



"""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


__snake_case    :		Optional[int]  =logging.get_logger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					b.T
				lowerCAmelCase__					:    List[Any]												=					np.sum(np.square(lowerCamelCase_)	,axis=1)
				lowerCAmelCase__					:    Dict												=					np.sum(np.square(lowerCamelCase_)	,axis=0)
				lowerCAmelCase__					:    Optional[int]												=					np.matmul(lowerCamelCase_	,lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					aa[:, None] - 2 * ab + ba[None, :]
				return d






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				lowerCAmelCase__					:    Tuple												=					x.reshape(-1	,3)
				lowerCAmelCase__					:    str												=					squared_euclidean_distance(lowerCamelCase_	,lowerCamelCase_)
				return np.argmin(lowerCamelCase_	,axis=1)



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""pixel_values"""]



		def __init__(self		,__lowerCamelCase = None		,__lowerCamelCase = True		,__lowerCamelCase = None		,__lowerCamelCase = PILImageResampling.BILINEAR		,__lowerCamelCase = True		,__lowerCamelCase = True		,**__lowerCamelCase		,)					->      None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
						lowerCAmelCase__					:    Optional[Any]												=					get_size_dict(__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					np.array(__lowerCamelCase			) if clusters is not None else None
						lowerCAmelCase__					:    Any												=					do_resize
						lowerCAmelCase__					:    List[str]												=					size
						lowerCAmelCase__					:    str												=					resample
						lowerCAmelCase__					:    List[str]												=					do_normalize
						lowerCAmelCase__					:    Union[str, Any]												=					do_color_quantize



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = PILImageResampling.BILINEAR		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					rescale(image=__lowerCamelCase		,scale=1 / 127.5		,data_format=__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					image - 1
						return image



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = ChannelDimension.FIRST		,**__lowerCamelCase		,)					->      PIL.Image.Image:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__					:    int												=					size if size is not None else self.size
						lowerCAmelCase__					:    str												=					get_size_dict(__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					resample if resample is not None else self.resample
						lowerCAmelCase__					:    Dict												=					do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__					:    str												=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						lowerCAmelCase__					:    Any												=					clusters if clusters is not None else self.clusters
						lowerCAmelCase__					:    Tuple												=					np.array(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					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.
						lowerCAmelCase__					:    int												=					[to_numpy_array(__lowerCamelCase			) for image in images]

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

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

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

										# flatten to (batch_size, height*width)
										lowerCAmelCase__					:    Optional[Any]												=					images.shape[0]
										lowerCAmelCase__					:    List[str]												=					images.reshape(__lowerCamelCase		,-1			)

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

						lowerCAmelCase__					:    Tuple												=					{'''input_ids''': images}
						return BatchFeature(data=__lowerCamelCase		,tensor_type=__lowerCamelCase			)






import flax.linen as nn
import jax
import jax.numpy as jnp



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					hidden_states.shape
						lowerCAmelCase__					:    Dict												=					jax.image.resize(
						    __lowerCamelCase		,shape=(batch, height * 2, width * 2, channels)		,method='''nearest'''		,)
						lowerCAmelCase__					:    Dict												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					nn.Conv(
						    self.out_channels		,kernel_size=(3, 3)		,strides=(2, 2)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.conv(__lowerCamelCase			)
						return hidden_states



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =None
		snake_case_     =0.0
		snake_case_     =None
		snake_case_     =jnp.floataa



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.in_channels if self.out_channels is None else self.out_channels

						lowerCAmelCase__					:    Union[str, Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    int												=					nn.Dense(__lowerCamelCase		,dtype=self.dtype			)

						lowerCAmelCase__					:    List[Any]												=					nn.GroupNorm(num_groups=32		,epsilon=1e-5			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.Dropout(self.dropout_prob			)
						lowerCAmelCase__					:    Optional[int]												=					nn.Conv(
						    __lowerCamelCase		,kernel_size=(3, 3)		,strides=(1, 1)		,padding=((1, 1), (1, 1))		,dtype=self.dtype		,)

						lowerCAmelCase__					:    str												=					self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						lowerCAmelCase__					:    Union[str, Any]												=					None
						if use_nin_shortcut:
										lowerCAmelCase__					:    Optional[Any]												=					nn.Conv(
										    __lowerCamelCase		,kernel_size=(1, 1)		,strides=(1, 1)		,padding='''VALID'''		,dtype=self.dtype		,)



		def __call__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=True			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					hidden_states
						lowerCAmelCase__					:    Dict												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					self.time_emb_proj(nn.swish(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					jnp.expand_dims(jnp.expand_dims(__lowerCamelCase		,1			)		,1			)
						lowerCAmelCase__					:    Optional[int]												=					hidden_states + temb

						lowerCAmelCase__					:    Optional[int]												=					self.norma(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					nn.swish(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.dropout(__lowerCamelCase		,__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conva(__lowerCamelCase			)

						if self.conv_shortcut is not None:
										lowerCAmelCase__					:    Optional[int]												=					self.conv_shortcut(__lowerCamelCase			)

						return hidden_states + residual

