


'''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 						a__					(					a_      ,  a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			StableDiffusionInpaintPipeline
			A						:				str					      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			A						:				Optional[int]					      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A						:				Optional[int]					      =			frozenset(
			    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			A						:				List[str]					      =			frozenset([]   )
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[str]:
						torch.manual_seed(0	)
						__A=	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=lowerCAmelCase_			,    )
						__A=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_	)
						torch.manual_seed(0	)
						__A=	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=128			,    )
						torch.manual_seed(0	)
						__A=	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=1_000			,    hidden_act='gelu'			,    projection_dim=512			,    )
						__A=	CLIPTextModel(lowerCAmelCase_	)
						__A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

						__A=	{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple=0	)      ->		int:
						# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
						__A=	floats_tensor((1, 3, 32, 32)			,    rng=random.Random(lowerCAmelCase_	)	).to(lowerCAmelCase_	)
						__A=	image.cpu().permute(0			,    2			,    3			,    1	)[0]
						__A=	Image.fromarray(np.uinta(lowerCAmelCase_	)	).convert('RGB'	).resize((64, 64)	)
						__A=	Image.fromarray(np.uinta(image + 4	)	).convert('RGB'	).resize((64, 64)	)
						if str(lowerCAmelCase_	).startswith('mps'	):
									__A=	torch.manual_seed(lowerCAmelCase_	)
						else:
									__A=	torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
						__A=	{
						    '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 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[Any]:
						__A=	'cpu'  # ensure determinism for the device-dependent torch.Generator
						__A=	self.get_dummy_components()
						__A=	StableDiffusionInpaintPipeline(**lowerCAmelCase_	)
						__A=	sd_pipe.to(lowerCAmelCase_	)
						sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

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

						assert image.shape == (1, 64, 64, 3)
						__A=	np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]	)

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

			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3	)


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





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		str:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
			def 					lowerCAmelCase     (  self							:			str	)      ->		Optional[Any]:
						__A=	load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'	)
						__A=	load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
						__A=	load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy'	)

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

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

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

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image	).max() < 9E-3
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Optional[int]:
						__A=	load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'	)
						__A=	load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
						__A=	load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'	)

						__A=	'stabilityai/stable-diffusion-2-inpainting'
						__A=	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()

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

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

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image	).max() < 5E-1

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

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

						__A=	'stabilityai/stable-diffusion-2-inpainting'
						__A=	PNDMScheduler.from_pretrained(lowerCAmelCase_			,    subfolder='scheduler'	)
						__A=	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()

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

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

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







'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    )      ->		Optional[int]:
						__A=	parent
						__A=	13
						__A=	7
						__A=	True
						__A=	True
						__A=	False
						__A=	True
						__A=	99
						__A=	32
						__A=	2
						__A=	4
						__A=	37
						__A=	'gelu'
						__A=	0.1
						__A=	0.1
						__A=	512
						__A=	16
						__A=	2
						__A=	0.02
						__A=	3
						__A=	4
						__A=	None
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size	)

						__A=	None
						if self.use_input_mask:
									__A=	random_attention_mask([self.batch_size, self.seq_length]	)

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

						__A=	DistilBertConfig(
						    vocab_size=self.vocab_size			,    dim=self.hidden_size			,    n_layers=self.num_hidden_layers			,    n_heads=self.num_attention_heads			,    hidden_dim=self.intermediate_size			,    hidden_act=self.hidden_act			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    initializer_range=self.initializer_range			,    )

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Any:
						__A=	TFDistilBertModel(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}

						__A=	model(lowerCAmelCase_	)

						__A=	[input_ids, input_mask]

						__A=	model(lowerCAmelCase_	)

						self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Tuple	)      ->		Optional[int]:
						__A=	TFDistilBertForMaskedLM(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		int:
						__A=	TFDistilBertForQuestionAnswering(config=lowerCAmelCase_	)
						__A=	{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.start_logits.shape			,    (self.batch_size, self.seq_length)	)
						self.parent.assertEqual(result.end_logits.shape			,    (self.batch_size, self.seq_length)	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Union[str, Any]:
						__A=	self.num_labels
						__A=	TFDistilBertForSequenceClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[Any]:
						__A=	self.num_choices
						__A=	TFDistilBertForMultipleChoice(lowerCAmelCase_	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_choices)	)
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Dict:
						__A=	self.num_labels
						__A=	TFDistilBertForTokenClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)	)

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						__A=	self.prepare_config_and_inputs()
						((__A),	(__A),	(__A),	(__A),	(__A),	(__A))=	config_and_inputs
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			(
			    (
			        TFDistilBertModel,
			        TFDistilBertForMaskedLM,
			        TFDistilBertForQuestionAnswering,
			        TFDistilBertForSequenceClassification,
			        TFDistilBertForTokenClassification,
			        TFDistilBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else None
			)
			A						:				Optional[int]					      =			(
			    {
			        '''feature-extraction''': TFDistilBertModel,
			        '''fill-mask''': TFDistilBertForMaskedLM,
			        '''question-answering''': TFDistilBertForQuestionAnswering,
			        '''text-classification''': TFDistilBertForSequenceClassification,
			        '''token-classification''': TFDistilBertForTokenClassification,
			        '''zero-shot''': TFDistilBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A						:				str					      =			False
			A						:				List[Any]					      =			False
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[Any]:
						__A=	TFDistilBertModelTester(self	)
						__A=	ConfigTester(self			,    config_class=lowerCAmelCase_			,    dim=37	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						self.config_tester.run_common_tests()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Any:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase_	)

			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__A=	TFDistilBertModel.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)


@require_tf
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			Any	)      ->		List[Any]:
						__A=	TFDistilBertModel.from_pretrained('distilbert-base-uncased'	)
						__A=	tf.constant([[0, 1, 2, 3, 4, 5]]	)
						__A=	model(lowerCAmelCase_	)[0]

						__A=	[1, 6, 768]
						self.assertEqual(output.shape			,    lowerCAmelCase_	)

						__A=	tf.constant(
						    [
						        [
						            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
						            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
						            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
						        ]
						    ]	)
						tf.debugging.assert_near(output[:, :3, :3]			,    lowerCAmelCase_			,    atol=1E-4	)



