
'''simple docstring'''





from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase    					=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"

def      A (       A_							:					Union[str, Any]    ,							A_							:					str    ,							A_							:					Union[str, Any]=8       ):
						snake_case       :     List[Any] =    height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						snake_case       :     Union[str, Any] =    width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				UNetaDConditionModel,    SCREAMING_SNAKE_CASE_	:				DDPMScheduler,    SCREAMING_SNAKE_CASE_	:				VQModel,    ):
										super().__init__()

										self.register_modules(
										    unet=SCREAMING_SNAKE_CASE_,    scheduler=SCREAMING_SNAKE_CASE_,    movq=SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Tuple =    2 ** (len(self.movq.config.block_out_channels      ) - 1)




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										if latents is None:
																snake_case       :     Optional[Any] =    randn_tensor(SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    device=SCREAMING_SNAKE_CASE_,    dtype=SCREAMING_SNAKE_CASE_      )
										else:
																if latents.shape != shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
																snake_case       :     Optional[int] =    latents.to(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    latents * scheduler.init_noise_sigma
										return latents




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict=0      ):
										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

										snake_case       :     Optional[Any] =    torch.device(F"""cuda:{gpu_id}"""      )

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




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any]=0      ):
										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       :     Union[str, Any] =    torch.device(F"""cuda:{gpu_id}"""      )

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

										snake_case       :     Tuple =    None
										for cpu_offloaded_model in [self.unet, self.movq]:
																snake_case,					snake_case       :     List[str] =    cpu_offload_with_hook(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    prev_module_hook=SCREAMING_SNAKE_CASE_      )

										# We'll offload the last model manually.
										snake_case       :     int =    hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 	__snake_case							(							self	:				Tuple      ):
										if not hasattr(self.unet,    '''_hf_hook'''      ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_,    '''_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(SCREAMING_SNAKE_CASE_      )
				def __call__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Union[torch.FloatTensor, List[torch.FloatTensor]],    SCREAMING_SNAKE_CASE_	:				Union[torch.FloatTensor, List[torch.FloatTensor]],    SCREAMING_SNAKE_CASE_	:				int = 5_12,    SCREAMING_SNAKE_CASE_	:				int = 5_12,    SCREAMING_SNAKE_CASE_	:				int = 1_00,    SCREAMING_SNAKE_CASE_	:				float = 4.0,    SCREAMING_SNAKE_CASE_	:				int = 1,    SCREAMING_SNAKE_CASE_	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[torch.FloatTensor] = None,    SCREAMING_SNAKE_CASE_	:				Optional[str] = "pil",    SCREAMING_SNAKE_CASE_	:				bool = True,    ):
										snake_case       :     Any =    self._execution_device

										snake_case       :     Dict =    guidance_scale > 1.0

										if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     int =    torch.cat(SCREAMING_SNAKE_CASE_,    dim=0      )
										snake_case       :     int =    image_embeds.shape[0] * num_images_per_prompt
										if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Optional[Any] =    torch.cat(SCREAMING_SNAKE_CASE_,    dim=0      )

										if do_classifier_free_guidance:
																snake_case       :     Optional[Any] =    image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,    dim=0      )
																snake_case       :     List[Any] =    negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,    dim=0      )

										snake_case       :     List[str] =    torch.cat([negative_image_embeds, image_embeds],    dim=0      ).to(dtype=self.unet.dtype,    device=SCREAMING_SNAKE_CASE_      )

										self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,    device=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Dict =    self.scheduler.timesteps

										snake_case       :     Tuple =    self.unet.config.in_channels

										snake_case,					snake_case       :     Any =    downscale_height_and_width(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    self.movq_scale_factor      )

										# create initial latent
										snake_case       :     Any =    self.prepare_latents(
										    (batch_size, num_channels_latents, height, width),    image_embeds.dtype,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    self.scheduler,    )

										for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_      )      ):
																# expand the latents if we are doing classifier free guidance
																snake_case       :     Optional[Any] =    torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

																snake_case       :     int =    {'''image_embeds''': image_embeds}
																snake_case       :     List[str] =    self.unet(
																    sample=SCREAMING_SNAKE_CASE_,    timestep=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    added_cond_kwargs=SCREAMING_SNAKE_CASE_,    return_dict=SCREAMING_SNAKE_CASE_,    )[0]

																if do_classifier_free_guidance:
																						snake_case,					snake_case       :     int =    noise_pred.split(latents.shape[1],    dim=1      )
																						snake_case,					snake_case       :     List[Any] =    noise_pred.chunk(2      )
																						snake_case,					snake_case       :     int =    variance_pred.chunk(2      )
																						snake_case       :     Optional[int] =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						snake_case       :     Union[str, Any] =    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       :     str =    noise_pred.split(latents.shape[1],    dim=1      )

																# compute the previous noisy sample x_t -> x_t-1
																snake_case       :     Dict =    self.scheduler.step(
																    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    )[0]
										# post-processing
										snake_case       :     List[Any] =    self.movq.decode(SCREAMING_SNAKE_CASE_,    force_not_quantize=SCREAMING_SNAKE_CASE_      )['''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       :     Optional[int] =    image * 0.5 + 0.5
																snake_case       :     Optional[int] =    image.clamp(0,    1      )
																snake_case       :     int =    image.cpu().permute(0,    2,    3,    1      ).float().numpy()

										if output_type == "pil":
																snake_case       :     List[Any] =    self.numpy_to_pil(SCREAMING_SNAKE_CASE_      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )



'''simple docstring'''





import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def      A (       A_							:					str       ):
						monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings'''    ,							set()       )

@pytest.fixture
def      A (       A_							:					Optional[Any]       ):

						class 						a   :




										def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     List[str] =    metric_id




						class 						a   :
										_snake_case		    = [MetricMock(__magic_name__    ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]




										def 	__snake_case							(							self	:				str      ):
																return self._metrics




						monkeypatch.setattr('''datasets.inspect.huggingface_hub'''    ,							HfhMock()       )





@pytest.mark.parametrize(
    '''func, args'''    ,							[(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))]       )
def      A (       A_							:					str    ,							A_							:					Dict    ,							A_							:					int    ,							A_							:					int    ,							A_							:					str       ):
						if "tmp_path" in args:
												snake_case       :     List[str] =    tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args       )
						with pytest.warns(A_    ,							match='''https://huggingface.co/docs/evaluate'''       ):
												func(*A_       )

