





'''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_     (				snake_case_      ,    snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

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


    def 			snake_case_(		self       )		->   str:
        torch.manual_seed(0       )
        _SCREAMING_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=A ,		)
        _SCREAMING_SNAKE_CASE									=			PNDMScheduler(skip_prk_steps=A       )
        torch.manual_seed(0       )
        _SCREAMING_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=128 ,		)
        torch.manual_seed(0       )
        _SCREAMING_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=1000 ,		hidden_act="""gelu""" ,		projection_dim=512 ,		)
        _SCREAMING_SNAKE_CASE									=			CLIPTextModel(A       )
        _SCREAMING_SNAKE_CASE									=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

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


    def 			snake_case_(		self ,		A ,		A=0       )		->   Optional[Any]:
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        _SCREAMING_SNAKE_CASE									=			floats_tensor((1, 3, 32, 32) ,		rng=random.Random(A       )       ).to(A       )
        _SCREAMING_SNAKE_CASE									=			image.cpu().permute(0 ,		2 ,		3 ,		1       )[0]
        _SCREAMING_SNAKE_CASE									=			Image.fromarray(np.uinta(A       )       ).convert("""RGB"""       ).resize((64, 64)       )
        _SCREAMING_SNAKE_CASE									=			Image.fromarray(np.uinta(image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )
        if str(A       ).startswith("""mps"""       ):
            _SCREAMING_SNAKE_CASE									=			torch.manual_seed(A       )
        else:
            _SCREAMING_SNAKE_CASE									=			torch.Generator(device=A       ).manual_seed(A       )
        _SCREAMING_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 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        _SCREAMING_SNAKE_CASE									=			self.get_dummy_components()
        _SCREAMING_SNAKE_CASE									=			StableDiffusionInpaintPipeline(**A       )
        _SCREAMING_SNAKE_CASE									=			sd_pipe.to(A       )
        sd_pipe.set_progress_bar_config(disable=A       )

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

        assert image.shape == (1, 64, 64, 3)
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self       )		->   Any:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




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

    '''simple docstring'''


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


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-inpaint/init_image.png"""       )
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
        _SCREAMING_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"""       )

        _SCREAMING_SNAKE_CASE									=			"""stabilityai/stable-diffusion-2-inpainting"""
        _SCREAMING_SNAKE_CASE									=			StableDiffusionInpaintPipeline.from_pretrained(A ,		safety_checker=A       )
        pipe.to(A       )
        pipe.set_progress_bar_config(disable=A       )
        pipe.enable_attention_slicing()

        _SCREAMING_SNAKE_CASE									=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )
        _SCREAMING_SNAKE_CASE									=			pipe(
            prompt=A ,		image=A ,		mask_image=A ,		generator=A ,		output_type="""np""" ,		)
        _SCREAMING_SNAKE_CASE									=			output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image       ).max() < 9e-3


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-inpaint/init_image.png"""       )
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
        _SCREAMING_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"""       )

        _SCREAMING_SNAKE_CASE									=			"""stabilityai/stable-diffusion-2-inpainting"""
        _SCREAMING_SNAKE_CASE									=			StableDiffusionInpaintPipeline.from_pretrained(
            A ,		torch_dtype=torch.floataa ,		safety_checker=A ,		)
        pipe.to(A       )
        pipe.set_progress_bar_config(disable=A       )
        pipe.enable_attention_slicing()

        _SCREAMING_SNAKE_CASE									=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )
        _SCREAMING_SNAKE_CASE									=			pipe(
            prompt=A ,		image=A ,		mask_image=A ,		generator=A ,		output_type="""np""" ,		)
        _SCREAMING_SNAKE_CASE									=			output.images[0]

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





    def 			snake_case_(		self       )		->   int:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

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

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

        _SCREAMING_SNAKE_CASE									=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

        _SCREAMING_SNAKE_CASE									=			torch.manual_seed(0       )
        _SCREAMING_SNAKE_CASE									=			pipe(
            prompt=A ,		image=A ,		mask_image=A ,		generator=A ,		num_inference_steps=2 ,		output_type="""np""" ,		)

        _SCREAMING_SNAKE_CASE									=			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 os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer.get_vocab()  )






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )
												processor.save_pretrained(
												    self.tmpdirname ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       speaker_embeddings_directory=self.speaker_embeddings_directory ,       )

												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					BarkProcessor.from_pretrained(
												    self.tmpdirname ,       self.speaker_embeddings_dict_path ,       bos_token="""(BOS)""" ,       eos_token="""(EOS)""" ,       )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
												    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
												}

												# test providing already loaded voice_preset
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from npz file
												__UpperCAmelCase   :						Union[str, Any]      							=					os.path.join(self.tmpdirname ,       """file.npz"""  )
												np.savez(UpperCamelCase ,       **UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					inputs["""history_prompt"""]

												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from the hub
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=self.voice_preset  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key] ,       encoded_processor[key].squeeze().tolist()  )
