
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Any	=		"AutoTokenizer"
						UpperCamelCase_							: Union[str, Any]	=		["tokenizer"]

						UpperCamelCase_							: str	=		{
						    "semantic_prompt": 1,
						    "coarse_prompt": 2,
						    "fine_prompt": 2,
						}

						def __init__(			self       ,							a       ,							a=None				)					->      Dict:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	speaker_embeddings

						@classmethod
						def  A_				(			cls       ,							a       ,							a="speaker_embeddings_path.json"       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

								if speaker_embeddings_dict_path is not None:
										_UpperCamelCase						    =	get_file_from_repo(
										    a       ,							a       ,							subfolder=kwargs.pop("""subfolder"""       ,							a				)       ,							cache_dir=kwargs.pop("""cache_dir"""       ,							a				)       ,							force_download=kwargs.pop("""force_download"""       ,							a				)       ,							proxies=kwargs.pop("""proxies"""       ,							a				)       ,							resume_download=kwargs.pop("""resume_download"""       ,							a				)       ,							local_files_only=kwargs.pop("""local_files_only"""       ,							a				)       ,							use_auth_token=kwargs.pop("""use_auth_token"""       ,							a				)       ,							revision=kwargs.pop("""revision"""       ,							a				)       ,							)
										if speaker_embeddings_path is None:
												logger.warning(
												    F'`{os.path.join(a       ,							a				)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'				)
												_UpperCamelCase						    =	None
										else:
												with open(a				) as speaker_embeddings_json:
														_UpperCamelCase						    =	json.load(a				)
								else:
										_UpperCamelCase						    =	None

								_UpperCamelCase						    =	AutoTokenizer.from_pretrained(a       ,							**a				)

								return cls(tokenizer=a       ,							speaker_embeddings=a				)

						def  A_				(			self       ,							a       ,							a="speaker_embeddings_path.json"       ,							a="speaker_embeddings"       ,							a = False       ,							**a       ,							)					->      Optional[int]:


								'''simple docstring'''

								if self.speaker_embeddings is not None:
										os.makedirs(os.path.join(a       ,							a       ,							"""v2"""				)       ,							exist_ok=a				)

										_UpperCamelCase						    =	{}

										_UpperCamelCase						    =	save_directory

										for prompt_key in self.speaker_embeddings:
												if prompt_key != "repo_or_path":
														_UpperCamelCase						    =	self._load_voice_preset(a				)

														_UpperCamelCase						    =	{}
														for key in self.speaker_embeddings[prompt_key]:
																np.save(
																    os.path.join(
																        embeddings_dict["""repo_or_path"""]       ,							a       ,							F'{prompt_key}_{key}'				)       ,							voice_preset[key]       ,							allow_pickle=a       ,							)
																_UpperCamelCase						    =	os.path.join(a       ,							F'{prompt_key}_{key}.npy'				)

														_UpperCamelCase						    =	tmp_dict

										with open(os.path.join(a       ,							a				)       ,							"""w"""				) as fp:
												json.dump(a       ,							a				)

								super().save_pretrained(a       ,							a       ,							**a				)

						def  A_				(			self       ,							a = None       ,							**a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.speaker_embeddings[voice_preset]

								_UpperCamelCase						    =	{}
								for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
										if key not in voice_preset_paths:
												raise ValueError(
												    F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'				)

										_UpperCamelCase						    =	get_file_from_repo(
										    self.speaker_embeddings.get("""repo_or_path"""       ,							"""/"""				)       ,							voice_preset_paths[key]       ,							subfolder=kwargs.pop("""subfolder"""       ,							a				)       ,							cache_dir=kwargs.pop("""cache_dir"""       ,							a				)       ,							force_download=kwargs.pop("""force_download"""       ,							a				)       ,							proxies=kwargs.pop("""proxies"""       ,							a				)       ,							resume_download=kwargs.pop("""resume_download"""       ,							a				)       ,							local_files_only=kwargs.pop("""local_files_only"""       ,							a				)       ,							use_auth_token=kwargs.pop("""use_auth_token"""       ,							a				)       ,							revision=kwargs.pop("""revision"""       ,							a				)       ,							)
										if path is None:
												raise ValueError(
												    F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"       ,							"/"				)       ,							voice_preset_paths[key]				)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'				)

										_UpperCamelCase						    =	np.load(a				)

								return voice_preset_dict

						def  A_				(			self       ,							a = None				)					->      Tuple:


								'''simple docstring'''

								for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
										if key not in voice_preset:
												raise ValueError(F'Voice preset unrecognized, missing {key} as a key.'				)

										if not isinstance(voice_preset[key]       ,							np.ndarray				):
												raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'				)

										if len(voice_preset[key].shape				) != self.preset_shape[key]:
												raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]				)}D ndarray.'				)







						def __call__(			self       ,							a=None       ,							a=None       ,							a="pt"       ,							a=2_56       ,							a=False       ,							a=True       ,							a=False       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								if voice_preset is not None and not isinstance(a       ,							a				):
										if (
										    isinstance(a       ,							a				)
										    and self.speaker_embeddings is not None
										    and voice_preset in self.speaker_embeddings
										):
												_UpperCamelCase						    =	self._load_voice_preset(a				)

										else:
												if isinstance(a       ,							a				) and not voice_preset.endswith(""".npz"""				):
														_UpperCamelCase						    =	voice_preset + """.npz"""

												_UpperCamelCase						    =	np.load(a				)

								if voice_preset is not None:
										self._validate_voice_preset_dict(a       ,							**a				)
										_UpperCamelCase						    =	BatchFeature(data=a       ,							tensor_type=a				)

								_UpperCamelCase						    =	self.tokenizer(
								    a       ,							return_tensors=a       ,							padding="""max_length"""       ,							max_length=a       ,							return_attention_mask=a       ,							return_token_type_ids=a       ,							add_special_tokens=a       ,							**a       ,							)

								if voice_preset is not None:
										_UpperCamelCase						    =	voice_preset

								return encoded_text





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__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		StableDiffusionInpaintPipeline
						UpperCamelCase_							: int	=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase_							: Union[str, Any]	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase_							: Tuple	=		frozenset(
						    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_							: int	=		frozenset([]	)

						def  A_				(			self				)					->      Union[str, Any]:


								'''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=a       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								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=1_28       ,							)
								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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_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       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(a				)				).to(a				)
								_UpperCamelCase						    =	image.cpu().permute(0       ,							2       ,							3       ,							1				)[0]
								_UpperCamelCase						    =	Image.fromarray(np.uinta(a				)				).convert("""RGB"""				).resize((64, 64)				)
								_UpperCamelCase						    =	Image.fromarray(np.uinta(image + 4				)				).convert("""RGB"""				).resize((64, 64)				)
								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_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				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline(**a				)
								_UpperCamelCase						    =	sd_pipe.to(a				)
								sd_pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	sd_pipe(**a				).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				)					->      Tuple:


								'''simple docstring'''

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







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

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

						def  A_				(			self				)					->      Dict:


								'''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(a       ,							safety_checker=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								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=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

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

						def  A_				(			self				)					->      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(
								    a       ,							torch_dtype=torch.floataa       ,							safety_checker=a       ,							)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								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=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

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







						def  A_				(			self				)					->      Dict:


								'''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(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	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()

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

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							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



