

import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met

class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   *A_  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										super().__init__(*A_  ,   **A_		)
										_UpperCAmelCase    :						str							=      eval_examples
										_UpperCAmelCase    :						Optional[int]							=      post_process_function


						def     _UpperCAmelCase			(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_ = "eval"		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.eval_dataset if eval_dataset is None else eval_dataset
										_UpperCAmelCase    :						List[str]							=      self.get_eval_dataloader(A_		)
										_UpperCAmelCase    :						int							=      self.eval_examples if eval_examples is None else eval_examples

										# Temporarily disable metric computation, we will do it in the loop here.
										_UpperCAmelCase    :						str							=      self.compute_metrics
										_UpperCAmelCase    :						str							=      None
										_UpperCAmelCase    :						Any							=      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										_UpperCAmelCase    :						List[Any]							=      time.time()
										try:
														_UpperCAmelCase    :						Union[str, Any]							=      eval_loop(
														    A_  ,   description="Evaluation"  ,   prediction_loss_only=True if compute_metrics is None else None  ,   ignore_keys=A_  ,   metric_key_prefix=A_  ,   )
										finally:
														_UpperCAmelCase    :						List[str]							=      compute_metrics
										_UpperCAmelCase    :						List[str]							=      self.args.eval_batch_size * self.args.world_size
										if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
														start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
										output.metrics.update(
										    speed_metrics(
										        A_  ,   A_  ,   num_samples=output.num_samples  ,   num_steps=math.ceil(output.num_samples / total_batch_size		)  ,   )		)
										if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
														# Only the main node write the results by default
														_UpperCAmelCase    :						List[Any]							=      self.post_process_function(A_  ,   A_  ,   output.predictions		)
														_UpperCAmelCase    :						List[Any]							=      self.compute_metrics(A_		)

														# Prefix all keys with metric_key_prefix + '_'
														for key in list(metrics.keys()		):
																		if not key.startswith(f'{metric_key_prefix}_'		):
																						_UpperCAmelCase    :						List[Any]							=      metrics.pop(A_		)
														metrics.update(output.metrics		)
										else:
														_UpperCAmelCase    :						List[Any]							=      output.metrics

										if self.args.should_log:
														# Only the main node log the results by default
														self.log(A_		)

										if self.args.tpu_metrics_debug or self.args.debug:
														# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
														xm.master_print(met.metrics_report()		)

										_UpperCAmelCase    :						List[Any]							=      self.callback_handler.on_evaluate(self.args  ,   self.state  ,   self.control  ,   A_		)
										return metrics





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_=None  ,   A_ = "test"		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_test_dataloader(A_		)

										# Temporarily disable metric computation, we will do it in the loop here.
										_UpperCAmelCase    :						List[str]							=      self.compute_metrics
										_UpperCAmelCase    :						Union[str, Any]							=      None
										_UpperCAmelCase    :						Tuple							=      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										_UpperCAmelCase    :						Union[str, Any]							=      time.time()
										try:
														_UpperCAmelCase    :						Optional[Any]							=      eval_loop(
														    A_  ,   description="Prediction"  ,   prediction_loss_only=True if compute_metrics is None else None  ,   ignore_keys=A_  ,   metric_key_prefix=A_  ,   )
										finally:
														_UpperCAmelCase    :						Any							=      compute_metrics
										_UpperCAmelCase    :						Tuple							=      self.args.eval_batch_size * self.args.world_size
										if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
														start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
										output.metrics.update(
										    speed_metrics(
										        A_  ,   A_  ,   num_samples=output.num_samples  ,   num_steps=math.ceil(output.num_samples / total_batch_size		)  ,   )		)

										if self.post_process_function is None or self.compute_metrics is None:
														return output

										_UpperCAmelCase    :						Dict							=      self.post_process_function(A_  ,   A_  ,   output.predictions  ,   "predict"		)
										_UpperCAmelCase    :						str							=      self.compute_metrics(A_		)

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()		):
														if not key.startswith(f'{metric_key_prefix}_'		):
																		_UpperCAmelCase    :						int							=      metrics.pop(A_		)
										metrics.update(output.metrics		)
										return PredictionOutput(predictions=predictions.predictions  ,   label_ids=predictions.label_ids  ,   metrics=A_		)





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,
)


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

SCREAMING_SNAKE_CASE_            = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					int=8	)				->    int:
				_UpperCAmelCase    :						int							=      height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase    :						List[str]							=      width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    unet=A_  ,   scheduler=A_  ,   movq=A_  ,   )
										_UpperCAmelCase    :						Optional[int]							=      2 ** (len(self.movq.config.block_out_channels		) - 1)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase    :						Any							=      randn_tensor(A_  ,   generator=A_  ,   device=A_  ,   dtype=A_		)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
														_UpperCAmelCase    :						Optional[int]							=      latents.to(A_		)

										_UpperCAmelCase    :						Tuple							=      latents * scheduler.init_noise_sigma
										return latents


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						Union[str, Any]							=      torch.device(f'cuda:{gpu_id}'		)

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


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''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."		)

										_UpperCAmelCase    :						str							=      torch.device(f'cuda:{gpu_id}'		)

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

										_UpperCAmelCase    :						Optional[int]							=      None
										for cpu_offloaded_model in [self.unet, self.movq]:
														_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      cpu_offload_with_hook(A_  ,   A_  ,   prev_module_hook=A_		)

										# We'll offload the last model manually.
										_UpperCAmelCase    :						Optional[Any]							=      hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not hasattr(self.unet  ,   "_hf_hook"		):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(A_  ,   "_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(A_		)
						def __call__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 100  ,   A_ = 4.0  ,   A_ = 1  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self._execution_device

										_UpperCAmelCase    :						Tuple							=      guidance_scale > 1.0

										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Dict							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      torch.cat(A_  ,   dim=0		)

										_UpperCAmelCase    :						Optional[Any]							=      image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
														_UpperCAmelCase    :						Optional[int]							=      image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Union[str, Any]							=      negative_image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Tuple							=      hint.repeat_interleave(A_  ,   dim=0		)

										_UpperCAmelCase    :						List[Any]							=      torch.cat([negative_image_embeds, image_embeds]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										_UpperCAmelCase    :						List[Any]							=      torch.cat([hint, hint]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										self.scheduler.set_timesteps(A_  ,   device=A_		)
										_UpperCAmelCase    :						Dict							=      self.scheduler.timesteps

										_UpperCAmelCase    :						Union[str, Any]							=      self.movq.config.latent_channels

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      downscale_height_and_width(A_  ,   A_  ,   self.movq_scale_factor		)

										# create initial latent
										_UpperCAmelCase    :						Tuple							=      self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)  ,   image_embeds.dtype  ,   A_  ,   A_  ,   A_  ,   self.scheduler  ,   )

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

														_UpperCAmelCase    :						Optional[Any]							=      {"image_embeds": image_embeds, "hint": hint}
														_UpperCAmelCase    :						Optional[int]							=      self.unet(
														    sample=A_  ,   timestep=A_  ,   encoder_hidden_states=A_  ,   added_cond_kwargs=A_  ,   return_dict=A_  ,   )[0]

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

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

										if output_type == "pil":
														_UpperCAmelCase    :						str							=      self.numpy_to_pil(A_		)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=A_		)


