
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 			A_  :




	'''simple docstring'''


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





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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       ):
		if attention_mask is None:
			lowercase	    =       input_ids.ne(config.pad_token_id			)
		if decoder_attention_mask is None:
			lowercase	    =       decoder_input_ids.ne(config.pad_token_id			)
		if head_mask is None:
			lowercase	    =       torch.ones(config.num_hidden_layers						,       config.num_attention_heads						,       device=snake_case			)
		if decoder_head_mask is None:
			lowercase	    =       torch.ones(config.num_decoder_layers						,       config.num_attention_heads						,       device=snake_case			)
		if cross_attn_head_mask is None:
			lowercase	    =       torch.ones(
			    config.num_decoder_layers						,       config.num_attention_heads						,       device=snake_case			)
		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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ids_tensor([self.batch_size, self.encoder_seq_length]						,       self.vocab_size			)
		lowercase	    =       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
		lowercase	    =       input_ids.clamp(self.pad_token_id + 1			)
		lowercase	    =       decoder_input_ids.clamp(self.pad_token_id + 1			)

		lowercase	    =       self.get_config()
		lowercase	    =       config.num_attention_heads
		lowercase	    =       self.prepare_inputs_dict(snake_case						,       snake_case						,       snake_case			)
		return config, input_dict





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.prepare_config_and_inputs()
		return config, inputs_dict





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return TaConfig(
		    vocab_size=166						,       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       SCREAMING_SNAKE_CASE__		(			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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       UMTaModel(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(
		    input_ids=snake_case						,       decoder_input_ids=snake_case						,       attention_mask=snake_case						,       decoder_attention_mask=snake_case						,       )
		lowercase	    =       model(input_ids=snake_case						,       decoder_input_ids=snake_case			)
		lowercase	    =       result.last_hidden_state
		lowercase	    =       result.past_key_values
		lowercase	    =       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(snake_case			)						,       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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       UMTaModel(config=snake_case			).get_decoder().to(snake_case			).eval()
		# first forward pass
		lowercase	    =       model(snake_case						,       use_cache=snake_case			)
		lowercase	    =       model(snake_case			)
		lowercase	    =       model(snake_case						,       use_cache=snake_case			)

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

		lowercase    ,   lowercase	    =       outputs.to_tuple()

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

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

		lowercase	    =       model(snake_case			)['last_hidden_state']
		lowercase	    =       model(snake_case						,       past_key_values=snake_case			)['last_hidden_state']

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

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





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







@require_torch
class 			A_  (     __lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       UMTaModelTester(self			)





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





	@unittest.skipIf(torch_device == 'cpu'						,       'Cant do half precision'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model_fpaa_forward(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		lowercase	    =       config_and_inputs[0]
		lowercase	    =       UMTaForConditionalGeneration(snake_case			).eval()
		model.to(snake_case			)

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

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

			lowercase	    =       model.generate(
			    config_and_inputs[1]['input_ids']						,       num_beams=1						,       max_length=3						,       output_attentions=snake_case						,       return_dict_in_generate=snake_case						,       **snake_case						,       )
			# We check the state of decoder_attentions and cross_attentions just from the last step
			lowercase	    =       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       SCREAMING_SNAKE_CASE__		(			self			):
		pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 			A_  (     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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,       return_dict=snake_case			).to(snake_case			)
		lowercase	    =       AutoTokenizer.from_pretrained('google/umt5-small'						,       use_fast=snake_case						,       legacy=snake_case			)
		lowercase	    =       [
		    '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>.',
		]
		lowercase	    =       tokenizer(snake_case						,       return_tensors='pt'						,       padding=snake_case			).input_ids
		# fmt: off
		lowercase	    =       torch.tensor(
		    [
		        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
		        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
		        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
		    ]			)
		# fmt: on
		torch.testing.assert_allclose(snake_case						,       snake_case			)

		lowercase	    =       model.generate(input_ids.to(snake_case			)			)
		lowercase	    =       [
		    '<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>',
		]
		lowercase	    =       tokenizer.batch_decode(snake_case			)
		self.assertEqual(snake_case						,       snake_case			)









"""simple docstring"""
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						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				StableDiffusionInpaintPipeline
						SCREAMING_SNAKE_CASE_:					List[str] 					=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						SCREAMING_SNAKE_CASE_:					Dict 					=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						SCREAMING_SNAKE_CASE_:					Any 					=				frozenset(
						    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						SCREAMING_SNAKE_CASE_:					Any 					=				frozenset([]       )







						def 			A      (    self							)		->							Optional[Any]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=9   ,				out_channels=4   ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,				cross_attention_dim=3_2   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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   ,				sample_size=1_2_8   ,				)
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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   ,				hidden_act='gelu'   ,				projection_dim=5_1_2   ,				)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    CLIPTextModel(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

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







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=0							)		->							Optional[int]:
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    floats_tensor((1, 3, 3_2, 3_2)   ,				rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		Any       		=    Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert('RGB'							).resize((6_4, 6_4)							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((6_4, 6_4)							)
										if str(lowerCAmelCase_							).startswith('mps'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.manual_seed(lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
										    '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 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
										_SCREAMING_SNAKE_CASE   :		str       		=    self.get_dummy_components()
										_SCREAMING_SNAKE_CASE   :		Any       		=    StableDiffusionInpaintPipeline(**lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_dummy_inputs(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(**lowerCAmelCase_							).images
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    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 			A      (    self							)		->							str:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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







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







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
										    '/yellow_cat_sitting_on_a_park_bench.npy'							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_							)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 9e-3







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
										    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		str       		=    StableDiffusionInpaintPipeline.from_pretrained(
										    lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				safety_checker=lowerCAmelCase_   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 5e-1







						def 			A      (    self							)		->							List[str]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    PNDMScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionInpaintPipeline.from_pretrained(
										    lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing(1							)
										pipe.enable_sequential_cpu_offload()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=2   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 1_0**9

