.num_heads		,				device=_UpperCAmelCase ),
           '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers		,				config.num_heads		,				device=_UpperCAmelCase ),
       }

       for attn_name, (name, mask) in zip(_UpperCAmelCase		,				head_masking.items() ):
             snake_case_	=				{name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                   snake_case_	=				torch.ones(
                       config.num_decoder_layers		,				config.num_heads		,				device=_UpperCAmelCase )

             snake_case_	=				model.generate(
                 config_and_inputs[1]['''input_ids''']		,				num_beams=1		,				max_length=3		,				output_attentions=_UpperCAmelCase		,				return_dict_in_generate=_UpperCAmelCase		,				**_UpperCAmelCase		,				)
             # We check the state of decoder_attentions and cross_attentions just from the last step
             snake_case_	=				out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
             self.assertEqual(sum([w.sum().item() for w in attn_weights] )		,				0.0 )





 @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
 def 						UpperCamelCase__					(     self ):
       pass







@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @slow
 @unittest.skip(
     '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''		,				return_dict=_UpperCAmelCase ).to(_UpperCAmelCase )
       snake_case_	=				AutoTokenizer.from_pretrained('''google/umt5-small'''		,				use_fast=_UpperCAmelCase		,				legacy=_UpperCAmelCase )
       snake_case_	=				[
           '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
           '''No se como puedo <extra_id_0>.''',
           '''This is the reason why we <extra_id_0> them.''',
           '''The <extra_id_0> walks in <extra_id_1>, seats''',
           '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
       ]
       snake_case_	=				tokenizer(_UpperCAmelCase		,				return_tensors='''pt'''		,				padding=_UpperCAmelCase ).input_ids
       # fmt: off
       snake_case_	=				torch.tensor(
           [
               [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
           ] )
       # fmt: on
       torch.testing.assert_allclose(_UpperCAmelCase		,				_UpperCAmelCase )

       snake_case_	=				model.generate(input_ids.to(_UpperCAmelCase ) )
       snake_case_	=				[
           '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
           '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
       ]
       snake_case_	=				tokenizer.batch_decode(_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''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 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

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

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9