




"""simple docstring"""






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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42
			lowerCamelCase__   =    42






class lowerCAmelCase__			(  nn.Module		):






			'''simple docstring'''



			lowerCamelCase__   =    42
			lowerCamelCase__   =    (16, 32, 96, 2_56)
			lowerCamelCase__   =    jnp.floataa
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					nn.Conv(
							    self.block_out_channels[0]						,   kernel_size=(3, 3)						,   padding=((1, 1), (1, 1))						,   dtype=self.dtype						,   )

							_lowerCamelCase  :    Any					=					[]
							for i in range(len(self.block_out_channels						) - 1						):
											_lowerCamelCase  :    str					=					self.block_out_channels[i]
											_lowerCamelCase  :    Any					=					self.block_out_channels[i + 1]
											_lowerCamelCase  :    Tuple					=					nn.Conv(
											    lowercase						,   kernel_size=(3, 3)						,   padding=((1, 1), (1, 1))						,   dtype=self.dtype						,   )
											blocks.append(lowercase						)
											_lowerCamelCase  :    Optional[int]					=					nn.Conv(
											    lowercase						,   kernel_size=(3, 3)						,   strides=(2, 2)						,   padding=((1, 1), (1, 1))						,   dtype=self.dtype						,   )
											blocks.append(lowercase						)
							_lowerCamelCase  :    str					=					blocks

							_lowerCamelCase  :    int					=					nn.Conv(
							    self.conditioning_embedding_channels						,   kernel_size=(3, 3)						,   padding=((1, 1), (1, 1))						,   kernel_init=nn.initializers.zeros_init()						,   bias_init=nn.initializers.zeros_init()						,   dtype=self.dtype						,   )






			def __call__(						self						,   lowercase						):
							_lowerCamelCase  :    Dict					=					self.conv_in(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					nn.silu(lowercase						)

							for block in self.blocks:
											_lowerCamelCase  :    Any					=					block(lowercase						)
											_lowerCamelCase  :    Optional[int]					=					nn.silu(lowercase						)

							_lowerCamelCase  :    List[str]					=					self.conv_out(lowercase						)

							return embedding






@flax_register_to_config
class lowerCAmelCase__			(  nn.Module,			lowercase,			lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    32
			lowerCamelCase__   =    4
			lowerCamelCase__   =    (
			    """CrossAttnDownBlock2D""",
			    """CrossAttnDownBlock2D""",
			    """CrossAttnDownBlock2D""",
			    """DownBlock2D""",
			)
			lowerCamelCase__   =    False
			lowerCamelCase__   =    (3_20, 6_40, 12_80, 12_80)
			lowerCamelCase__   =    2
			lowerCamelCase__   =    8
			lowerCamelCase__   =    None
			lowerCamelCase__   =    12_80
			lowerCamelCase__   =    0.0
			lowerCamelCase__   =    False
			lowerCamelCase__   =    jnp.floataa
			lowerCamelCase__   =    True
			lowerCamelCase__   =    0
			lowerCamelCase__   =    """rgb"""
			lowerCamelCase__   =    (16, 32, 96, 2_56)
			def       A_					(						self						,   lowercase						):
							# init input tensors
							_lowerCamelCase  :    int					=					(1, self.in_channels, self.sample_size, self.sample_size)
							_lowerCamelCase  :    Optional[Any]					=					jnp.zeros(lowercase						,   dtype=jnp.floataa						)
							_lowerCamelCase  :    List[Any]					=					jnp.ones((1,)						,   dtype=jnp.intaa						)
							_lowerCamelCase  :    Any					=					jnp.zeros((1, 1, self.cross_attention_dim)						,   dtype=jnp.floataa						)
							_lowerCamelCase  :    int					=					(1, 3, self.sample_size * 8, self.sample_size * 8)
							_lowerCamelCase  :    Optional[Any]					=					jnp.zeros(lowercase						,   dtype=jnp.floataa						)

							_lowerCamelCase  :    List[Any]					=					jax.random.split(lowercase						)
							_lowerCamelCase  :    List[str]					=					{'params': params_rng, 'dropout': dropout_rng}

							return self.init(lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						)["params"]
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.block_out_channels
							_lowerCamelCase  :    List[str]					=					block_out_channels[0] * 4

							# 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.
							_lowerCamelCase  :    List[str]					=					self.num_attention_heads or self.attention_head_dim

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

							# time
							_lowerCamelCase  :    Optional[Any]					=					FlaxTimesteps(
							    block_out_channels[0]						,   flip_sin_to_cos=self.flip_sin_to_cos						,   freq_shift=self.config.freq_shift						)
							_lowerCamelCase  :    int					=					FlaxTimestepEmbedding(lowercase						,   dtype=self.dtype						)

							_lowerCamelCase  :    Dict					=					FlaxControlNetConditioningEmbedding(
							    conditioning_embedding_channels=block_out_channels[0]						,   block_out_channels=self.conditioning_embedding_out_channels						,   )

							_lowerCamelCase  :    int					=					self.only_cross_attention
							if isinstance(lowercase						,   lowercase						):
											_lowerCamelCase  :    Tuple					=					(only_cross_attention,) * len(self.down_block_types						)

							if isinstance(lowercase						,   lowercase						):
											_lowerCamelCase  :    int					=					(num_attention_heads,) * len(self.down_block_types						)

							# down
							_lowerCamelCase  :    List[str]					=					[]
							_lowerCamelCase  :    List[Any]					=					[]

							_lowerCamelCase  :    Union[str, Any]					=					block_out_channels[0]

							_lowerCamelCase  :    List[str]					=					nn.Conv(
							    lowercase						,   kernel_size=(1, 1)						,   padding='VALID'						,   kernel_init=nn.initializers.zeros_init()						,   bias_init=nn.initializers.zeros_init()						,   dtype=self.dtype						,   )
							controlnet_down_blocks.append(lowercase						)

							for i, down_block_type in enumerate(self.down_block_types						):
											_lowerCamelCase  :    Optional[int]					=					output_channel
											_lowerCamelCase  :    Union[str, Any]					=					block_out_channels[i]
											_lowerCamelCase  :    List[Any]					=					i == len(lowercase						) - 1

											if down_block_type == "CrossAttnDownBlock2D":
															_lowerCamelCase  :    Optional[int]					=					FlaxCrossAttnDownBlockaD(
															    in_channels=lowercase						,   out_channels=lowercase						,   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]						,   dtype=self.dtype						,   )
											else:
															_lowerCamelCase  :    Union[str, Any]					=					FlaxDownBlockaD(
															    in_channels=lowercase						,   out_channels=lowercase						,   dropout=self.dropout						,   num_layers=self.layers_per_block						,   add_downsample=not is_final_block						,   dtype=self.dtype						,   )

											down_blocks.append(lowercase						)

											for _ in range(self.layers_per_block						):
															_lowerCamelCase  :    Tuple					=					nn.Conv(
															    lowercase						,   kernel_size=(1, 1)						,   padding='VALID'						,   kernel_init=nn.initializers.zeros_init()						,   bias_init=nn.initializers.zeros_init()						,   dtype=self.dtype						,   )
															controlnet_down_blocks.append(lowercase						)

											if not is_final_block:
															_lowerCamelCase  :    Tuple					=					nn.Conv(
															    lowercase						,   kernel_size=(1, 1)						,   padding='VALID'						,   kernel_init=nn.initializers.zeros_init()						,   bias_init=nn.initializers.zeros_init()						,   dtype=self.dtype						,   )
															controlnet_down_blocks.append(lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					down_blocks
							_lowerCamelCase  :    List[Any]					=					controlnet_down_blocks

							# mid
							_lowerCamelCase  :    List[Any]					=					block_out_channels[-1]
							_lowerCamelCase  :    str					=					FlaxUNetMidBlockaDCrossAttn(
							    in_channels=lowercase						,   dropout=self.dropout						,   num_attention_heads=num_attention_heads[-1]						,   use_linear_projection=self.use_linear_projection						,   dtype=self.dtype						,   )

							_lowerCamelCase  :    str					=					nn.Conv(
							    lowercase						,   kernel_size=(1, 1)						,   padding='VALID'						,   kernel_init=nn.initializers.zeros_init()						,   bias_init=nn.initializers.zeros_init()						,   dtype=self.dtype						,   )






			def __call__(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase = 1.0						,   lowercase = True						,   lowercase = False						,   ):
							_lowerCamelCase  :    Dict					=					self.controlnet_conditioning_channel_order
							if channel_order == "bgr":
											_lowerCamelCase  :    int					=					jnp.flip(lowercase						,   axis=1						)

							# 1. time
							if not isinstance(lowercase						,   jnp.ndarray						):
											_lowerCamelCase  :    str					=					jnp.array([timesteps]						,   dtype=jnp.intaa						)
							elif isinstance(lowercase						,   jnp.ndarray						) and len(timesteps.shape						) == 0:
											_lowerCamelCase  :    Union[str, Any]					=					timesteps.astype(dtype=jnp.floataa						)
											_lowerCamelCase  :    List[Any]					=					jnp.expand_dims(lowercase						,   0						)

							_lowerCamelCase  :    Dict					=					self.time_proj(lowercase						)
							_lowerCamelCase  :    Tuple					=					self.time_embedding(lowercase						)

							# 2. pre-process
							_lowerCamelCase  :    Dict					=					jnp.transpose(lowercase						,   (0, 2, 3, 1)						)
							_lowerCamelCase  :    Tuple					=					self.conv_in(lowercase						)

							_lowerCamelCase  :    Any					=					jnp.transpose(lowercase						,   (0, 2, 3, 1)						)
							_lowerCamelCase  :    List[str]					=					self.controlnet_cond_embedding(lowercase						)
							sample += controlnet_cond

							# 3. down
							_lowerCamelCase  :    int					=					(sample,)
							for down_block in self.down_blocks:
											if isinstance(lowercase						,   lowercase						):
															_lowerCamelCase  :    Optional[int]					=					down_block(lowercase						,   lowercase						,   lowercase						,   deterministic=not train						)
											else:
															_lowerCamelCase  :    str					=					down_block(lowercase						,   lowercase						,   deterministic=not train						)
											down_block_res_samples += res_samples

							# 4. mid
							_lowerCamelCase  :    Tuple					=					self.mid_block(lowercase						,   lowercase						,   lowercase						,   deterministic=not train						)

							# 5. contronet blocks
							_lowerCamelCase  :    Dict					=					()
							for down_block_res_sample, controlnet_block in zip(lowercase						,   self.controlnet_down_blocks						):
											_lowerCamelCase  :    Any					=					controlnet_block(lowercase						)
											controlnet_down_block_res_samples += (down_block_res_sample,)

							_lowerCamelCase  :    str					=					controlnet_down_block_res_samples

							_lowerCamelCase  :    int					=					self.controlnet_mid_block(lowercase						)

							# 6. scaling
							_lowerCamelCase  :    Dict					=					[sample * conditioning_scale for sample in down_block_res_samples]
							mid_block_res_sample *= conditioning_scale

							if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

							return FlaxControlNetOutput(
							    down_block_res_samples=lowercase						,   mid_block_res_sample=lowercase						)









"""simple docstring"""






import copy
import os

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






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()