from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)





@flax.struct.dataclass
class      A							(				_lowerCamelCase     ):
   __UpperCAmelCase : Optional[Any]      =			42





@flax_register_to_config
class      A							(				nn.Module		,				_lowerCamelCase		,				_lowerCamelCase     ):
   __UpperCAmelCase : Tuple      =			32
   __UpperCAmelCase : str      =			4
   __UpperCAmelCase : Optional[int]      =			4
   __UpperCAmelCase : Union[str, Any]      =			(
       'CrossAttnDownBlock2D',
       'CrossAttnDownBlock2D',
       'CrossAttnDownBlock2D',
       'DownBlock2D',
   )
   __UpperCAmelCase : int      =			('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D')
   __UpperCAmelCase : Tuple      =			False
   __UpperCAmelCase : Optional[int]      =			(3_20, 6_40, 12_80, 12_80)
   __UpperCAmelCase : Union[str, Any]      =			2
   __UpperCAmelCase : Optional[int]      =			8
   __UpperCAmelCase : Union[str, Any]      =			None
   __UpperCAmelCase : Union[str, Any]      =			12_80
   __UpperCAmelCase : List[str]      =			0.0
   __UpperCAmelCase : List[Any]      =			False
   __UpperCAmelCase : List[Any]      =			jnp.floataa
   __UpperCAmelCase : List[str]      =			True
   __UpperCAmelCase : Tuple      =			0
   __UpperCAmelCase : List[str]      =			False


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						jax.random.KeyArray		)						->				FrozenDict:
         """simple docstring"""



         UpperCAmelCase__								=  (1, self.in_channels, self.sample_size, self.sample_size)
         UpperCAmelCase__								=  jnp.zeros(_UpperCamelCase       ,  dtype=jnp.floataa		)
         UpperCAmelCase__								=  jnp.ones((1,)       ,  dtype=jnp.intaa		)
         UpperCAmelCase__								=  jnp.zeros((1, 1, self.cross_attention_dim)       ,  dtype=jnp.floataa		)

         UpperCAmelCase__		,      UpperCAmelCase__								=  jax.random.split(_UpperCamelCase		)
         UpperCAmelCase__								=  {"params": params_rng, "dropout": dropout_rng}

         return self.init(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		)["params"]


   def 			lowercase_      (self     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.block_out_channels
         UpperCAmelCase__								=  block_out_channels[0] * 4

         if self.num_attention_heads is not None:
               raise ValueError(
                   "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."		)

         # If `num_attention_heads` is not defined (which is the case for most models)
         # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
         # The reason for this behavior is to correct for incorrectly named variables that were introduced
         # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
         # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
         # which is why we correct for the naming here.
         UpperCAmelCase__								=  self.num_attention_heads or self.attention_head_dim

         # input
         UpperCAmelCase__								=  nn.Conv(
             block_out_channels[0]       ,  kernel_size=(3, 3)       ,  strides=(1, 1)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )

         # time
         UpperCAmelCase__								=  FlaxTimesteps(
             block_out_channels[0]       ,  flip_sin_to_cos=self.flip_sin_to_cos       ,  freq_shift=self.config.freq_shift		)
         UpperCAmelCase__								=  FlaxTimestepEmbedding(_UpperCamelCase       ,  dtype=self.dtype		)

         UpperCAmelCase__								=  self.only_cross_attention
         if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
               UpperCAmelCase__								=  (only_cross_attention,) * len(self.down_block_types		)

         if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
               UpperCAmelCase__								=  (num_attention_heads,) * len(self.down_block_types		)

         # down
         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  block_out_channels[0]
         for i, down_block_type in enumerate(self.down_block_types		):
               UpperCAmelCase__								=  output_channel
               UpperCAmelCase__								=  block_out_channels[i]
               UpperCAmelCase__								=  i == len(_UpperCamelCase		) - 1

               if down_block_type == "CrossAttnDownBlock2D":
                     UpperCAmelCase__								=  FlaxCrossAttnDownBlockaD(
                         in_channels=_UpperCamelCase       ,  out_channels=_UpperCamelCase       ,  dropout=self.dropout       ,  num_layers=self.layers_per_block       ,  num_attention_heads=num_attention_heads[i]       ,  add_downsample=not is_final_block       ,  use_linear_projection=self.use_linear_projection       ,  only_cross_attention=only_cross_attention[i]       ,  use_memory_efficient_attention=self.use_memory_efficient_attention       ,  dtype=self.dtype       ,  )
               else:
                     UpperCAmelCase__								=  FlaxDownBlockaD(
                         in_channels=_UpperCamelCase       ,  out_channels=_UpperCamelCase       ,  dropout=self.dropout       ,  num_layers=self.layers_per_block       ,  add_downsample=not is_final_block       ,  dtype=self.dtype       ,  )

               down_blocks.append(_UpperCamelCase		)
         UpperCAmelCase__								=  down_blocks

         # mid
         UpperCAmelCase__								=  FlaxUNetMidBlockaDCrossAttn(
             in_channels=block_out_channels[-1]       ,  dropout=self.dropout       ,  num_attention_heads=num_attention_heads[-1]       ,  use_linear_projection=self.use_linear_projection       ,  use_memory_efficient_attention=self.use_memory_efficient_attention       ,  dtype=self.dtype       ,  )

         # up
         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  list(reversed(_UpperCamelCase		)		)
         UpperCAmelCase__								=  list(reversed(_UpperCamelCase		)		)
         UpperCAmelCase__								=  list(reversed(_UpperCamelCase		)		)
         UpperCAmelCase__								=  reversed_block_out_channels[0]
         for i, up_block_type in enumerate(self.up_block_types		):
               UpperCAmelCase__								=  output_channel
               UpperCAmelCase__								=  reversed_block_out_channels[i]
               UpperCAmelCase__								=  reversed_block_out_channels[min(i + 1       ,  len(_UpperCamelCase		) - 1		)]

               UpperCAmelCase__								=  i == len(_UpperCamelCase		) - 1

               if up_block_type == "CrossAttnUpBlock2D":
                     UpperCAmelCase__								=  FlaxCrossAttnUpBlockaD(
                         in_channels=_UpperCamelCase       ,  out_channels=_UpperCamelCase       ,  prev_output_channel=_UpperCamelCase       ,  num_layers=self.layers_per_block + 1       ,  num_attention_heads=reversed_num_attention_heads[i]       ,  add_upsample=not is_final_block       ,  dropout=self.dropout       ,  use_linear_projection=self.use_linear_projection       ,  only_cross_attention=only_cross_attention[i]       ,  use_memory_efficient_attention=self.use_memory_efficient_attention       ,  dtype=self.dtype       ,  )
               else:
                     UpperCAmelCase__								=  FlaxUpBlockaD(
                         in_channels=_UpperCamelCase       ,  out_channels=_UpperCamelCase       ,  prev_output_channel=_UpperCamelCase       ,  num_layers=self.layers_per_block + 1       ,  add_upsample=not is_final_block       ,  dropout=self.dropout       ,  dtype=self.dtype       ,  )

               up_blocks.append(_UpperCamelCase		)
               UpperCAmelCase__								=  output_channel
         UpperCAmelCase__								=  up_blocks

         # out
         UpperCAmelCase__								=  nn.GroupNorm(num_groups=3_2       ,  epsilon=1E-5		)
         UpperCAmelCase__								=  nn.Conv(
             self.out_channels       ,  kernel_size=(3, 3)       ,  strides=(1, 1)       ,  padding=((1, 1), (1, 1))       ,  dtype=self.dtype       ,  )





   def __call__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int=None       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						bool = False       ,  )						->				Union[FlaxUNetaDConditionOutput, Tuple]:
         """simple docstring"""



         if not isinstance(_UpperCamelCase       ,  jnp.ndarray		):
               UpperCAmelCase__								=  jnp.array([timesteps]       ,  dtype=jnp.intaa		)
         elif isinstance(_UpperCamelCase       ,  jnp.ndarray		) and len(timesteps.shape		) == 0:
               UpperCAmelCase__								=  timesteps.astype(dtype=jnp.floataa		)
               UpperCAmelCase__								=  jnp.expand_dims(_UpperCamelCase       ,  0		)

         UpperCAmelCase__								=  self.time_proj(_UpperCamelCase		)
         UpperCAmelCase__								=  self.time_embedding(_UpperCamelCase		)

         # 2. pre-process
         UpperCAmelCase__								=  jnp.transpose(_UpperCamelCase       ,  (0, 2, 3, 1)		)
         UpperCAmelCase__								=  self.conv_in(_UpperCamelCase		)

         # 3. down
         UpperCAmelCase__								=  (sample,)
         for down_block in self.down_blocks:
               if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
                     UpperCAmelCase__		,      UpperCAmelCase__								=  down_block(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  deterministic=not train		)
               else:
                     UpperCAmelCase__		,      UpperCAmelCase__								=  down_block(_UpperCamelCase       ,  _UpperCamelCase       ,  deterministic=not train		)
               down_block_res_samples += res_samples

         if down_block_additional_residuals is not None:
               UpperCAmelCase__								=  ()

               for down_block_res_sample, down_block_additional_residual in zip(
                   _UpperCamelCase       ,  _UpperCamelCase		):
                     down_block_res_sample += down_block_additional_residual
                     new_down_block_res_samples += (down_block_res_sample,)

               UpperCAmelCase__								=  new_down_block_res_samples

         # 4. mid
         UpperCAmelCase__								=  self.mid_block(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  deterministic=not train		)

         if mid_block_additional_residual is not None:
               sample += mid_block_additional_residual

         # 5. up
         for up_block in self.up_blocks:
               UpperCAmelCase__								=  down_block_res_samples[-(self.layers_per_block + 1) :]
               UpperCAmelCase__								=  down_block_res_samples[: -(self.layers_per_block + 1)]
               if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
                     UpperCAmelCase__								=  up_block(
                         _UpperCamelCase       ,  temb=_UpperCamelCase       ,  encoder_hidden_states=_UpperCamelCase       ,  res_hidden_states_tuple=_UpperCamelCase       ,  deterministic=not train       ,  )
               else:
                     UpperCAmelCase__								=  up_block(_UpperCamelCase       ,  temb=_UpperCamelCase       ,  res_hidden_states_tuple=_UpperCamelCase       ,  deterministic=not train		)

        # 6. post-process
         UpperCAmelCase__								=  self.conv_norm_out(_UpperCamelCase		)
         UpperCAmelCase__								=  nn.silu(_UpperCamelCase		)
         UpperCAmelCase__								=  self.conv_out(_UpperCamelCase		)
         UpperCAmelCase__								=  jnp.transpose(_UpperCamelCase       ,  (0, 3, 1, 2)		)

         if not return_dict:
               return (sample,)

         return FlaxUNetaDConditionOutput(sample=_UpperCamelCase		)










from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





