"""simple docstring"""




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase    :							List[str]													=						logging.get_logger(__name__)  # pylint: disable=invalid-name







def 							_UpperCAmelCase		(       lowerCamelCase__     )     ->   int:





		"""simple docstring"""




		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""			,  _A			,  )
		if isinstance(_A			,  torch.Tensor     ):
				return image
		elif isinstance(_A			,  PIL.Image.Image     ):
				lowerCAmelCase__        =   [image]

		if isinstance(image[0]			,  PIL.Image.Image     ):
				lowerCAmelCase__   ,					lowerCAmelCase__        =   image[0].size
				lowerCAmelCase__   ,					lowerCAmelCase__        =   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				lowerCAmelCase__        =   [np.array(i.resize((w, h)			,  resample=PIL_INTERPOLATION["""lanczos"""]     )     )[None, :] for i in image]
				lowerCAmelCase__        =   np.concatenate(_A			,  axis=0     )
				lowerCAmelCase__        =   np.array(_A     ).astype(np.floataa     ) / 2_55.0
				lowerCAmelCase__        =   image.transpose(0			,  3			,  1			,  2     )
				lowerCAmelCase__        =   2.0 * image - 1.0
				lowerCAmelCase__        =   torch.from_numpy(_A     )
		elif isinstance(image[0]			,  torch.Tensor     ):
				lowerCAmelCase__        =   torch.cat(_A			,  dim=0     )
		return image







def 							_UpperCAmelCase		(       lowerCamelCase__     )     ->   Tuple:





		"""simple docstring"""




		if isinstance(_A			,  torch.Tensor     ):
				return mask
		elif isinstance(_A			,  PIL.Image.Image     ):
				lowerCAmelCase__        =   [mask]

		if isinstance(mask[0]			,  PIL.Image.Image     ):
				lowerCAmelCase__   ,					lowerCAmelCase__        =   mask[0].size
				lowerCAmelCase__   ,					lowerCAmelCase__        =   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowerCAmelCase__        =   [np.array(m.convert("""L"""     ).resize((w, h)			,  resample=PIL_INTERPOLATION["""nearest"""]     )     )[None, :] for m in mask]
				lowerCAmelCase__        =   np.concatenate(_A			,  axis=0     )
				lowerCAmelCase__        =   mask.astype(np.floataa     ) / 2_55.0
				lowerCAmelCase__        =   0
				lowerCAmelCase__        =   1
				lowerCAmelCase__        =   torch.from_numpy(_A     )
		elif isinstance(mask[0]			,  torch.Tensor     ):
				lowerCAmelCase__        =   torch.cat(_A			,  dim=0     )
		return mask






class       a_      (     _UpperCAmelCase			):
					UpperCamelCase_ :	UNetaDModel
					UpperCamelCase_ :	RePaintScheduler




					def __init__(	self						:     List[str]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     str     ):
							super().__init__()
							self.register_modules(unet=__UpperCamelCase  ,						scheduler=__UpperCamelCase     )




					@torch.no_grad()
					def __call__(	self						:     Any  ,						snake_case__						:     Union[torch.Tensor, PIL.Image.Image]  ,						snake_case__						:     Union[torch.Tensor, PIL.Image.Image]  ,						snake_case__						:     int = 250  ,						snake_case__						:     float = 0.0  ,						snake_case__						:     int = 10  ,						snake_case__						:     int = 10  ,						snake_case__						:     Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,						snake_case__						:     Optional[str] = "pil"  ,						snake_case__						:     bool = True  ,						):
							lowerCAmelCase__        =   image

							lowerCAmelCase__        =   _preprocess_image(__UpperCamelCase     )
							lowerCAmelCase__        =   original_image.to(device=self.device  ,						dtype=self.unet.dtype     )
							lowerCAmelCase__        =   _preprocess_mask(__UpperCamelCase     )
							lowerCAmelCase__        =   mask_image.to(device=self.device  ,						dtype=self.unet.dtype     )

							lowerCAmelCase__        =   original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(__UpperCamelCase  ,						__UpperCamelCase     ) and len(__UpperCamelCase     ) != batch_size:
									raise ValueError(
									    F"""You have passed a list of generators of length {len(__UpperCamelCase     )}, but requested an effective batch"""
									    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

							lowerCAmelCase__        =   original_image.shape
							lowerCAmelCase__        =   randn_tensor(__UpperCamelCase  ,						generator=__UpperCamelCase  ,						device=self.device  ,						dtype=self.unet.dtype     )

							# set step values
							self.scheduler.set_timesteps(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						self.device     )
							lowerCAmelCase__        =   eta

							lowerCAmelCase__        =   self.scheduler.timesteps[0] + 1
							lowerCAmelCase__        =   generator[0] if isinstance(__UpperCamelCase  ,						__UpperCamelCase     ) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
									if t < t_last:
											# predict the noise residual
											lowerCAmelCase__        =   self.unet(__UpperCamelCase  ,						__UpperCamelCase     ).sample
											# compute previous image: x_t -> x_t-1
											lowerCAmelCase__        =   self.scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase     ).prev_sample

									else:
											# compute the reverse: x_t-1 -> x_t
											lowerCAmelCase__        =   self.scheduler.undo_step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase     )
									lowerCAmelCase__        =   t

							lowerCAmelCase__        =   (image / 2 + 0.5).clamp(0  ,						1     )
							lowerCAmelCase__        =   image.cpu().permute(0  ,						2  ,						3  ,						1     ).numpy()
							if output_type == "pil":
									lowerCAmelCase__        =   self.numpy_to_pil(__UpperCamelCase     )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase     )





"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     List[Any]=2  ,						snake_case__						:     Any=3  ,						snake_case__						:     Union[str, Any]=64  ,						snake_case__						:     Any=None     ):
							lowerCAmelCase__        =   np.random.default_rng(snake_case__     )
							lowerCAmelCase__        =   length
							lowerCAmelCase__        =   rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase__        =   a * self.x + b + rng.normal(scale=0.1  ,						size=(length,)     ).astype(np.floataa     )




					def __len__(	self						:     Optional[Any]     ):
							return self.length




					def __getitem__(	self						:     List[str]  ,						snake_case__						:     Optional[int]     ):
							return {"x": self.x[i], "y": self.y[i]}

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     str=0  ,						snake_case__						:     Dict=0  ,						snake_case__						:     Any=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a[0] + self.b[0]

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     Any  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     List[Any]=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a + self.b







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = 16     ):





		"""simple docstring"""




		from datasets import load_dataset
		from transformers import AutoTokenizer

		lowerCAmelCase__        =   AutoTokenizer.from_pretrained("""bert-base-cased"""     )
		lowerCAmelCase__        =   {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
		lowerCAmelCase__        =   load_dataset("""csv"""			,  data_files=lowerCamelCase__     )
		lowerCAmelCase__        =   datasets["""train"""].unique("""label"""     )

		lowerCAmelCase__        =   {v: i for i, v in enumerate(lowerCamelCase__     )}

		def tokenize_function(lowerCamelCase__     ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__        =   tokenizer(
				    examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCamelCase__			,  max_length=lowerCamelCase__			,  padding="""max_length"""     )
				if "label" in examples:
						lowerCAmelCase__        =   [label_to_id[l] for l in examples["""label"""]]
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase__        =   datasets.map(
		    lowerCamelCase__			,  batched=lowerCamelCase__			,  remove_columns=["""sentence1""", """sentence2""", """label"""]			,  )

		def collate_fn(lowerCamelCase__     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(lowerCamelCase__			,  padding="""max_length"""			,  max_length=128			,  return_tensors="""pt"""     )
				return tokenizer.pad(lowerCamelCase__			,  padding="""longest"""			,  return_tensors="""pt"""     )

		# Instantiate dataloaders.
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""train"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=2     )
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""validation"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=1     )

		return train_dataloader, eval_dataloader




