


"""simple docstring"""


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


A  :     str    					=    logging.get_logger(__name__)
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =["""pixel_values"""]

							def __init__(  self					,     __a = True					,     __a = None					,     __a = PILImageResampling.BICUBIC					,     __a = True					,     __a = None					,     __a = True					,     __a = 1 / 2_55					,     __a = True					,     __a = IMAGENET_DEFAULT_MEAN					,     __a = IMAGENET_DEFAULT_STD					,     **__a					,     ):
										super().__init__(**__a			)
										__lowerCAmelCase       					=    size if size is not None else {"shortest_edge": 2_24}
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										__lowerCAmelCase       					=    crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase       					=    get_size_dict(__a					,     param_name="crop_size"			)

										__lowerCAmelCase       					=    do_resize
										__lowerCAmelCase       					=    size
										__lowerCAmelCase       					=    resample
										__lowerCAmelCase       					=    do_center_crop
										__lowerCAmelCase       					=    crop_size
										__lowerCAmelCase       					=    do_rescale
										__lowerCAmelCase       					=    rescale_factor
										__lowerCAmelCase       					=    do_normalize
										__lowerCAmelCase       					=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__lowerCAmelCase       					=    image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def     snake_case       (  self					,     __a					,     __a					,     __a = PILImageResampling.BICUBIC					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
													__lowerCAmelCase       					=    int((2_56 / 2_24) * size["shortest_edge"]			)
													__lowerCAmelCase       					=    get_resize_output_image_size(__a					,     size=__a					,     default_to_square=__a			)
													__lowerCAmelCase       					=    {"height": output_size[0], "width": output_size[1]}
										if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"			)
										return resize(
										    __a					,     size=(size_dict["height"], size_dict["width"])					,     resample=__a					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    get_size_dict(__a			)
										if "height" not in size or "width" not in size:
													raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"			)
										return center_crop(__a					,     size=(size["height"], size["width"])					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										return rescale(__a					,     scale=__a					,     data_format=__a					,     **__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a = None					,     **__a					,     ):
										return normalize(__a					,     mean=__a					,     std=__a					,     data_format=__a					,     **__a			)



							def     snake_case       (  self					,     __a					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = None					,     __a = ChannelDimension.FIRST					,     **__a					,     ):
										__lowerCAmelCase       					=    do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase       					=    resample if resample is not None else self.resample
										__lowerCAmelCase       					=    do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase       					=    do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase       					=    rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase       					=    do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase       					=    image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase       					=    image_std if image_std is not None else self.image_std

										__lowerCAmelCase       					=    size if size is not None else self.size
										__lowerCAmelCase       					=    get_size_dict(__a					,     default_to_square=__a			)
										__lowerCAmelCase       					=    crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase       					=    get_size_dict(__a					,     param_name="crop_size"			)

										__lowerCAmelCase       					=    make_list_of_images(__a			)

										if not valid_images(__a			):
													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:
													raise ValueError("Size must be specified if do_resize is True."			)

										if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."			)

										if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."			)

										if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."			)

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

										if do_resize:
													__lowerCAmelCase       					=    [self.resize(__a					,     __a					,     __a			) for image in images]

										if do_center_crop:
													__lowerCAmelCase       					=    [self.center_crop(__a					,     __a			) for image in images]

										if do_rescale:
													__lowerCAmelCase       					=    [self.rescale(__a					,     __a			) for image in images]

										if do_normalize:
													__lowerCAmelCase       					=    [self.normalize(__a					,     __a					,     __a			) for image in images]

										__lowerCAmelCase       					=    [to_channel_dimension_format(__a					,     __a			) for image in images]

										__lowerCAmelCase       					=    {"pixel_values": images}
										return BatchFeature(data=__a					,     tensor_type=__a			)








"""simple docstring"""


import flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




