






'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__UpperCamelCase      :      Dict     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  b.T
	__lowercase             =  np.sum(np.square(_lowercase					)				,  axis=1					)
	__lowercase             =  np.sum(np.square(_lowercase					)				,  axis=0					)
	__lowercase             =  np.matmul(_lowercase				,  _lowercase					)
	__lowercase             =  aa[:, None] - 2 * ab + ba[None, :]
	return d





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  x.reshape(-1				,  3					)
	__lowercase             =  squared_euclidean_distance(_lowercase				,  _lowercase					)
	return np.argmin(_lowercase				,  axis=1					)




class        __UpperCamelCase    (     a__   ):
	__snake_case	:Union[str, Any]   =       ["""pixel_values"""]







	def __init__(  self      :							Optional[int]       ,				_lowerCAmelCase      :							int = None       ,				_lowerCAmelCase      :							Optional[Any] = True       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Any = PILImageResampling.BILINEAR       ,				_lowerCAmelCase      :							int = True       ,				_lowerCAmelCase      :							Tuple = True       ,				**_lowerCAmelCase      :							Dict       ,				)		-> None:
		"""simple docstring"""







		super().__init__(**lowerCAmelCase__			)
		__lowercase             =  size if size is not None else {"height": 256, "width": 256}
		__lowercase             =  get_size_dict(lowerCAmelCase__			)
		__lowercase             =  np.array(lowerCAmelCase__			) if clusters is not None else None
		__lowercase             =  do_resize
		__lowercase             =  size
		__lowercase             =  resample
		__lowercase             =  do_normalize
		__lowercase             =  do_color_quantize







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							List[str] = PILImageResampling.BILINEAR       ,				_lowerCAmelCase      :							List[Any] = None       ,				**_lowerCAmelCase      :							str       ,				)		-> np.ndarray:
		"""simple docstring"""







		__lowercase             =  get_size_dict(lowerCAmelCase__			)
		if "height" not in size or "width" not in size:
			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'			)
		return resize(
		    lowerCAmelCase__       ,				size=(size["""height"""], size["""width"""])       ,				resample=lowerCAmelCase__       ,				data_format=lowerCAmelCase__       ,				**lowerCAmelCase__			)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Union[str, Any] = None       ,				)		-> np.ndarray:
		"""simple docstring"""







		__lowercase             =  rescale(image=lowerCAmelCase__       ,				scale=1 / 127.5       ,				data_format=lowerCAmelCase__			)
		__lowercase             =  image - 1
		return image







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Dict = None       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Optional[Any] = None       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Dict = None       ,				_lowerCAmelCase      :							Union[str, Any] = None       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Any] = ChannelDimension.FIRST       ,				**_lowerCAmelCase      :							Any       ,				)		-> PIL.Image.Image:
		"""simple docstring"""







		__lowercase             =  do_resize if do_resize is not None else self.do_resize
		__lowercase             =  size if size is not None else self.size
		__lowercase             =  get_size_dict(lowerCAmelCase__			)
		__lowercase             =  resample if resample is not None else self.resample
		__lowercase             =  do_normalize if do_normalize is not None else self.do_normalize
		__lowercase             =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
		__lowercase             =  clusters if clusters is not None else self.clusters
		__lowercase             =  np.array(lowerCAmelCase__			)

		__lowercase             =  make_list_of_images(lowerCAmelCase__			)

		if not valid_images(lowerCAmelCase__			):
			raise ValueError(
			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
			    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

		if do_resize and size is None or resample is None:
			raise ValueError("""Size and resample must be specified if do_resize is True."""			)

		if do_color_quantize and clusters is None:
			raise ValueError("""Clusters must be specified if do_color_quantize is True."""			)

		# All transformations expect numpy arrays.
		__lowercase             =  [to_numpy_array(lowerCAmelCase__			) for image in images]

		if do_resize:
			__lowercase             =  [self.resize(image=lowerCAmelCase__       ,				size=lowerCAmelCase__       ,				resample=lowerCAmelCase__			) for image in images]

		if do_normalize:
			__lowercase             =  [self.normalize(image=lowerCAmelCase__			) for image in images]

		if do_color_quantize:
			__lowercase             =  [to_channel_dimension_format(lowerCAmelCase__       ,				ChannelDimension.LAST			) for image in images]
			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
			__lowercase             =  np.array(lowerCAmelCase__			)
			__lowercase             =  color_quantize(lowerCAmelCase__       ,				lowerCAmelCase__			).reshape(images.shape[:-1]			)

			# flatten to (batch_size, height*width)
			__lowercase             =  images.shape[0]
			__lowercase             =  images.reshape(lowerCAmelCase__       ,				-1			)

			# We need to convert back to a list of images to keep consistent behaviour across processors.
			__lowercase             =  list(lowerCAmelCase__			)
		else:
			__lowercase             =  [to_channel_dimension_format(lowerCAmelCase__       ,				lowerCAmelCase__			) for image in images]

		__lowercase             =  {"input_ids": images}
		return BatchFeature(data=lowerCAmelCase__       ,				tensor_type=lowerCAmelCase__			)









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

