






import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
lowerCamelCase_		  =						"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
lowerCamelCase_		  =						"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
lowerCamelCase_		  =						""""""

# TODO: Add the licence for the dataset here if you can find it
lowerCamelCase_		  =						""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
lowerCamelCase_		  =						{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}




class 							a_				(		datasets.GeneratorBasedBuilder	):


							'''simple docstring'''






							__a:			Optional[int]									=					datasets.Version('''1.1.0'''	)

							# This is an example of a dataset with multiple configurations.
							# If you don't want/need to define several sub-sets in your dataset,
							# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

							# If you need to make complex sub-parts in the datasets with configurable options
							# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
							# BUILDER_CONFIG_CLASS = MyBuilderConfig

							# You will be able to load one or the other configurations in the following list with
							# data = datasets.load_dataset('my_dataset', 'first_domain')
							# data = datasets.load_dataset('my_dataset', 'second_domain')
							__a:			int									=					[
							    datasets.BuilderConfig(name='''first_domain'''			,			version=a_			,			description='''This part of my dataset covers a first domain'''	),
							    datasets.BuilderConfig(name='''second_domain'''			,			version=a_			,			description='''This part of my dataset covers a second domain'''	),
							]

							__a:			str									=					'''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.

							def   _lowercase (	self     )      ->      Tuple:
														'''simple docstring'''

														if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
																					lowerCAmelCase_       		=  datasets.Features(
																					    {
																					        'sentence': datasets.Value('string'     ),
																					        'option1': datasets.Value('string'     ),
																					        'answer': datasets.Value('string'     )
																					        # These are the features of your dataset like images, labels ...
																					    }     )
														else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
																					lowerCAmelCase_       		=  datasets.Features(
																					    {
																					        'sentence': datasets.Value('string'     ),
																					        'option2': datasets.Value('string'     ),
																					        'second_domain_answer': datasets.Value('string'     )
																					        # These are the features of your dataset like images, labels ...
																					    }     )
														return datasets.DatasetInfo(
														    # This is the description that will appear on the datasets page.
														    description=_DESCRIPTION    ,    features=lowercase_    ,    homepage=_HOMEPAGE    ,    license=_LICENSE    ,    citation=_CITATION    ,    )

							def   _lowercase (	self    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  _URLS[self.config.name]
														lowerCAmelCase_       		=  dl_manager.download_and_extract(lowercase_     )
														return [
														    datasets.SplitGenerator(
														        name=datasets.Split.TRAIN    ,    gen_kwargs={
														            'filepath': os.path.join(lowercase_    ,    'train.jsonl'     ),
														            'split': 'train',
														        }    ,    ),
														    datasets.SplitGenerator(
														        name=datasets.Split.VALIDATION    ,    gen_kwargs={
														            'filepath': os.path.join(lowercase_    ,    'dev.jsonl'     ),
														            'split': 'dev',
														        }    ,    ),
														    datasets.SplitGenerator(
														        name=datasets.Split.TEST    ,    gen_kwargs={
														            'filepath': os.path.join(lowercase_    ,    'test.jsonl'     ),
														            'split': 'test'
														        }    ,    ),
														]






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														with open(lowercase_    ,    encoding='utf-8'     ) as f:
																					for key, row in enumerate(lowercase_     ):
																												lowerCAmelCase_       		=  json.loads(lowercase_     )
																												if self.config.name == "first_domain":
																																			# Yields examples as (key, example) tuples
																																			yield key, {
																																			    "sentence": data["sentence"],
																																			    "option1": data["option1"],
																																			    "answer": "" if split == "test" else data["answer"],
																																			}
																												else:
																																			yield key, {
																																			    "sentence": data["sentence"],
																																			    "option2": data["option2"],
																																			    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
																																			}











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,
)


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

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

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""
def   lowerCamelCase      (		a_				,		a_				,		a_=8			)			->    Tuple:
							lowerCAmelCase_       		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCAmelCase_       		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
def   lowerCamelCase      (		a_				,		a_=512				,		a_=512			)			->    Any:
							lowerCAmelCase_       		=  pil_image.resize((w, h)				,		resample=Image.BICUBIC				,		reducing_gap=1			)
							lowerCAmelCase_       		=  np.array(pil_image.convert('RGB'			)			)
							lowerCAmelCase_       		=  arr.astype(np.floataa			) / 127.5 - 1
							lowerCAmelCase_       		=  np.transpose(a_				,		[2, 0, 1]			)
							lowerCAmelCase_       		=  torch.from_numpy(a_			).unsqueeze(0			)
							return image




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      List[Any]:
														'''simple docstring'''

														super().__init__()

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

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  min(int(num_inference_steps * strength     )    ,    lowercase_     )

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

														return timesteps, num_inference_steps - t_start

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_=None     )      ->      str:
														'''simple docstring'''

														if not isinstance(lowercase_    ,    (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(lowercase_     )}'''     )

														lowerCAmelCase_       		=  image.to(device=lowercase_    ,    dtype=lowercase_     )

														lowerCAmelCase_       		=  batch_size * num_images_per_prompt

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

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

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

																					lowerCAmelCase_       		=  self.movq.config.scaling_factor * init_latents

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

														lowerCAmelCase_       		=  init_latents.shape
														lowerCAmelCase_       		=  randn_tensor(lowercase_    ,    generator=lowercase_    ,    device=lowercase_    ,    dtype=lowercase_     )

														# get latents
														lowerCAmelCase_       		=  self.scheduler.add_noise(lowercase_    ,    lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  init_latents

														return latents

							def   _lowercase (	self    ,    lowercase_=0     )      ->      int:
														'''simple docstring'''

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

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

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

							def   _lowercase (	self    ,    lowercase_=0     )      ->      Union[str, Any]:
														'''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.'     )

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

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

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

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

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														if not hasattr(self.unet    ,    '_hf_hook'     ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowercase_    ,    '_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(lowercase_     )
							def __call__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_1_2    ,    lowercase_ = 1_0_0    ,    lowercase_ = 4.0    ,    lowercase_ = 0.3    ,    lowercase_ = 1    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    )      ->      int:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._execution_device

														lowerCAmelCase_       		=  guidance_scale > 1.0

														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  image_embeds.shape[0]

														if do_classifier_free_guidance:
																					lowerCAmelCase_       		=  image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  negative_image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  hint.repeat_interleave(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  torch.cat([negative_image_embeds, image_embeds]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )
														lowerCAmelCase_       		=  torch.cat([hint, hint]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )

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

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

														lowerCAmelCase_       		=  self.movq.encode(lowercase_     )['latents']
														lowerCAmelCase_       		=  latents.repeat_interleave(lowercase_    ,    dim=0     )
														self.scheduler.set_timesteps(lowercase_    ,    device=lowercase_     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.get_timesteps(lowercase_    ,    lowercase_    ,    lowercase_     )
														lowerCAmelCase_       		=  timesteps[:1].repeat(batch_size * num_images_per_prompt     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  downscale_height_and_width(lowercase_    ,    lowercase_    ,    self.movq_scale_factor     )
														lowerCAmelCase_       		=  self.prepare_latents(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    image_embeds.dtype    ,    lowercase_    ,    lowercase_     )
														for i, t in enumerate(self.progress_bar(lowercase_     )     ):
																					# expand the latents if we are doing classifier free guidance
																					lowerCAmelCase_       		=  torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

																					lowerCAmelCase_       		=  {'image_embeds': image_embeds, 'hint': hint}
																					lowerCAmelCase_       		=  self.unet(
																					    sample=lowercase_    ,    timestep=lowercase_    ,    encoder_hidden_states=lowercase_    ,    added_cond_kwargs=lowercase_    ,    return_dict=lowercase_    ,    )[0]

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

																					# compute the previous noisy sample x_t -> x_t-1

																					lowerCAmelCase_       		=  self.scheduler.step(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    generator=lowercase_    ,    )[0]

														# post-processing
														lowerCAmelCase_       		=  self.movq.decode(lowercase_    ,    force_not_quantize=lowercase_     )['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"]:
																					lowerCAmelCase_       		=  image * 0.5 + 0.5
																					lowerCAmelCase_       		=  image.clamp(0    ,    1     )
																					lowerCAmelCase_       		=  image.cpu().permute(0    ,    2    ,    3    ,    1     ).float().numpy()

														if output_type == "pil":
																					lowerCAmelCase_       		=  self.numpy_to_pil(lowercase_     )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_     )



