


"""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,
)


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

_lowerCamelCase     =  '\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 __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[int]=8  ):
				SCREAMING_SNAKE_CASE__     							=				height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				SCREAMING_SNAKE_CASE__     							=				width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								super().__init__()

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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								if latents is None:
												SCREAMING_SNAKE_CASE__     							=				randn_tensor(UpperCAmelCase__       ,		generator=UpperCAmelCase__       ,		device=UpperCAmelCase__       ,		dtype=UpperCAmelCase__     )
								else:
												if latents.shape != shape:
																raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
												SCREAMING_SNAKE_CASE__     							=				latents.to(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				latents * scheduler.init_noise_sigma
								return latents



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=0     ):
								if is_accelerate_available():
												from accelerate import cpu_offload
								else:
												raise ImportError("Please install accelerate via `pip install accelerate`"     )

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

								SCREAMING_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       ,		UpperCAmelCase__=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."     )

								SCREAMING_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)

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

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



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   lowerCAmelCase__		(						self     ):
								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       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 100       ,		UpperCAmelCase__ = 4.0       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "pil"       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				self._execution_device

								SCREAMING_SNAKE_CASE__     							=				guidance_scale > 1.0

								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )

								SCREAMING_SNAKE_CASE__     							=				image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
												SCREAMING_SNAKE_CASE__     							=				image_embeds.repeat_interleave(UpperCAmelCase__       ,		dim=0     )
												SCREAMING_SNAKE_CASE__     							=				negative_image_embeds.repeat_interleave(UpperCAmelCase__       ,		dim=0     )
												SCREAMING_SNAKE_CASE__     							=				hint.repeat_interleave(UpperCAmelCase__       ,		dim=0     )

								SCREAMING_SNAKE_CASE__     							=				torch.cat([negative_image_embeds, image_embeds]       ,		dim=0     ).to(dtype=self.unet.dtype       ,		device=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.cat([hint, hint]       ,		dim=0     ).to(dtype=self.unet.dtype       ,		device=UpperCAmelCase__     )
								self.scheduler.set_timesteps(UpperCAmelCase__       ,		device=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.scheduler.timesteps

								SCREAMING_SNAKE_CASE__     							=				self.movq.config.latent_channels

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				downscale_height_and_width(UpperCAmelCase__       ,		UpperCAmelCase__       ,		self.movq_scale_factor     )

								# create initial latent
								SCREAMING_SNAKE_CASE__     							=				self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)       ,		image_embeds.dtype       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		self.scheduler       ,		)

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

												SCREAMING_SNAKE_CASE__     							=				{"image_embeds": image_embeds, "hint": hint}
												SCREAMING_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:
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				noise_pred.split(latents.shape[1]       ,		dim=1     )
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				noise_pred.chunk(2     )
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				variance_pred.chunk(2     )
																SCREAMING_SNAKE_CASE__     							=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																SCREAMING_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"]
												):
																SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				noise_pred.split(latents.shape[1]       ,		dim=1     )

												# compute the previous noisy sample x_t -> x_t-1
												SCREAMING_SNAKE_CASE__     							=				self.scheduler.step(
												    UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		generator=UpperCAmelCase__       ,		)[0]
								# post-processing
								SCREAMING_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"]:
												SCREAMING_SNAKE_CASE__     							=				image * 0.5 + 0.5
												SCREAMING_SNAKE_CASE__     							=				image.clamp(0       ,		1     )
												SCREAMING_SNAKE_CASE__     							=				image.cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								if output_type == "pil":
												SCREAMING_SNAKE_CASE__     							=				self.numpy_to_pil(UpperCAmelCase__     )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=UpperCAmelCase__     )




"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
				_lowerCAmelCase      :      str      							=					field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
				_lowerCAmelCase      :      int      							=					field(
				    default=128       ,  metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								warnings.warn(
								    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
								    "library. You can have a look at this example script for pointers: "
								    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir       ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																logger.info(
																    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,		time.time() - start     )
												else:
																logger.info(f'''Creating features from dataset file at {args.data_dir}'''     )

																if mode == Split.dev:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																logger.info(
																    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''     )



				def __len__(						self     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
