


"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE							:      Optional[int]						=      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=8			)      ->			str:
							snake_case_          =						height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_          =						width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=512			,	_SCREAMING_SNAKE_CASE=512			)      ->			List[str]:
							snake_case_          =						pil_image.resize((w, h)			,	resample=Image.BICUBIC			,	reducing_gap=1			)
							snake_case_          =						np.array(pil_image.convert("""RGB"""			)			)
							snake_case_          =						arr.astype(np.floataa			) / 127.5 - 1
							snake_case_          =						np.transpose(_SCREAMING_SNAKE_CASE			,	[2, 0, 1]			)
							snake_case_          =						torch.from_numpy(_SCREAMING_SNAKE_CASE			).unsqueeze(0			)
							return image





class        __A       (snake_case__):





							'''simple docstring'''

							def __init__(			self							: Optional[Any]						,    UpperCAmelCase_							: UNetaDConditionModel						,    UpperCAmelCase_							: DDPMScheduler						,    UpperCAmelCase_							: VQModel						,    )   ->Dict:



														"""simple docstring"""



														super().__init__()

														self.register_modules(
														    unet=UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    movq=UpperCAmelCase_						,    )
														snake_case_          =						2 ** (len(self.movq.config.block_out_channels    ) - 1)



							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: Optional[Any]						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: Dict    )   ->Any:



														"""simple docstring"""



														snake_case_          =						min(int(num_inference_steps * strength    )						,    UpperCAmelCase_    )

														snake_case_          =						max(num_inference_steps - init_timestep						,    0    )
														snake_case_          =						self.scheduler.timesteps[t_start:]

														return timesteps, num_inference_steps - t_start



							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: List[Any]						,    UpperCAmelCase_							: Optional[Any]						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: List[str]=None    )   ->Dict:



														"""simple docstring"""



														if not isinstance(UpperCAmelCase_						,    (torch.Tensor, PIL.Image.Image, list)    ):
																					raise ValueError(
																					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_    )}"""    )

														snake_case_          =						image.to(device=UpperCAmelCase_						,    dtype=UpperCAmelCase_    )

														snake_case_          =						batch_size * num_images_per_prompt

														if image.shape[1] == 4:
																					snake_case_          =						image

														else:
																					if isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ) and len(UpperCAmelCase_    ) != batch_size:
																												raise ValueError(
																												    F"""You have passed a list of generators of length {len(UpperCAmelCase_    )}, but requested an effective batch"""
																												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

																					elif isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
																												snake_case_          =						[
																												    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(UpperCAmelCase_    )
																												]
																												snake_case_          =						torch.cat(UpperCAmelCase_						,    dim=0    )
																					else:
																												snake_case_          =						self.movq.encode(UpperCAmelCase_    ).latent_dist.sample(UpperCAmelCase_    )

																					snake_case_          =						self.movq.config.scaling_factor * init_latents

														snake_case_          =						torch.cat([init_latents]						,    dim=0    )

														snake_case_          =						init_latents.shape
														snake_case_          =						randn_tensor(UpperCAmelCase_						,    generator=UpperCAmelCase_						,    device=UpperCAmelCase_						,    dtype=UpperCAmelCase_    )

														# get latents
														snake_case_          =						self.scheduler.add_noise(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						init_latents

														return latents



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    UpperCAmelCase_							: List[Any]=0    )   ->Dict:



														"""simple docstring"""



														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

														snake_case_          =						torch.device(F"""cuda:{gpu_id}"""    )

														snake_case_          =						[
														    self.unet,
														    self.movq,
														]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(UpperCAmelCase_						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Dict						,    UpperCAmelCase_							: str=0    )   ->Optional[int]:



														"""simple docstring"""



														if is_accelerate_available() and is_accelerate_version(""">="""						,    """0.17.0.dev0"""    ):
																					from accelerate import cpu_offload_with_hook
														else:
																					raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

														snake_case_          =						torch.device(F"""cuda:{gpu_id}"""    )

														if self.device.type != "cpu":
																					self.to("""cpu"""						,    silence_dtype_warnings=UpperCAmelCase_    )
																					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

														snake_case_          =						None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					snake_case_    ,  snake_case_          =						cpu_offload_with_hook(UpperCAmelCase_						,    UpperCAmelCase_						,    prev_module_hook=UpperCAmelCase_    )

														# We'll offload the last model manually.
														snake_case_          =						hook



							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 						lowerCAmelCase    (			self							: str    )   ->int:



														"""simple docstring"""



														if not hasattr(self.unet						,    """_hf_hook"""    ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(UpperCAmelCase_						,    """_hf_hook"""    )
																					    and hasattr(module._hf_hook						,    """execution_device"""    )
																					    and module._hf_hook.execution_device is not None
																					):
																												return torch.device(module._hf_hook.execution_device    )
														return self.device



							@torch.no_grad()
							@replace_example_docstring(UpperCAmelCase_    )
							def __call__(			self							: int						,    UpperCAmelCase_							: Union[torch.FloatTensor, List[torch.FloatTensor]]						,    UpperCAmelCase_							: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,    UpperCAmelCase_							: Union[torch.FloatTensor, List[torch.FloatTensor]]						,    UpperCAmelCase_							: int = 512						,    UpperCAmelCase_							: int = 512						,    UpperCAmelCase_							: int = 100						,    UpperCAmelCase_							: float = 4.0						,    UpperCAmelCase_							: float = 0.3						,    UpperCAmelCase_							: int = 1						,    UpperCAmelCase_							: Optional[Union[torch.Generator, List[torch.Generator]]] = None						,    UpperCAmelCase_							: Optional[str] = "pil"						,    UpperCAmelCase_							: bool = True						,    )   ->int:



														"""simple docstring"""



														snake_case_          =						self._execution_device

														snake_case_          =						guidance_scale > 1.0

														if isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
																					snake_case_          =						torch.cat(UpperCAmelCase_						,    dim=0    )
														snake_case_          =						image_embeds.shape[0]
														if isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
																					snake_case_          =						torch.cat(UpperCAmelCase_						,    dim=0    )

														if do_classifier_free_guidance:
																					snake_case_          =						image_embeds.repeat_interleave(UpperCAmelCase_						,    dim=0    )
																					snake_case_          =						negative_image_embeds.repeat_interleave(UpperCAmelCase_						,    dim=0    )

														snake_case_          =						torch.cat([negative_image_embeds, image_embeds]						,    dim=0    ).to(dtype=self.unet.dtype						,    device=UpperCAmelCase_    )

														if not isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
																					snake_case_          =						[image]
														if not all(isinstance(UpperCAmelCase_						,    (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
																					raise ValueError(
																					    F"""Input is in incorrect format: {[type(UpperCAmelCase_    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""    )

														snake_case_          =						torch.cat([prepare_image(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    ) for i in image]						,    dim=0    )
														snake_case_          =						image.to(dtype=image_embeds.dtype						,    device=UpperCAmelCase_    )

														snake_case_          =						self.movq.encode(UpperCAmelCase_    )["""latents"""]
														snake_case_          =						latents.repeat_interleave(UpperCAmelCase_						,    dim=0    )
														self.scheduler.set_timesteps(UpperCAmelCase_						,    device=UpperCAmelCase_    )
														snake_case_    ,  snake_case_          =						self.get_timesteps(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						timesteps[:1].repeat(batch_size * num_images_per_prompt    )
														snake_case_    ,  snake_case_          =						downscale_height_and_width(UpperCAmelCase_						,    UpperCAmelCase_						,    self.movq_scale_factor    )
														snake_case_          =						self.prepare_latents(
														    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_						,    image_embeds.dtype						,    UpperCAmelCase_						,    UpperCAmelCase_    )
														for i, t in enumerate(self.progress_bar(UpperCAmelCase_    )    ):
																					# expand the latents if we are doing classifier free guidance
																					snake_case_          =						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

																					snake_case_          =						{"""image_embeds""": image_embeds}
																					snake_case_          =						self.unet(
																					    sample=UpperCAmelCase_						,    timestep=UpperCAmelCase_						,    encoder_hidden_states=UpperCAmelCase_						,    added_cond_kwargs=UpperCAmelCase_						,    return_dict=UpperCAmelCase_						,    )[0]

																					if do_classifier_free_guidance:
																												snake_case_    ,  snake_case_          =						noise_pred.split(latents.shape[1]						,    dim=1    )
																												snake_case_    ,  snake_case_          =						noise_pred.chunk(2    )
																												snake_case_    ,  snake_case_          =						variance_pred.chunk(2    )
																												snake_case_          =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																												snake_case_          =						torch.cat([noise_pred, variance_pred_text]						,    dim=1    )

																					if not (
																					    hasattr(self.scheduler.config						,    """variance_type"""    )
																					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																					):
																												snake_case_    ,  snake_case_          =						noise_pred.split(latents.shape[1]						,    dim=1    )

																					# compute the previous noisy sample x_t -> x_t-1
																					snake_case_          =						self.scheduler.step(
																					    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_						,    generator=UpperCAmelCase_						,    )[0]

														# post-processing
														snake_case_          =						self.movq.decode(UpperCAmelCase_						,    force_not_quantize=UpperCAmelCase_    )["""sample"""]

														if output_type not in ["pt", "np", "pil"]:
																					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

														if output_type in ["np", "pil"]:
																					snake_case_          =						image * 0.5 + 0.5
																					snake_case_          =						image.clamp(0						,    1    )
																					snake_case_          =						image.cpu().permute(0						,    2						,    3						,    1    ).float().numpy()

														if output_type == "pil":
																					snake_case_          =						self.numpy_to_pil(UpperCAmelCase_    )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCAmelCase_    )







"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""laion/clap-htsat-unfused"""
														snake_case_          =						tempfile.mkdtemp()



							def 						lowerCAmelCase    (			self							: List[str]						,    **UpperCAmelCase_							: Tuple    )   ->str:



														"""simple docstring"""



														return RobertaTokenizer.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: str    )   ->Any:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_feature_extractor()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														processor.save_pretrained(self.tmpdirname    )
														snake_case_          =						ClapProcessor.from_pretrained(self.tmpdirname    )

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

														self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()    )
														self.assertIsInstance(processor.feature_extractor						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()    )
														processor.save_pretrained(self.tmpdirname    )

														snake_case_          =						self.get_tokenizer(bos_token="""(BOS)"""						,    eos_token="""(EOS)"""    )
														snake_case_          =						self.get_feature_extractor(do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

														snake_case_          =						ClapProcessor.from_pretrained(
														    self.tmpdirname						,    bos_token="""(BOS)"""						,    eos_token="""(EOS)"""						,    do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

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

														self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()    )
														self.assertIsInstance(processor.feature_extractor						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						floats_list((3, 1_000)    )

														snake_case_          =						feature_extractor(UpperCAmelCase_						,    return_tensors="""np"""    )
														snake_case_          =						processor(audios=UpperCAmelCase_						,    return_tensors="""np"""    )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2    )



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						"""This is a test string"""

														snake_case_          =						processor(text=UpperCAmelCase_    )

														snake_case_          =						tokenizer(UpperCAmelCase_    )

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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														snake_case_          =						processor.batch_decode(UpperCAmelCase_    )
														snake_case_          =						tokenizer.batch_decode(UpperCAmelCase_    )

														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														self.assertListEqual(
														    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg="""`processor` and `feature_extractor` model input names do not match"""						,    )



