import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,			torch.Tensor							):
								return image
				elif isinstance(lowercase							,			PIL.Image.Image							):
								__lowercase 			=							[image]

				if isinstance(image[0]							,			PIL.Image.Image							):
								__lowercase 			=							[np.array(i.resize((w, h)							,			resample=PIL_INTERPOLATION['''lanczos''']							)							)[None, :] for i in image]
								__lowercase 			=							np.concatenate(lowercase							,			axis=0							)
								__lowercase 			=							np.array(lowercase							).astype(np.floataa							) / 255.0
								__lowercase 			=							image.transpose(0							,			3							,			1							,			2							)
								__lowercase 			=							2.0 * image - 1.0
								__lowercase 			=							torch.from_numpy(lowercase							)
				elif isinstance(image[0]							,			torch.Tensor							):
								__lowercase 			=							torch.cat(lowercase							,			dim=0							)
				return image





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=0.9995							):







				"""simple docstring"""



				if not isinstance(lowercase							,			np.ndarray							):
								__lowercase 			=							True
								__lowercase 			=							va.device
								__lowercase 			=							va.cpu().numpy()
								__lowercase 			=							va.cpu().numpy()

				__lowercase 			=							np.sum(va * va / (np.linalg.norm(lowercase							) * np.linalg.norm(lowercase							))							)
				if np.abs(lowercase							) > DOT_THRESHOLD:
								__lowercase 			=							(1 - t) * va + t * va
				else:
								__lowercase 			=							np.arccos(lowercase							)
								__lowercase 			=							np.sin(lowercase							)
								__lowercase 			=							theta_a * t
								__lowercase 			=							np.sin(lowercase							)
								__lowercase 			=							np.sin(theta_a - theta_t							) / sin_theta_a
								__lowercase 			=							sin_theta_t / sin_theta_a
								__lowercase 			=							sa * va + sa * va

				if inputs_are_torch:
								__lowercase 			=							torch.from_numpy(lowercase							).to(lowercase							)

				return va





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							F.normalize(lowercase							,			dim=-1							)
				__lowercase 			=							F.normalize(lowercase							,			dim=-1							)
				return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				for param in model.parameters():
								__lowercase 			=							value




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=None      ,   )     ->					str:

							'''simple docstring'''


							super().__init__()
							self.register_modules(
							    vae=lowerCAmelCase__      ,   text_encoder=lowerCAmelCase__      ,   clip_model=lowerCAmelCase__      ,   tokenizer=lowerCAmelCase__      ,   unet=lowerCAmelCase__      ,   scheduler=lowerCAmelCase__      ,   feature_extractor=lowerCAmelCase__      ,   coca_model=lowerCAmelCase__      ,   coca_tokenizer=lowerCAmelCase__      ,   coca_transform=lowerCAmelCase__      ,   )
							__lowercase 			=							(
							    feature_extractor.size
							    if isinstance(feature_extractor.size      ,   lowerCAmelCase__						)
							    else feature_extractor.size['''shortest_edge''']
							)
							__lowercase 			=							transforms.Normalize(mean=feature_extractor.image_mean      ,   std=feature_extractor.image_std						)
							set_requires_grad(self.text_encoder      ,   lowerCAmelCase__						)
							set_requires_grad(self.clip_model      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__ = "auto"						)     ->					Optional[Any]:

							'''simple docstring'''


							if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											__lowercase 			=							self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							self.enable_attention_slicing(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							set_requires_grad(self.vae      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							set_requires_grad(self.vae      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							set_requires_grad(self.unet      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							set_requires_grad(self.unet      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							min(int(num_inference_steps * strength						)      ,   lowerCAmelCase__						)

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

							return timesteps, num_inference_steps - t_start





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=None						)     ->					Optional[Any]:

							'''simple docstring'''


							if not isinstance(lowerCAmelCase__      ,   torch.Tensor						):
											raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(lowerCAmelCase__						)}"						)

							__lowercase 			=							image.to(device=lowerCAmelCase__      ,   dtype=lowerCAmelCase__						)

							if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						):
											__lowercase 			=							[
											    self.vae.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(lowerCAmelCase__						)
											]
											__lowercase 			=							torch.cat(lowerCAmelCase__      ,   dim=0						)
							else:
											__lowercase 			=							self.vae.encode(lowerCAmelCase__						).latent_dist.sample(lowerCAmelCase__						)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase 			=							0.1_8215 * init_latents
							__lowercase 			=							init_latents.repeat_interleave(lowerCAmelCase__      ,   dim=0						)

							__lowercase 			=							randn_tensor(init_latents.shape      ,   generator=lowerCAmelCase__      ,   device=lowerCAmelCase__      ,   dtype=lowerCAmelCase__						)

							# get latents
							__lowercase 			=							self.scheduler.add_noise(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							init_latents

							return latents





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.coca_transform(lowerCAmelCase__						).unsqueeze(0						)
							with torch.no_grad(), torch.cuda.amp.autocast():
											__lowercase 			=							self.coca_model.generate(transformed_image.to(device=self.device      ,   dtype=self.coca_model.dtype						)						)
							__lowercase 			=							self.coca_tokenizer.decode(generated[0].cpu().numpy()						)
							return generated.split('''<end_of_text>'''						)[0].replace('''<start_of_text>'''      ,   ''''''						).rstrip(''' .,'''						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							self.feature_extractor.preprocess(lowerCAmelCase__						)
							__lowercase 			=							torch.from_numpy(clip_image_input['''pixel_values'''][0]						).unsqueeze(0						).to(self.device						).half()
							__lowercase 			=							self.clip_model.get_image_features(lowerCAmelCase__						)
							__lowercase 			=							image_embeddings_clip / image_embeddings_clip.norm(p=2      ,   dim=-1      ,   keepdim=lowerCAmelCase__						)
							__lowercase 			=							image_embeddings_clip.repeat_interleave(lowerCAmelCase__      ,   dim=0						)
							return image_embeddings_clip





			@torch.enable_grad()
			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   )     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							latents.detach().requires_grad_()

							__lowercase 			=							self.scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

							# predict the noise residual
							__lowercase 			=							self.unet(lowerCAmelCase__      ,   lowerCAmelCase__      ,   encoder_hidden_states=lowerCAmelCase__						).sample

							if isinstance(self.scheduler      ,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
											__lowercase 			=							self.scheduler.alphas_cumprod[timestep]
											__lowercase 			=							1 - alpha_prod_t
											# compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											__lowercase 			=							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

											__lowercase 			=							torch.sqrt(lowerCAmelCase__						)
											__lowercase 			=							pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler      ,   lowerCAmelCase__						):
											__lowercase 			=							self.scheduler.sigmas[index]
											__lowercase 			=							latents - sigma * noise_pred
							else:
											raise ValueError(F"scheduler type {type(self.scheduler						)} not supported"						)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase 			=							1 / 0.1_8215 * sample
							__lowercase 			=							self.vae.decode(lowerCAmelCase__						).sample
							__lowercase 			=							(image / 2 + 0.5).clamp(0      ,   1						)

							__lowercase 			=							transforms.Resize(self.feature_extractor_size						)(lowerCAmelCase__						)
							__lowercase 			=							self.normalize(lowerCAmelCase__						).to(latents.dtype						)

							__lowercase 			=							self.clip_model.get_image_features(lowerCAmelCase__						)
							__lowercase 			=							image_embeddings_clip / image_embeddings_clip.norm(p=2      ,   dim=-1      ,   keepdim=lowerCAmelCase__						)

							__lowercase 			=							spherical_dist_loss(lowerCAmelCase__      ,   lowerCAmelCase__						).mean() * clip_guidance_scale

							__lowercase 			=							-torch.autograd.grad(lowerCAmelCase__      ,   lowerCAmelCase__						)[0]

							if isinstance(self.scheduler      ,   lowerCAmelCase__						):
											__lowercase 			=							latents.detach() + grads * (sigma**2)
											__lowercase 			=							noise_pred_original
							else:
											__lowercase 			=							noise_pred_original - torch.sqrt(lowerCAmelCase__						) * grads
							return noise_pred, latents





			@torch.no_grad()
			def __call__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = 5_12      ,   lowerCAmelCase__ = 5_12      ,   lowerCAmelCase__ = 0.6      ,   lowerCAmelCase__ = 50      ,   lowerCAmelCase__ = 7.5      ,   lowerCAmelCase__ = 1      ,   lowerCAmelCase__ = 0.0      ,   lowerCAmelCase__ = 1_00      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = "pil"      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = 0.8      ,   lowerCAmelCase__ = 0.1      ,   lowerCAmelCase__ = 0.1      ,   )     ->					List[Any]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						) and len(lowerCAmelCase__						) != batch_size:
											raise ValueError(F"You have passed {batch_size} batch_size, but only {len(lowerCAmelCase__						)} generators."						)

							if height % 8 != 0 or width % 8 != 0:
											raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."						)

							if isinstance(lowerCAmelCase__      ,   torch.Generator						) and batch_size > 1:
											__lowercase 			=							[generator] + [None] * (batch_size - 1)

							__lowercase 			=							[
							    ('''model''', self.coca_model is None),
							    ('''tokenizer''', self.coca_tokenizer is None),
							    ('''transform''', self.coca_transform is None),
							]
							__lowercase 			=							[x[0] for x in coca_is_none if x[1]]
							__lowercase 			=							''', '''.join(lowerCAmelCase__						)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
											if len(lowerCAmelCase__						):
															raise ValueError(
															    F"Content prompt is None and CoCa [{coca_is_none_str}] is None."
															    F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."						)
											__lowercase 			=							self.get_image_description(lowerCAmelCase__						)
							if style_prompt is None:
											if len(lowerCAmelCase__						):
															raise ValueError(
															    F"Style prompt is None and CoCa [{coca_is_none_str}] is None."
															    F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."						)
											__lowercase 			=							self.get_image_description(lowerCAmelCase__						)

							# get prompt text embeddings for content and style
							__lowercase 			=							self.tokenizer(
							    lowerCAmelCase__      ,   padding='''max_length'''      ,   max_length=self.tokenizer.model_max_length      ,   truncation=lowerCAmelCase__      ,   return_tensors='''pt'''      ,   )
							__lowercase 			=							self.text_encoder(content_text_input.input_ids.to(self.device						)						)[0]

							__lowercase 			=							self.tokenizer(
							    lowerCAmelCase__      ,   padding='''max_length'''      ,   max_length=self.tokenizer.model_max_length      ,   truncation=lowerCAmelCase__      ,   return_tensors='''pt'''      ,   )
							__lowercase 			=							self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

							__lowercase 			=							slerp(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

							# duplicate text embeddings for each generation per prompt
							__lowercase 			=							text_embeddings.repeat_interleave(lowerCAmelCase__      ,   dim=0						)

							# set timesteps
							__lowercase 			=							'''offset''' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
							__lowercase 			=							{}
							if accepts_offset:
											__lowercase 			=							1

							self.scheduler.set_timesteps(lowerCAmelCase__      ,   **lowerCAmelCase__						)
							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							self.scheduler.timesteps.to(self.device						)

							__lowercase			,     __lowercase 			=							self.get_timesteps(lowerCAmelCase__      ,   lowerCAmelCase__      ,   self.device						)
							__lowercase 			=							timesteps[:1].repeat(lowerCAmelCase__						)

							# Preprocess image
							__lowercase 			=							preprocess(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							self.prepare_latents(
							    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   text_embeddings.dtype      ,   self.device      ,   lowerCAmelCase__						)

							__lowercase 			=							preprocess(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							__lowercase 			=							self.prepare_latents(
							    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   text_embeddings.dtype      ,   self.device      ,   lowerCAmelCase__						)

							__lowercase 			=							slerp(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

							if clip_guidance_scale > 0:
											__lowercase 			=							self.get_clip_image_embeddings(lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							self.get_clip_image_embeddings(lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							slerp(
											    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							__lowercase 			=							guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
											__lowercase 			=							content_text_input.input_ids.shape[-1]
											__lowercase 			=							self.tokenizer(['''''']      ,   padding='''max_length'''      ,   max_length=lowerCAmelCase__      ,   return_tensors='''pt'''						)
											__lowercase 			=							self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
											# duplicate unconditional embeddings for each generation per prompt
											__lowercase 			=							uncond_embeddings.repeat_interleave(lowerCAmelCase__      ,   dim=0						)

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											__lowercase 			=							torch.cat([uncond_embeddings, text_embeddings]						)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							__lowercase 			=							(batch_size, self.unet.config.in_channels, height // 8, width // 8)
							__lowercase 			=							text_embeddings.dtype
							if latents is None:
											if self.device.type == "mps":
															# randn does not work reproducibly on mps
															__lowercase 			=							torch.randn(lowerCAmelCase__      ,   generator=lowerCAmelCase__      ,   device='''cpu'''      ,   dtype=lowerCAmelCase__						).to(
															    self.device						)
											else:
															__lowercase 			=							torch.randn(lowerCAmelCase__      ,   generator=lowerCAmelCase__      ,   device=self.device      ,   dtype=lowerCAmelCase__						)
							else:
											if latents.shape != latents_shape:
															raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"						)
											__lowercase 			=							latents.to(self.device						)

							# scale the initial noise by the standard deviation required by the scheduler
							__lowercase 			=							latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							__lowercase 			=							'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
							__lowercase 			=							{}
							if accepts_eta:
											__lowercase 			=							eta

							# check if the scheduler accepts generator
							__lowercase 			=							'''generator''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
							if accepts_generator:
											__lowercase 			=							generator

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

															# predict the noise residual
															__lowercase 			=							self.unet(lowerCAmelCase__      ,   lowerCAmelCase__      ,   encoder_hidden_states=lowerCAmelCase__						).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																			__lowercase			,     __lowercase 			=							noise_pred.chunk(2						)
																			__lowercase 			=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																			__lowercase 			=							(
																			    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
																			)
																			__lowercase			,     __lowercase 			=							self.cond_fn(
																			    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   )

															# compute the previous noisy sample x_t -> x_t-1
															__lowercase 			=							self.scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   **lowerCAmelCase__						).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							__lowercase 			=							1 / 0.1_8215 * latents
							__lowercase 			=							self.vae.decode(lowerCAmelCase__						).sample

							__lowercase 			=							(image / 2 + 0.5).clamp(0      ,   1						)
							__lowercase 			=							image.cpu().permute(0      ,   2      ,   3      ,   1						).numpy()

							if output_type == "pil":
											__lowercase 			=							self.numpy_to_pil(lowerCAmelCase__						)

							if not return_dict:
											return (image, None)

							return StableDiffusionPipelineOutput(images=lowerCAmelCase__      ,   nsfw_content_detected=lowerCAmelCase__						)
from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)