import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def 					__lowerCAmelCase			(  _A ,_A ,_A = False ,_A = 1 ,_A = 1 ,_A = 10_000 ,):



							"""simple docstring"""


							assert len(timesteps.shape					) == 1, "Timesteps should be a 1d-array"

							_lowercase				=	embedding_dim // 2
							_lowercase				=	-math.log(UpperCamelCase_					) * torch.arange(
							    start=0 ,end=UpperCamelCase_ ,dtype=torch.floataa ,device=timesteps.device					)
							_lowercase				=	exponent / (half_dim - downscale_freq_shift)

							_lowercase				=	torch.exp(UpperCamelCase_					)
							_lowercase				=	timesteps[:, None].float() * emb[None, :]

							# scale embeddings
							_lowercase				=	scale * emb

							# concat sine and cosine embeddings
							_lowercase				=	torch.cat([torch.sin(UpperCamelCase_					), torch.cos(UpperCamelCase_					)] ,dim=-1					)

							# flip sine and cosine embeddings
							if flip_sin_to_cos:
														_lowercase				=	torch.cat([emb[:, half_dim:], emb[:, :half_dim]] ,dim=-1					)

							# zero pad
							if embedding_dim % 2 == 1:
														_lowercase				=	torch.nn.functional.pad(UpperCamelCase_ ,(0, 1, 0, 0)					)
							return emb


def 					__lowerCAmelCase			(  _A ,_A ,_A=False ,_A=0					):



							"""simple docstring"""


							_lowercase				=	np.arange(UpperCamelCase_ ,dtype=np.floataa					)
							_lowercase				=	np.arange(UpperCamelCase_ ,dtype=np.floataa					)
							_lowercase				=	np.meshgrid(UpperCamelCase_ ,UpperCamelCase_					)  # here w goes first
							_lowercase				=	np.stack(UpperCamelCase_ ,axis=0					)

							_lowercase				=	grid.reshape([2, 1, grid_size, grid_size]					)
							_lowercase				=	get_ad_sincos_pos_embed_from_grid(UpperCamelCase_ ,UpperCamelCase_					)
							if cls_token and extra_tokens > 0:
														_lowercase				=	np.concatenate([np.zeros([extra_tokens, embed_dim]					), pos_embed] ,axis=0					)
							return pos_embed


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							if embed_dim % 2 != 0:
														raise ValueError("""embed_dim must be divisible by 2"""					)

							# use half of dimensions to encode grid_h
							_lowercase				=	get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,grid[0]					)  # (H*W, D/2)
							_lowercase				=	get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,grid[1]					)  # (H*W, D/2)

							_lowercase				=	np.concatenate([emb_h, emb_w] ,axis=1					)  # (H*W, D)
							return emb


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							if embed_dim % 2 != 0:
														raise ValueError("""embed_dim must be divisible by 2"""					)

							_lowercase				=	np.arange(embed_dim // 2 ,dtype=np.floataa					)
							omega /= embed_dim / 2.0
							_lowercase				=	1.0 / 10_000**omega  # (D/2,)

							_lowercase				=	pos.reshape(-1					)  # (M,)
							_lowercase				=	np.einsum("""m,d->md""" ,UpperCamelCase_ ,UpperCamelCase_					)  # (M, D/2), outer product

							_lowercase				=	np.sin(UpperCamelCase_					)  # (M, D/2)
							_lowercase				=	np.cos(UpperCamelCase_					)  # (M, D/2)

							_lowercase				=	np.concatenate([emb_sin, emb_cos] ,axis=1					)  # (M, D)
							return emb


class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase=224     ,   UpperCAmelCase=224     ,   UpperCAmelCase=16     ,   UpperCAmelCase=3     ,   UpperCAmelCase=768     ,   UpperCAmelCase=False     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	(height // patch_size) * (width // patch_size)
														_lowercase				=	flatten
														_lowercase				=	layer_norm

														_lowercase				=	nn.Convad(
														    __snake_case     ,   __snake_case     ,   kernel_size=(patch_size, patch_size)     ,   stride=__snake_case     ,   bias=__snake_case  )
														if layer_norm:
																					_lowercase				=	nn.LayerNorm(__snake_case     ,   elementwise_affine=__snake_case     ,   eps=1e-6  )
														else:
																					_lowercase				=	None

														_lowercase				=	get_ad_sincos_pos_embed(__snake_case     ,   int(num_patches**0.5  )  )
														self.register_buffer("""pos_embed"""     ,   torch.from_numpy(__snake_case  ).float().unsqueeze(0  )     ,   persistent=__snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.proj(__snake_case  )
														if self.flatten:
																					_lowercase				=	latent.flatten(2  ).transpose(1     ,   2  )  # BCHW -> BNC
														if self.layer_norm:
																					_lowercase				=	self.norm(__snake_case  )
														return latent + self.pos_embed






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = "silu"     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase=None     ,   ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )

														if cond_proj_dim is not None:
																					_lowercase				=	nn.Linear(__snake_case     ,   __snake_case     ,   bias=__snake_case  )
														else:
																					_lowercase				=	None

														_lowercase				=	get_activation(__snake_case  )

														if out_dim is not None:
																					_lowercase				=	out_dim
														else:
																					_lowercase				=	time_embed_dim
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )

														if post_act_fn is None:
																					_lowercase				=	None
														else:
																					_lowercase				=	get_activation(__snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														if condition is not None:
																					_lowercase				=	sample + self.cond_proj(__snake_case  )
														_lowercase				=	self.linear_a(__snake_case  )

														if self.act is not None:
																					_lowercase				=	self.act(__snake_case  )

														_lowercase				=	self.linear_a(__snake_case  )

														if self.post_act is not None:
																					_lowercase				=	self.post_act(__snake_case  )
														return sample






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	num_channels
														_lowercase				=	flip_sin_to_cos
														_lowercase				=	downscale_freq_shift



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	get_timestep_embedding(
														    __snake_case     ,   self.num_channels     ,   flip_sin_to_cos=self.flip_sin_to_cos     ,   downscale_freq_shift=self.downscale_freq_shift     ,   )
														return t_emb






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 256     ,   UpperCAmelCase = 1.0     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase=False  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	nn.Parameter(torch.randn(__snake_case  ) * scale     ,   requires_grad=__snake_case  )
														_lowercase				=	log
														_lowercase				=	flip_sin_to_cos

														if set_W_to_weight:
																					# to delete later
																					_lowercase				=	nn.Parameter(torch.randn(__snake_case  ) * scale     ,   requires_grad=__snake_case  )

																					_lowercase				=	self.W



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														if self.log:
																					_lowercase				=	torch.log(__snake_case  )

														_lowercase				=	x[:, None] * self.weight[None, :] * 2 * np.pi

														if self.flip_sin_to_cos:
																					_lowercase				=	torch.cat([torch.cos(__snake_case  ), torch.sin(__snake_case  )]     ,   dim=-1  )
														else:
																					_lowercase				=	torch.cat([torch.sin(__snake_case  ), torch.cos(__snake_case  )]     ,   dim=-1  )
														return out






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	height
														_lowercase				=	width
														_lowercase				=	num_embed
														_lowercase				=	embed_dim

														_lowercase				=	nn.Embedding(self.num_embed     ,   __snake_case  )
														_lowercase				=	nn.Embedding(self.height     ,   __snake_case  )
														_lowercase				=	nn.Embedding(self.width     ,   __snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.emb(__snake_case  )

														_lowercase				=	self.height_emb(torch.arange(self.height     ,   device=index.device  ).view(1     ,   self.height  )  )

														# 1 x H x D -> 1 x H x 1 x D
														_lowercase				=	height_emb.unsqueeze(2  )

														_lowercase				=	self.width_emb(torch.arange(self.width     ,   device=index.device  ).view(1     ,   self.width  )  )

														# 1 x W x D -> 1 x 1 x W x D
														_lowercase				=	width_emb.unsqueeze(1  )

														_lowercase				=	height_emb + width_emb

														# 1 x H x W x D -> 1 x L xD
														_lowercase				=	pos_emb.view(1     ,   self.height * self.width     ,   -1  )

														_lowercase				=	emb + pos_emb[:, : emb.shape[1], :]

														return emb






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	dropout_prob > 0
														_lowercase				=	nn.Embedding(num_classes + use_cfg_embedding     ,   __snake_case  )
														_lowercase				=	num_classes
														_lowercase				=	dropout_prob



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														if force_drop_ids is None:
																					_lowercase				=	torch.rand(labels.shape[0]     ,   device=labels.device  ) < self.dropout_prob
														else:
																					_lowercase				=	torch.tensor(force_drop_ids == 1  )
														_lowercase				=	torch.where(__snake_case     ,   self.num_classes     ,   __snake_case  )
														return labels



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														_lowercase				=	self.dropout_prob > 0
														if (self.training and use_dropout) or (force_drop_ids is not None):
																					_lowercase				=	self.token_drop(__snake_case     ,   __snake_case  )
														_lowercase				=	self.embedding_table(__snake_case  )
														return embeddings






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 1024     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 10     ,   ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	num_image_text_embeds
														_lowercase				=	nn.Linear(__snake_case     ,   self.num_image_text_embeds * cross_attention_dim  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	text_embeds.shape[0]

														# image
														_lowercase				=	self.image_embeds(__snake_case  )
														_lowercase				=	image_text_embeds.reshape(__snake_case     ,   self.num_image_text_embeds     ,   -1  )

														# text
														_lowercase				=	self.text_proj(__snake_case  )

														return torch.cat([image_text_embeds, text_embeds]     ,   dim=1  )






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 32     ,   ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	num_image_text_embeds
														_lowercase				=	nn.Linear(__snake_case     ,   self.num_image_text_embeds * cross_attention_dim  )
														_lowercase				=	nn.LayerNorm(__snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	image_embeds.shape[0]

														# image
														_lowercase				=	self.image_embeds(__snake_case  )
														_lowercase				=	image_embeds.reshape(__snake_case     ,   self.num_image_text_embeds     ,   -1  )
														_lowercase				=	self.norm(__snake_case  )
														return image_embeds






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=0.1  ):







														'''simple docstring'''






														super().__init__()

														_lowercase				=	Timesteps(num_channels=256     ,   flip_sin_to_cos=__snake_case     ,   downscale_freq_shift=1  )
														_lowercase				=	TimestepEmbedding(in_channels=256     ,   time_embed_dim=__snake_case  )
														_lowercase				=	LabelEmbedding(__snake_case     ,   __snake_case     ,   __snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														_lowercase				=	self.time_proj(__snake_case  )
														_lowercase				=	self.timestep_embedder(timesteps_proj.to(dtype=__snake_case  )  )  # (N, D)

														_lowercase				=	self.class_embedder(__snake_case  )  # (N, D)

														_lowercase				=	timesteps_emb + class_labels  # (N, D)

														return conditioning






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 64  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	nn.LayerNorm(__snake_case  )
														_lowercase				=	AttentionPooling(__snake_case     ,   __snake_case  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )
														_lowercase				=	nn.LayerNorm(__snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.norma(__snake_case  )
														_lowercase				=	self.pool(__snake_case  )
														_lowercase				=	self.proj(__snake_case  )
														_lowercase				=	self.norma(__snake_case  )
														return hidden_states






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 1536  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )
														_lowercase				=	nn.LayerNorm(__snake_case  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.text_proj(__snake_case  )
														_lowercase				=	self.text_norm(__snake_case  )

														# image
														_lowercase				=	self.image_proj(__snake_case  )

														return time_image_embeds + time_text_embeds






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 1536  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )
														_lowercase				=	nn.LayerNorm(__snake_case  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.image_proj(__snake_case  )
														_lowercase				=	self.image_norm(__snake_case  )
														return time_image_embeds






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase = 768     ,   UpperCAmelCase = 1536  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case  )
														_lowercase				=	nn.LayerNorm(__snake_case  )
														_lowercase				=	nn.Sequential(
														    nn.Convad(3     ,   16     ,   3     ,   padding=1  )     ,   nn.SiLU()     ,   nn.Convad(16     ,   16     ,   3     ,   padding=1  )     ,   nn.SiLU()     ,   nn.Convad(16     ,   32     ,   3     ,   padding=1     ,   stride=2  )     ,   nn.SiLU()     ,   nn.Convad(32     ,   32     ,   3     ,   padding=1  )     ,   nn.SiLU()     ,   nn.Convad(32     ,   96     ,   3     ,   padding=1     ,   stride=2  )     ,   nn.SiLU()     ,   nn.Convad(96     ,   96     ,   3     ,   padding=1  )     ,   nn.SiLU()     ,   nn.Convad(96     ,   256     ,   3     ,   padding=1     ,   stride=2  )     ,   nn.SiLU()     ,   nn.Convad(256     ,   4     ,   3     ,   padding=1  )     ,   )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	self.image_proj(__snake_case  )
														_lowercase				=	self.image_norm(__snake_case  )
														_lowercase				=	self.input_hint_block(__snake_case  )
														return time_image_embeds, hint






class _lowercase					(       nn.Module						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														super().__init__()
														_lowercase				=	dtype
														_lowercase				=	nn.Parameter(torch.randn(1     ,   __snake_case  ) / embed_dim**0.5  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case     ,   dtype=self.dtype  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case     ,   dtype=self.dtype  )
														_lowercase				=	nn.Linear(__snake_case     ,   __snake_case     ,   dtype=self.dtype  )
														_lowercase				=	num_heads
														_lowercase				=	embed_dim // self.num_heads



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				,       _lowercase				,       _lowercase				=	x.size()

														def shape(UpperCAmelCase  ):
																					# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																					_lowercase				=	x.view(__snake_case     ,   -1     ,   self.num_heads     ,   self.dim_per_head  )
																					# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
																					_lowercase				=	x.transpose(1     ,   2  )
																					# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																					_lowercase				=	x.reshape(bs * self.num_heads     ,   -1     ,   self.dim_per_head  )
																					# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
																					_lowercase				=	x.transpose(1     ,   2  )
																					return x

														_lowercase				=	x.mean(dim=1     ,   keepdim=__snake_case  ) + self.positional_embedding.to(x.dtype  )
														_lowercase				=	torch.cat([class_token, x]     ,   dim=1  )  # (bs, length+1, width)

														# (bs*n_heads, class_token_length, dim_per_head)
														_lowercase				=	shape(self.q_proj(__snake_case  )  )
														# (bs*n_heads, length+class_token_length, dim_per_head)
														_lowercase				=	shape(self.k_proj(__snake_case  )  )
														_lowercase				=	shape(self.v_proj(__snake_case  )  )

														# (bs*n_heads, class_token_length, length+class_token_length):
														_lowercase				=	1 / math.sqrt(math.sqrt(self.dim_per_head  )  )
														_lowercase				=	torch.einsum("""bct,bcs->bts"""     ,   q * scale     ,   k * scale  )  # More stable with f16 than dividing afterwards
														_lowercase				=	torch.softmax(weight.float()     ,   dim=-1  ).type(weight.dtype  )

														# (bs*n_heads, dim_per_head, class_token_length)
														_lowercase				=	torch.einsum("""bts,bcs->bct"""     ,   __snake_case     ,   __snake_case  )

														# (bs, length+1, width)
														_lowercase				=	a.reshape(__snake_case     ,   -1     ,   1  ).transpose(1     ,   2  )

														return a[:, 0, :]  # cls_token



import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A_:      Tuple        =			logging.get_logger(__name__)


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):







														'''simple docstring'''






														warnings.warn(
														    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
														    """ Please use MobileNetV2ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )


