






from dataclasses import dataclass
from typing import Dict, Optional, Union

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:torch.FloatTensor



class  lowerCAmelCase_       (				snake_case__     ,    snake_case__					):




							@register_to_config
							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int = 32 ,							SCREAMING_SNAKE_CASE_     :   int = 64 ,							SCREAMING_SNAKE_CASE_     :   int = 20 ,							SCREAMING_SNAKE_CASE_     :   int = 768 ,							SCREAMING_SNAKE_CASE_     :   List[str]=77 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=4 ,							SCREAMING_SNAKE_CASE_     :   float = 0.0 ,							SCREAMING_SNAKE_CASE_     :   str = "silu" ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "linear" ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "prd" ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = None ,							):
											super().__init__()
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						attention_head_dim
											lowerCAmelCase__      			=						num_attention_heads * attention_head_dim
											lowerCAmelCase__      			=						additional_embeddings

											lowerCAmelCase__      			=						time_embed_dim or inner_dim
											lowerCAmelCase__      			=						embedding_proj_dim or embedding_dim
											lowerCAmelCase__      			=						clip_embed_dim or embedding_dim

											lowerCAmelCase__      			=						Timesteps(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							0	)
											lowerCAmelCase__      			=						TimestepEmbedding(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							out_dim=SCREAMING_SNAKE_CASE_ ,							act_fn=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						nn.Linear(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											if embedding_proj_norm_type is None:
															lowerCAmelCase__      			=						None
											elif embedding_proj_norm_type == "layer":
															lowerCAmelCase__      			=						nn.LayerNorm(SCREAMING_SNAKE_CASE_	)
											else:
															raise ValueError(f'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'	)

											lowerCAmelCase__      			=						nn.Linear(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											if encoder_hid_proj_type is None:
															lowerCAmelCase__      			=						None
											elif encoder_hid_proj_type == "linear":
															lowerCAmelCase__      			=						nn.Linear(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
											else:
															raise ValueError(f'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'	)

											lowerCAmelCase__      			=						nn.Parameter(torch.zeros(1 ,							num_embeddings + additional_embeddings ,							SCREAMING_SNAKE_CASE_	)	)

											if added_emb_type == "prd":
															lowerCAmelCase__      			=						nn.Parameter(torch.zeros(1 ,							1 ,							SCREAMING_SNAKE_CASE_	)	)
											elif added_emb_type is None:
															lowerCAmelCase__      			=						None
											else:
															raise ValueError(
															    f'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'	)

											lowerCAmelCase__      			=						nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dropout=SCREAMING_SNAKE_CASE_ ,							activation_fn='''gelu''' ,							attention_bias=SCREAMING_SNAKE_CASE_ ,							)
											        for d in range(SCREAMING_SNAKE_CASE_	)
											    ]	)

											if norm_in_type == "layer":
															lowerCAmelCase__      			=						nn.LayerNorm(SCREAMING_SNAKE_CASE_	)
											elif norm_in_type is None:
															lowerCAmelCase__      			=						None
											else:
															raise ValueError(f'Unsupported norm_in_type: {norm_in_type}.'	)

											lowerCAmelCase__      			=						nn.LayerNorm(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						nn.Linear(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.full(
											    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] ,							-10_000.0	)
											causal_attention_mask.triu_(1	)
											lowerCAmelCase__      			=						causal_attention_mask[None, ...]
											self.register_buffer('''causal_attention_mask''' ,							SCREAMING_SNAKE_CASE_ ,							persistent=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						nn.Parameter(torch.zeros(1 ,							SCREAMING_SNAKE_CASE_	)	)
											lowerCAmelCase__      			=						nn.Parameter(torch.zeros(1 ,							SCREAMING_SNAKE_CASE_	)	)




							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						{}

											def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   torch.nn.Module ,							SCREAMING_SNAKE_CASE_     :   Dict[str, AttentionProcessor]	):
															if hasattr(SCREAMING_SNAKE_CASE_ ,							'''set_processor'''	):
																			lowerCAmelCase__      			=						module.processor

															for sub_name, child in module.named_children():
																			fn_recursive_add_processors(f'{name}.{sub_name}' ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

															return processors

											for name, module in self.named_children():
															fn_recursive_add_processors(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											return processors




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Union[AttentionProcessor, Dict[str, AttentionProcessor]]	):
											lowerCAmelCase__      			=						len(self.attn_processors.keys()	)

											if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) != count:
															raise ValueError(
															    f'A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_	)} does not match the'
															    f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'	)

											def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   torch.nn.Module ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
															if hasattr(SCREAMING_SNAKE_CASE_ ,							'''set_processor'''	):
																			if not isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																							module.set_processor(SCREAMING_SNAKE_CASE_	)
																			else:
																							module.set_processor(processor.pop(f'{name}.processor'	)	)

															for sub_name, child in module.named_children():
																			fn_recursive_attn_processor(f'{name}.{sub_name}' ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											for name, module in self.named_children():
															fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											self.set_attn_processor(AttnProcessor()	)




							def 						__snake_case					(   self     :   int ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[torch.Tensor, float, int] ,							SCREAMING_SNAKE_CASE_     :   torch.FloatTensor ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.FloatTensor] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.BoolTensor] = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						hidden_states.shape[0]

											lowerCAmelCase__      			=						timestep
											if not torch.is_tensor(SCREAMING_SNAKE_CASE_	):
															lowerCAmelCase__      			=						torch.tensor([timesteps] ,							dtype=torch.long ,							device=hidden_states.device	)
											elif torch.is_tensor(SCREAMING_SNAKE_CASE_	) and len(timesteps.shape	) == 0:
															lowerCAmelCase__      			=						timesteps[None].to(hidden_states.device	)

											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											lowerCAmelCase__      			=						timesteps * torch.ones(SCREAMING_SNAKE_CASE_ ,							dtype=timesteps.dtype ,							device=timesteps.device	)

											lowerCAmelCase__      			=						self.time_proj(SCREAMING_SNAKE_CASE_	)

											# timesteps does not contain any weights and will always return f32 tensors
											# but time_embedding might be fp16, so we need to cast here.
											lowerCAmelCase__      			=						timesteps_projected.to(dtype=self.dtype	)
											lowerCAmelCase__      			=						self.time_embedding(SCREAMING_SNAKE_CASE_	)

											if self.embedding_proj_norm is not None:
															lowerCAmelCase__      			=						self.embedding_proj_norm(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.embedding_proj(SCREAMING_SNAKE_CASE_	)
											if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
															lowerCAmelCase__      			=						self.encoder_hidden_states_proj(SCREAMING_SNAKE_CASE_	)
											elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
															raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''	)

											lowerCAmelCase__      			=						self.proj_in(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.positional_embedding.to(hidden_states.dtype	)

											lowerCAmelCase__      			=						[]
											lowerCAmelCase__      			=						0

											if encoder_hidden_states is not None:
															additional_embeds.append(SCREAMING_SNAKE_CASE_	)
															additional_embeddings_len += encoder_hidden_states.shape[1]

											if len(proj_embeddings.shape	) == 2:
															lowerCAmelCase__      			=						proj_embeddings[:, None, :]

											if len(hidden_states.shape	) == 2:
															lowerCAmelCase__      			=						hidden_states[:, None, :]

											lowerCAmelCase__      			=						additional_embeds + [
											    proj_embeddings,
											    time_embeddings[:, None, :],
											    hidden_states,
											]

											if self.prd_embedding is not None:
															lowerCAmelCase__      			=						self.prd_embedding.to(hidden_states.dtype	).expand(SCREAMING_SNAKE_CASE_ ,							-1 ,							-1	)
															additional_embeds.append(SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.cat(
											    SCREAMING_SNAKE_CASE_ ,							dim=1 ,							)

											# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
											lowerCAmelCase__      			=						additional_embeddings_len + proj_embeddings.shape[1] + 1
											if positional_embeddings.shape[1] < hidden_states.shape[1]:
															lowerCAmelCase__      			=						F.pad(
															    SCREAMING_SNAKE_CASE_ ,							(
															        0,
															        0,
															        additional_embeddings_len,
															        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
															    ) ,							value=0.0 ,							)

											lowerCAmelCase__      			=						hidden_states + positional_embeddings

											if attention_mask is not None:
															lowerCAmelCase__      			=						(1 - attention_mask.to(hidden_states.dtype	)) * -10_000.0
															lowerCAmelCase__      			=						F.pad(SCREAMING_SNAKE_CASE_ ,							(0, self.additional_embeddings) ,							value=0.0	)
															lowerCAmelCase__      			=						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype	)
															lowerCAmelCase__      			=						attention_mask.repeat_interleave(self.config.num_attention_heads ,							dim=0	)

											if self.norm_in is not None:
															lowerCAmelCase__      			=						self.norm_in(SCREAMING_SNAKE_CASE_	)

											for block in self.transformer_blocks:
															lowerCAmelCase__      			=						block(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.norm_out(SCREAMING_SNAKE_CASE_	)

											if self.prd_embedding is not None:
															lowerCAmelCase__      			=						hidden_states[:, -1]
											else:
															lowerCAmelCase__      			=						hidden_states[:, additional_embeddings_len:]

											lowerCAmelCase__      			=						self.proj_to_clip_embeddings(SCREAMING_SNAKE_CASE_	)

											if not return_dict:
															return (predicted_image_embedding,)

											return PriorTransformerOutput(predicted_image_embedding=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any]	):
											lowerCAmelCase__      			=						(prior_latents * self.clip_std) + self.clip_mean
											return prior_latents













import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class  lowerCAmelCase_       :




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int	):
											if dst_width < 0 or dst_height < 0:
															raise ValueError('''Destination width/height should be > 0'''	)

											lowerCAmelCase__      			=						img
											lowerCAmelCase__      			=						img.shape[1]
											lowerCAmelCase__      			=						img.shape[0]
											lowerCAmelCase__      			=						dst_width
											lowerCAmelCase__      			=						dst_height

											lowerCAmelCase__      			=						self.src_w / self.dst_w
											lowerCAmelCase__      			=						self.src_h / self.dst_h

											lowerCAmelCase__      			=						lowerCAmelCase__      			=						(
											    np.ones((self.dst_h, self.dst_w, 3) ,							np.uinta	) * 255
											)




							def 						__snake_case					(   self     :   List[str]	):
											for i in range(self.dst_h	):
															for j in range(self.dst_w	):
																			lowerCAmelCase__      			=						self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_x * x	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_y * y	)


if __name__ == "__main__":
			_UpperCAmelCase				,   _UpperCAmelCase			:		List[str]   				=    800, 600
			_UpperCAmelCase			:		Tuple   				=    imread("image_data/lena.jpg", 1)
			_UpperCAmelCase			:		str   				=    NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
			)
			waitKey(0)
			destroyAllWindows()





