



'''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


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







def 						__UpperCamelCase				(							_lowercase					) ->			Any:
	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead',				__lowerCAmelCase,				)
	if isinstance(__lowerCAmelCase,				torch.Tensor					):
		return image
	elif isinstance(__lowerCAmelCase,				PIL.Image.Image					):
		_lowercase					:       Optional[Any]       =		[image]

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

		_lowercase					:       int       =		[np.array(i.resize((w, h),				resample=PIL_INTERPOLATION['lanczos']					)					)[None, :] for i in image]
		_lowercase					:       Dict       =		np.concatenate(__lowerCAmelCase,				axis=0					)
		_lowercase					:       Tuple       =		np.array(__lowerCAmelCase					).astype(np.floataa					) / 2_5_5.0
		_lowercase					:       List[Any]       =		image.transpose(0,				3,				1,				2					)
		_lowercase					:       Dict       =		2.0 * image - 1.0
		_lowercase					:       Dict       =		torch.from_numpy(__lowerCAmelCase					)
	elif isinstance(image[0],				torch.Tensor					):
		_lowercase					:       Union[str, Any]       =		torch.cat(__lowerCAmelCase,				dim=0					)
	return image







def 						__UpperCamelCase				(							_lowercase					) ->			Optional[int]:
	if isinstance(__lowerCAmelCase,				torch.Tensor					):
		return mask
	elif isinstance(__lowerCAmelCase,				PIL.Image.Image					):
		_lowercase					:       Optional[Any]       =		[mask]

	if isinstance(mask[0],				PIL.Image.Image					):
		_lowercase					:       Union[str, Any]       =		mask[0].size
		_lowercase					:       List[Any]       =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		_lowercase					:       Dict       =		[np.array(m.convert('L'					).resize((w, h),				resample=PIL_INTERPOLATION['nearest']					)					)[None, :] for m in mask]
		_lowercase					:       int       =		np.concatenate(__lowerCAmelCase,				axis=0					)
		_lowercase					:       List[Any]       =		mask.astype(np.floataa					) / 2_5_5.0
		_lowercase					:       Any       =		0
		_lowercase					:       Dict       =		1
		_lowercase					:       List[str]       =		torch.from_numpy(__lowerCAmelCase					)
	elif isinstance(mask[0],				torch.Tensor					):
		_lowercase					:       Optional[Any]       =		torch.cat(__lowerCAmelCase,				dim=0					)
	return mask






class 		lowerCamelCase__      (						UpperCamelCase_     ):




					'''simple docstring'''



					A_						    =						42
					A_						    =						42




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Any					)  ->       Any:



						'''simple docstring'''





						super().__init__()
						self.register_modules(unet=_a     ,  scheduler=_a					)




					@torch.no_grad()
					def __call__(  self				:	Optional[int]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[Any] = 250     ,  UpperCamelCase_				:	Union[str, Any] = 0.0     ,  UpperCamelCase_				:	Dict = 10     ,  UpperCamelCase_				:	Union[str, Any] = 10     ,  UpperCamelCase_				:	Any = None     ,  UpperCamelCase_				:	Dict = "pil"     ,  UpperCamelCase_				:	Optional[Any] = True     ,  )  ->       Union[ImagePipelineOutput, Tuple]:



						'''simple docstring'''





						_lowercase					:       str       =		image

						_lowercase					:       List[Any]       =		_preprocess_image(_a					)
						_lowercase					:       int       =		original_image.to(device=self.device     ,  dtype=self.unet.dtype					)
						_lowercase					:       Optional[int]       =		_preprocess_mask(_a					)
						_lowercase					:       Optional[Any]       =		mask_image.to(device=self.device     ,  dtype=self.unet.dtype					)

						_lowercase					:       Optional[int]       =		original_image.shape[0]

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

						_lowercase					:       int       =		original_image.shape
						_lowercase					:       List[str]       =		randn_tensor(_a     ,  generator=_a     ,  device=self.device     ,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(_a     ,  _a     ,  _a     ,  self.device					)
						_lowercase					:       Dict       =		eta

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

							else:
								# compute the reverse: x_t-1 -> x_t
								_lowercase					:       List[Any]       =		self.scheduler.undo_step(_a     ,  _a     ,  _a					)
							_lowercase					:       int       =		t

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

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=_a					)





'''simple docstring'''

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       str       =		AutoTokenizer.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       List[Any]       =		'The dog is cute and lives in the garden house'
						_lowercase					:       Optional[int]       =		jnp.array([tokenizer.encode(UpperCamelCase_					)]					)

						_lowercase					:       int       =		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
						_lowercase					:       Tuple       =		jnp.array(
						    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]					)

						_lowercase					:       List[str]       =		model(UpperCamelCase_					)['last_hidden_state']
						self.assertEqual(output.shape     ,  UpperCamelCase_					)
						# compare the actual values for a slice of last dim
						self.assertTrue(jnp.allclose(output[:, :, -1]     ,  UpperCamelCase_     ,  atol=1E-3					)					)
