




'''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				):
       __a       :  List[Any]            =      StableDiffusionInpaintPipeline
       __a       :  Union[str, Any]            =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __a       :  Dict            =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __a       :  Any            =      frozenset(
           []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       __a       :  Union[str, Any]            =      frozenset([]				)
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	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=lowercase     ,       )
           UpperCAmelCase     =	PNDMScheduler(skip_prk_steps=lowercase       )
           torch.manual_seed(0       )
           UpperCAmelCase     =	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       )
           UpperCAmelCase     =	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     ,       )
           UpperCAmelCase     =	CLIPTextModel(lowercase       )
           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

           UpperCAmelCase     =	{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[int]=0       ):



           '''simple docstring'''

           UpperCAmelCase     =	floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(lowercase       )       ).to(lowercase       )
           UpperCAmelCase     =	image.cpu().permute(0     ,       2     ,       3     ,       1       )[0]
           UpperCAmelCase     =	Image.fromarray(np.uinta(lowercase       )       ).convert('''RGB'''       ).resize((64, 64)       )
           UpperCAmelCase     =	Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((64, 64)       )
           if str(lowercase       ).startswith('''mps'''       ):
               UpperCAmelCase     =	torch.manual_seed(lowercase       )
           else:
               UpperCAmelCase     =	torch.Generator(device=lowercase       ).manual_seed(lowercase       )
           UpperCAmelCase     =	{
               '''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		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =	self.get_dummy_components()
           UpperCAmelCase     =	StableDiffusionInpaintPipeline(**lowercase       )
           UpperCAmelCase     =	sd_pipe.to(lowercase       )
           sd_pipe.set_progress_bar_config(disable=lowercase       )

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

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase     =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]       )

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







       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






@slow
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

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

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

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

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

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 9E-3
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

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

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

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

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

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







       def 					A   (			self		:			str       ):



           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

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

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

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

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

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










'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      MgpstrTokenizer
       __a       :  Optional[Any]            =      False
       __a       :  str            =      {}
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           # fmt: off
           UpperCAmelCase     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write(json.dumps(lowercase       ) + '''\n'''       )
       def 					A   (			self		:			int     ,       **lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''tester'''
           UpperCAmelCase     =	'''tester'''
           return input_text, output_text
       @unittest.skip('''MGP-STR always lower cases letters.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers(do_lower_case=lowercase       )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase     =	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   UpperCAmelCase     =	tokenizer.encode([special_token]     ,       add_special_tokens=lowercase       )
                   self.assertEqual(len(lowercase       )     ,       1       )

                   UpperCAmelCase     =	tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
                   self.assertTrue(special_token not in decoded       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase ,  UpperCAmelCase     =	self.get_input_output_texts(lowercase       )

                   UpperCAmelCase     =	tokenizer.tokenize(lowercase       )
                   UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
                   UpperCAmelCase     =	tokenizer.encode(lowercase     ,       add_special_tokens=lowercase       )
                   self.assertListEqual(lowercase     ,       lowercase       )

                   UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
                   self.assertNotEqual(len(lowercase       )     ,       0       )
                   UpperCAmelCase     =	tokenizer.decode(lowercase       )
                   self.assertIsInstance(lowercase     ,       lowercase       )

                   self.assertEqual(text_a.replace(''' '''     ,       ''''''       )     ,       lowercase       )
       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass







       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           pass




