"""simple docstring"""




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class       a_      (     nn.Module			):




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     int=0.0  ,						snake_case__						:     Optional[int] = None  ,						snake_case__						:     str = "geglu"  ,						snake_case__						:     Optional[int] = None  ,						snake_case__						:     bool = False  ,						snake_case__						:     bool = False  ,						snake_case__						:     bool = False  ,						snake_case__						:     bool = False  ,						snake_case__						:     bool = True  ,						snake_case__						:     str = "layer_norm"  ,						snake_case__						:     bool = False  ,						):
							super().__init__()
							lowerCAmelCase__        =   only_cross_attention

							lowerCAmelCase__        =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
							lowerCAmelCase__        =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

							if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
									raise ValueError(
									    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
									    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""     )

							# Define 3 blocks. Each block has its own normalization layer.
							# 1. Self-Attn
							if self.use_ada_layer_norm:
									lowerCAmelCase__        =   AdaLayerNorm(snake_case__  ,						snake_case__     )
							elif self.use_ada_layer_norm_zero:
									lowerCAmelCase__        =   AdaLayerNormZero(snake_case__  ,						snake_case__     )
							else:
									lowerCAmelCase__        =   nn.LayerNorm(snake_case__  ,						elementwise_affine=snake_case__     )
							lowerCAmelCase__        =   Attention(
							    query_dim=snake_case__  ,						heads=snake_case__  ,						dim_head=snake_case__  ,						dropout=snake_case__  ,						bias=snake_case__  ,						cross_attention_dim=cross_attention_dim if only_cross_attention else None  ,						upcast_attention=snake_case__  ,						)

							# 2. Cross-Attn
							if cross_attention_dim is not None or double_self_attention:
									# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
									# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
									# the second cross attention block.
									lowerCAmelCase__        =   (
									    AdaLayerNorm(snake_case__  ,						snake_case__     )
									    if self.use_ada_layer_norm
									    else nn.LayerNorm(snake_case__  ,						elementwise_affine=snake_case__     )
									)
									lowerCAmelCase__        =   Attention(
									    query_dim=snake_case__  ,						cross_attention_dim=cross_attention_dim if not double_self_attention else None  ,						heads=snake_case__  ,						dim_head=snake_case__  ,						dropout=snake_case__  ,						bias=snake_case__  ,						upcast_attention=snake_case__  ,						)  # is self-attn if encoder_hidden_states is none
							else:
									lowerCAmelCase__        =   None
									lowerCAmelCase__        =   None

							# 3. Feed-forward
							lowerCAmelCase__        =   nn.LayerNorm(snake_case__  ,						elementwise_affine=snake_case__     )
							lowerCAmelCase__        =   FeedForward(snake_case__  ,						dropout=snake_case__  ,						activation_fn=snake_case__  ,						final_dropout=snake_case__     )

							# let chunk size default to None
							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     int     ):
							# Sets chunk feed-forward
							lowerCAmelCase__        =   chunk_size
							lowerCAmelCase__        =   dim




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[torch.FloatTensor] = None  ,						snake_case__						:     Optional[torch.FloatTensor] = None  ,						snake_case__						:     Optional[torch.FloatTensor] = None  ,						snake_case__						:     Optional[torch.LongTensor] = None  ,						snake_case__						:     Dict[str, Any] = None  ,						snake_case__						:     Optional[torch.LongTensor] = None  ,						):
							# Notice that normalization is always applied before the real computation in the following blocks.
							# 1. Self-Attention
							if self.use_ada_layer_norm:
									lowerCAmelCase__        =   self.norma(snake_case__  ,						snake_case__     )
							elif self.use_ada_layer_norm_zero:
									lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self.norma(
									    snake_case__  ,						snake_case__  ,						snake_case__  ,						hidden_dtype=hidden_states.dtype     )
							else:
									lowerCAmelCase__        =   self.norma(snake_case__     )

							lowerCAmelCase__        =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

							lowerCAmelCase__        =   self.attna(
							    snake_case__  ,						encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None  ,						attention_mask=snake_case__  ,						**snake_case__  ,						)
							if self.use_ada_layer_norm_zero:
									lowerCAmelCase__        =   gate_msa.unsqueeze(1     ) * attn_output
							lowerCAmelCase__        =   attn_output + hidden_states

							# 2. Cross-Attention
							if self.attna is not None:
									lowerCAmelCase__        =   (
									    self.norma(snake_case__  ,						snake_case__     ) if self.use_ada_layer_norm else self.norma(snake_case__     )
									)

									lowerCAmelCase__        =   self.attna(
									    snake_case__  ,						encoder_hidden_states=snake_case__  ,						attention_mask=snake_case__  ,						**snake_case__  ,						)
									lowerCAmelCase__        =   attn_output + hidden_states

							# 3. Feed-forward
							lowerCAmelCase__        =   self.norma(snake_case__     )

							if self.use_ada_layer_norm_zero:
									lowerCAmelCase__        =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

							if self._chunk_size is not None:
									# "feed_forward_chunk_size" can be used to save memory
									if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
											raise ValueError(
											    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""     )

									lowerCAmelCase__        =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
									lowerCAmelCase__        =   torch.cat(
									    [self.ff(snake_case__     ) for hid_slice in norm_hidden_states.chunk(snake_case__  ,						dim=self._chunk_dim     )]  ,						dim=self._chunk_dim  ,						)
							else:
									lowerCAmelCase__        =   self.ff(snake_case__     )

							if self.use_ada_layer_norm_zero:
									lowerCAmelCase__        =   gate_mlp.unsqueeze(1     ) * ff_output

							lowerCAmelCase__        =   ff_output + hidden_states

							return hidden_states

class       a_      (     nn.Module			):




					def __init__(	self						:     str  ,						snake_case__						:     int  ,						snake_case__						:     Optional[int] = None  ,						snake_case__						:     int = 4  ,						snake_case__						:     float = 0.0  ,						snake_case__						:     str = "geglu"  ,						snake_case__						:     bool = False  ,						):
							super().__init__()
							lowerCAmelCase__        =   int(dim * mult     )
							lowerCAmelCase__        =   dim_out if dim_out is not None else dim

							if activation_fn == "gelu":
									lowerCAmelCase__        =   GELU(snake_case__  ,						snake_case__     )
							if activation_fn == "gelu-approximate":
									lowerCAmelCase__        =   GELU(snake_case__  ,						snake_case__  ,						approximate="""tanh"""     )
							elif activation_fn == "geglu":
									lowerCAmelCase__        =   GEGLU(snake_case__  ,						snake_case__     )
							elif activation_fn == "geglu-approximate":
									lowerCAmelCase__        =   ApproximateGELU(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   nn.ModuleList([]     )
							# project in
							self.net.append(snake_case__     )
							# project dropout
							self.net.append(nn.Dropout(snake_case__     )     )
							# project out
							self.net.append(nn.Linear(snake_case__  ,						snake_case__     )     )
							# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
							if final_dropout:
									self.net.append(nn.Dropout(snake_case__     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Dict     ):
							for module in self.net:
									lowerCAmelCase__        =   module(snake_case__     )
							return hidden_states

class       a_      (     nn.Module			):




					def __init__(	self						:     Tuple  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     str = "none"     ):
							super().__init__()
							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   approximate




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     str     ):
							if gate.device.type != "mps":
									return F.gelu(snake_case__  ,						approximate=self.approximate     )
							# mps: gelu is not implemented for float16
							return F.gelu(gate.to(dtype=torch.floataa     )  ,						approximate=self.approximate     ).to(dtype=gate.dtype     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   self.proj(snake_case__     )
							lowerCAmelCase__        =   self.gelu(snake_case__     )
							return hidden_states

class       a_      (     nn.Module			):




					def __init__(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							super().__init__()
							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						dim_out * 2     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     int     ):
							if gate.device.type != "mps":
									return F.gelu(snake_case__     )
							# mps: gelu is not implemented for float16
							return F.gelu(gate.to(dtype=torch.floataa     )     ).to(dtype=gate.dtype     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.proj(snake_case__     ).chunk(2  ,						dim=-1     )
							return hidden_states * self.gelu(snake_case__     )

class       a_      (     nn.Module			):




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							super().__init__()
							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   self.proj(snake_case__     )
							return x * torch.sigmoid(1.702 * x     )

class       a_      (     nn.Module			):




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Optional[int]     ):
							super().__init__()
							lowerCAmelCase__        =   nn.Embedding(snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   nn.SiLU()
							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						embedding_dim * 2     )
							lowerCAmelCase__        =   nn.LayerNorm(snake_case__  ,						elementwise_affine=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.linear(self.silu(self.emb(snake_case__     )     )     )
							lowerCAmelCase__   ,					lowerCAmelCase__        =   torch.chunk(snake_case__  ,						2     )
							lowerCAmelCase__        =   self.norm(snake_case__     ) * (1 + scale) + shift
							return x

class       a_      (     nn.Module			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     int  ,						snake_case__						:     List[Any]     ):
							super().__init__()

							lowerCAmelCase__        =   CombinedTimestepLabelEmbeddings(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   nn.SiLU()
							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						6 * embedding_dim  ,						bias=snake_case__     )
							lowerCAmelCase__        =   nn.LayerNorm(snake_case__  ,						elementwise_affine=snake_case__  ,						eps=1E-6     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     str  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Dict=None     ):
							lowerCAmelCase__        =   self.linear(self.silu(self.emb(snake_case__  ,						snake_case__  ,						hidden_dtype=snake_case__     )     )     )
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   emb.chunk(6  ,						dim=1     )
							lowerCAmelCase__        =   self.norm(snake_case__     ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
							return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

class       a_      (     nn.Module			):




					def __init__(	self						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[str] = None  ,						snake_case__						:     float = 1E-5     ):
							super().__init__()
							lowerCAmelCase__        =   num_groups
							lowerCAmelCase__        =   eps

							if act_fn is None:
									lowerCAmelCase__        =   None
							else:
									lowerCAmelCase__        =   get_activation(snake_case__     )

							lowerCAmelCase__        =   nn.Linear(snake_case__  ,						out_dim * 2     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     List[str]  ,						snake_case__						:     Tuple     ):
							if self.act:
									lowerCAmelCase__        =   self.act(snake_case__     )
							lowerCAmelCase__        =   self.linear(snake_case__     )
							lowerCAmelCase__        =   emb[:, :, None, None]
							lowerCAmelCase__   ,					lowerCAmelCase__        =   emb.chunk(2  ,						dim=1     )

							lowerCAmelCase__        =   F.group_norm(snake_case__  ,						self.num_groups  ,						eps=self.eps     )
							lowerCAmelCase__        =   x * (1 + scale) + shift
							return x





"""simple docstring"""




import copy
import os

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

class       a_      :




					def __init__(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   256
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							plt.hist(self.img.ravel()  ,						256  ,						[0, 256]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							cva.imshow("""Output-Image"""  ,						self.img     )
							cva.imshow("""Input-Image"""  ,						self.original_image     )
							cva.waitKey(5000     )
							cva.destroyAllWindows()


if __name__ == "__main__":
			__lowerCAmelCase    :							Dict													=						os.path.join(os.path.basename(__file__), "image_data/input.jpg")
			__lowerCAmelCase    :							Optional[int]													=						ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()




