



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    :		Optional[int]		      =    logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase    :		Tuple		      =    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

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



class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				Tuple       ,	snake_case    :				UNetaDConditionModel       ,	snake_case    :				DDPMScheduler       ,	snake_case    :				VQModel       ,	):



											'''simple docstring'''


											super().__init__()

											self.register_modules(
											    unet=snake_case       ,	scheduler=snake_case       ,	movq=snake_case       ,	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				2 ** (len(self.movq.config.block_out_channels	) - 1)







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Dict       ,	snake_case    :				Optional[int]       ,	snake_case    :				Dict       ,	snake_case    :				List[str]       ,	snake_case    :				int       ,	snake_case    :				Dict	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Any						=				latents * scheduler.init_noise_sigma
											return latents







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				Any=0	):



											'''simple docstring'''


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

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

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																					cpu_offload(snake_case       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]       ,	snake_case    :				Optional[int]=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.'	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.device(f'''cuda:{gpu_id}'''	)

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

											SCREAMING_SNAKE_CASE							:     Dict						=				None
											for cpu_offloaded_model in [self.unet, self.movq]:
																SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				cpu_offload_with_hook(snake_case       ,	snake_case       ,	prev_module_hook=snake_case	)

											# We'll offload the last model manually.
											SCREAMING_SNAKE_CASE							:     Any						=				hook







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



											'''simple docstring'''


											if not hasattr(self.unet       ,	'_hf_hook'	):
																return self.device
											for module in self.unet.modules():
																if (
																    hasattr(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(snake_case	)
						def __call__(  self    :				List[str]       ,	snake_case    :				Union[torch.FloatTensor, List[torch.FloatTensor]]       ,	snake_case    :				Union[torch.FloatTensor, List[torch.FloatTensor]]       ,	snake_case    :				torch.FloatTensor       ,	snake_case    :				int = 512       ,	snake_case    :				int = 512       ,	snake_case    :				int = 100       ,	snake_case    :				float = 4.0       ,	snake_case    :				int = 1       ,	snake_case    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	snake_case    :				Optional[torch.FloatTensor] = None       ,	snake_case    :				Optional[str] = "pil"       ,	snake_case    :				bool = True       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				self._execution_device

											SCREAMING_SNAKE_CASE							:     Any						=				guidance_scale > 1.0

											if isinstance(snake_case       ,	snake_case	):
																SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.cat(snake_case       ,	dim=0	)
											if isinstance(snake_case       ,	snake_case	):
																SCREAMING_SNAKE_CASE							:     List[str]						=				torch.cat(snake_case       ,	dim=0	)
											if isinstance(snake_case       ,	snake_case	):
																SCREAMING_SNAKE_CASE							:     int						=				torch.cat(snake_case       ,	dim=0	)

											SCREAMING_SNAKE_CASE							:     Any						=				image_embeds.shape[0] * num_images_per_prompt

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

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.cat([negative_image_embeds, image_embeds]       ,	dim=0	).to(dtype=self.unet.dtype       ,	device=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.cat([hint, hint]       ,	dim=0	).to(dtype=self.unet.dtype       ,	device=snake_case	)
											self.scheduler.set_timesteps(snake_case       ,	device=snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.scheduler.timesteps

											SCREAMING_SNAKE_CASE							:     int						=				self.movq.config.latent_channels

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				downscale_height_and_width(snake_case       ,	snake_case       ,	self.movq_scale_factor	)

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

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

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				{'image_embeds': image_embeds, 'hint': hint}
																SCREAMING_SNAKE_CASE							:     Any						=				self.unet(
																    sample=snake_case       ,	timestep=snake_case       ,	encoder_hidden_states=snake_case       ,	added_cond_kwargs=snake_case       ,	return_dict=snake_case       ,	)[0]

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

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

											if output_type == "pil":
																SCREAMING_SNAKE_CASE							:     int						=				self.numpy_to_pil(snake_case	)

											if not return_dict:
																return (image,)

											return ImagePipelineOutput(images=snake_case	)




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase    :		Dict		      =    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(os.path.join(self.data_dir       ,	self.data[index]['img']	)	).convert('RGB'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.transforms(snake_case	)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Any						=				torch.stack([row['image_end_token'] for row in batch]   )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        __a			(   )     ->     str:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]



def        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )