




"""simple docstring"""




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = False					,							__UpperCamelCase = 1					,							__UpperCamelCase = 1					,							__UpperCamelCase = 1_00_00					,							):
			assert len(timesteps.shape					) == 1, "Timesteps should be a 1d-array"

			__lowercase	:       Dict				=   embedding_dim // 2
			__lowercase	:       Union[str, Any]				=   -math.log(__UpperCamelCase					) * torch.arange(
			    start=0					,							end=__UpperCamelCase					,							dtype=torch.floataa					,							device=timesteps.device					)
			__lowercase	:       Union[str, Any]				=   exponent / (half_dim - downscale_freq_shift)

			__lowercase	:       Tuple				=   torch.exp(__UpperCamelCase					)
			__lowercase	:       Dict				=   timesteps[:, None].float() * emb[None, :]

			# scale embeddings
			__lowercase	:       Optional[int]				=   scale * emb

			# concat sine and cosine embeddings
			__lowercase	:       str				=   torch.cat([torch.sin(__UpperCamelCase					), torch.cos(__UpperCamelCase					)]					,							dim=-1					)

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

			# zero pad
			if embedding_dim % 2 == 1:
						__lowercase	:       List[str]				=   torch.nn.functional.pad(__UpperCamelCase					,							(0, 1, 0, 0)					)
			return emb







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase=False					,							__UpperCamelCase=0					):
			__lowercase	:       Dict				=   np.arange(__UpperCamelCase					,							dtype=np.floataa					)
			__lowercase	:       List[str]				=   np.arange(__UpperCamelCase					,							dtype=np.floataa					)
			__lowercase	:       Optional[Any]				=   np.meshgrid(__UpperCamelCase					,							__UpperCamelCase					)  # here w goes first
			__lowercase	:       int				=   np.stack(__UpperCamelCase					,							axis=0					)

			__lowercase	:       int				=   grid.reshape([2, 1, grid_size, grid_size]					)
			__lowercase	:       int				=   get_ad_sincos_pos_embed_from_grid(__UpperCamelCase					,							__UpperCamelCase					)
			if cls_token and extra_tokens > 0:
						__lowercase	:       Optional[Any]				=   np.concatenate([np.zeros([extra_tokens, embed_dim]					), pos_embed]					,							axis=0					)
			return pos_embed







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			if embed_dim % 2 != 0:
						raise ValueError('''embed_dim must be divisible by 2'''					)

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

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







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			if embed_dim % 2 != 0:
						raise ValueError('''embed_dim must be divisible by 2'''					)

			__lowercase	:       Any				=   np.arange(embed_dim // 2					,							dtype=np.floataa					)
			omega /= embed_dim / 2.0
			__lowercase	:       Tuple				=   1.0 / 1_00_00**omega  # (D/2,)

			__lowercase	:       int				=   pos.reshape(-1					)  # (M,)
			__lowercase	:       List[str]				=   np.einsum('''m,d->md'''					,							__UpperCamelCase					,							__UpperCamelCase					)  # (M, D/2), outer product

			__lowercase	:       List[Any]				=   np.sin(__UpperCamelCase					)  # (M, D/2)
			__lowercase	:       Union[str, Any]				=   np.cos(__UpperCamelCase					)  # (M, D/2)

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

class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_=2_24							,		UpperCamelCase_=2_24							,		UpperCamelCase_=16							,		UpperCamelCase_=3							,		UpperCamelCase_=7_68							,		UpperCamelCase_=False							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		)		->   Dict:
					super().__init__()

					__lowercase	:       Dict				=   (height // patch_size) * (width // patch_size)
					__lowercase	:       Union[str, Any]				=   flatten
					__lowercase	:       List[Any]				=   layer_norm

					__lowercase	:       Optional[Any]				=   nn.Convad(
					    UpperCamelCase_							,		UpperCamelCase_							,		kernel_size=(patch_size, patch_size)							,		stride=UpperCamelCase_							,		bias=UpperCamelCase_   )
					if layer_norm:
								__lowercase	:       List[str]				=   nn.LayerNorm(UpperCamelCase_							,		elementwise_affine=UpperCamelCase_							,		eps=1E-6   )
					else:
								__lowercase	:       Any				=   None

					__lowercase	:       int				=   get_ad_sincos_pos_embed(UpperCamelCase_							,		int(num_patches**0.5   )   )
					self.register_buffer('''pos_embed'''							,		torch.from_numpy(UpperCamelCase_   ).float().unsqueeze(0   )							,		persistent=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					__lowercase	:       Optional[Any]				=   self.proj(UpperCamelCase_   )
					if self.flatten:
								__lowercase	:       List[Any]				=   latent.flatten(2   ).transpose(1							,		2   )  # BCHW -> BNC
					if self.layer_norm:
								__lowercase	:       List[str]				=   self.norm(UpperCamelCase_   )
					return latent + self.pos_embed


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = "silu"							,		UpperCamelCase_ = None							,		UpperCamelCase_ = None							,		UpperCamelCase_=None							,		)		->   str:
					super().__init__()

					__lowercase	:       Union[str, Any]				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )

					if cond_proj_dim is not None:
								__lowercase	:       Dict				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_							,		bias=UpperCamelCase_   )
					else:
								__lowercase	:       List[Any]				=   None

					__lowercase	:       List[str]				=   get_activation(UpperCamelCase_   )

					if out_dim is not None:
								__lowercase	:       List[str]				=   out_dim
					else:
								__lowercase	:       Optional[Any]				=   time_embed_dim
					__lowercase	:       Tuple				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )

					if post_act_fn is None:
								__lowercase	:       Dict				=   None
					else:
								__lowercase	:       int				=   get_activation(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   Optional[Any]:
					if condition is not None:
								__lowercase	:       Tuple				=   sample + self.cond_proj(UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   self.linear_a(UpperCamelCase_   )

					if self.act is not None:
								__lowercase	:       Dict				=   self.act(UpperCamelCase_   )

					__lowercase	:       int				=   self.linear_a(UpperCamelCase_   )

					if self.post_act is not None:
								__lowercase	:       Optional[Any]				=   self.post_act(UpperCamelCase_   )
					return sample


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   List[Any]:
					super().__init__()
					__lowercase	:       int				=   num_channels
					__lowercase	:       List[str]				=   flip_sin_to_cos
					__lowercase	:       Dict				=   downscale_freq_shift
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Tuple:
					__lowercase	:       List[str]				=   get_timestep_embedding(
					    UpperCamelCase_							,		self.num_channels							,		flip_sin_to_cos=self.flip_sin_to_cos							,		downscale_freq_shift=self.downscale_freq_shift							,		)
					return t_emb


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 2_56							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=False   )		->   str:
					super().__init__()
					__lowercase	:       int				=   nn.Parameter(torch.randn(UpperCamelCase_   ) * scale							,		requires_grad=UpperCamelCase_   )
					__lowercase	:       int				=   log
					__lowercase	:       List[Any]				=   flip_sin_to_cos

					if set_W_to_weight:
								# to delete later
								__lowercase	:       int				=   nn.Parameter(torch.randn(UpperCamelCase_   ) * scale							,		requires_grad=UpperCamelCase_   )

								__lowercase	:       List[str]				=   self.W
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Union[str, Any]:
					if self.log:
								__lowercase	:       int				=   torch.log(UpperCamelCase_   )

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

					if self.flip_sin_to_cos:
								__lowercase	:       str				=   torch.cat([torch.cos(UpperCamelCase_   ), torch.sin(UpperCamelCase_   )]							,		dim=-1   )
					else:
								__lowercase	:       Dict				=   torch.cat([torch.sin(UpperCamelCase_   ), torch.cos(UpperCamelCase_   )]							,		dim=-1   )
					return out


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		)		->   List[str]:
					super().__init__()

					__lowercase	:       List[Any]				=   height
					__lowercase	:       Optional[int]				=   width
					__lowercase	:       str				=   num_embed
					__lowercase	:       Dict				=   embed_dim

					__lowercase	:       int				=   nn.Embedding(self.num_embed							,		UpperCamelCase_   )
					__lowercase	:       List[str]				=   nn.Embedding(self.height							,		UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   nn.Embedding(self.width							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   str:
					__lowercase	:       str				=   self.emb(UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   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	:       int				=   height_emb.unsqueeze(2   )

					__lowercase	:       str				=   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	:       Tuple				=   width_emb.unsqueeze(1   )

					__lowercase	:       Dict				=   height_emb + width_emb

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

					__lowercase	:       Optional[int]				=   emb + pos_emb[:, : emb.shape[1], :]

					return emb


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   int:
					super().__init__()
					__lowercase	:       str				=   dropout_prob > 0
					__lowercase	:       Tuple				=   nn.Embedding(num_classes + use_cfg_embedding							,		UpperCamelCase_   )
					__lowercase	:       List[str]				=   num_classes
					__lowercase	:       Tuple				=   dropout_prob
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   int:
					if force_drop_ids is None:
								__lowercase	:       List[Any]				=   torch.rand(labels.shape[0]							,		device=labels.device   ) < self.dropout_prob
					else:
								__lowercase	:       Union[str, Any]				=   torch.tensor(force_drop_ids == 1   )
					__lowercase	:       Any				=   torch.where(UpperCamelCase_							,		self.num_classes							,		UpperCamelCase_   )
					return labels
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   List[str]:
					__lowercase	:       List[str]				=   self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
								__lowercase	:       int				=   self.token_drop(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       List[str]				=   self.embedding_table(UpperCamelCase_   )
					return embeddings


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 10_24							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 10							,		)		->   str:
					super().__init__()

					__lowercase	:       Optional[Any]				=   num_image_text_embeds
					__lowercase	:       str				=   nn.Linear(UpperCamelCase_							,		self.num_image_text_embeds * cross_attention_dim   )
					__lowercase	:       Any				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					__lowercase	:       int				=   text_embeds.shape[0]

					# image
					__lowercase	:       str				=   self.image_embeds(UpperCamelCase_   )
					__lowercase	:       Union[str, Any]				=   image_text_embeds.reshape(UpperCamelCase_							,		self.num_image_text_embeds							,		-1   )

					# text
					__lowercase	:       Dict				=   self.text_proj(UpperCamelCase_   )

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


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 32							,		)		->   Optional[Any]:
					super().__init__()

					__lowercase	:       Optional[int]				=   num_image_text_embeds
					__lowercase	:       Union[str, Any]				=   nn.Linear(UpperCamelCase_							,		self.num_image_text_embeds * cross_attention_dim   )
					__lowercase	:       Optional[Any]				=   nn.LayerNorm(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Any:
					__lowercase	:       Optional[int]				=   image_embeds.shape[0]

					# image
					__lowercase	:       List[str]				=   self.image_embeds(UpperCamelCase_   )
					__lowercase	:       Any				=   image_embeds.reshape(UpperCamelCase_							,		self.num_image_text_embeds							,		-1   )
					__lowercase	:       Optional[Any]				=   self.norm(UpperCamelCase_   )
					return image_embeds


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=0.1   )		->   int:
					super().__init__()

					__lowercase	:       List[Any]				=   Timesteps(num_channels=2_56							,		flip_sin_to_cos=UpperCamelCase_							,		downscale_freq_shift=1   )
					__lowercase	:       Optional[Any]				=   TimestepEmbedding(in_channels=2_56							,		time_embed_dim=UpperCamelCase_   )
					__lowercase	:       List[str]				=   LabelEmbedding(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   int:
					__lowercase	:       int				=   self.time_proj(UpperCamelCase_   )
					__lowercase	:       str				=   self.timestep_embedder(timesteps_proj.to(dtype=UpperCamelCase_   )   )  # (N, D)

					__lowercase	:       str				=   self.class_embedder(UpperCamelCase_   )  # (N, D)

					__lowercase	:       Optional[int]				=   timesteps_emb + class_labels  # (N, D)

					return conditioning


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = 64   )		->   Dict:
					super().__init__()
					__lowercase	:       Optional[Any]				=   nn.LayerNorm(UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   AttentionPooling(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       int				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       Dict				=   nn.LayerNorm(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Dict:
					__lowercase	:       List[str]				=   self.norma(UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   self.pool(UpperCamelCase_   )
					__lowercase	:       Dict				=   self.proj(UpperCamelCase_   )
					__lowercase	:       Any				=   self.norma(UpperCamelCase_   )
					return hidden_states


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 15_36   )		->   List[str]:
					super().__init__()
					__lowercase	:       Optional[int]				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       str				=   nn.LayerNorm(UpperCamelCase_   )
					__lowercase	:       List[Any]				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Dict:
					# text
					__lowercase	:       Optional[int]				=   self.text_proj(UpperCamelCase_   )
					__lowercase	:       Any				=   self.text_norm(UpperCamelCase_   )

					# image
					__lowercase	:       Dict				=   self.image_proj(UpperCamelCase_   )

					return time_image_embeds + time_text_embeds


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 15_36   )		->   Union[str, Any]:
					super().__init__()
					__lowercase	:       List[Any]				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       str				=   nn.LayerNorm(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Optional[int]:
					# image
					__lowercase	:       Union[str, Any]				=   self.image_proj(UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   self.image_norm(UpperCamelCase_   )
					return time_image_embeds


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_ = 7_68							,		UpperCamelCase_ = 15_36   )		->   Optional[Any]:
					super().__init__()
					__lowercase	:       Any				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       Any				=   nn.LayerNorm(UpperCamelCase_   )
					__lowercase	:       List[Any]				=   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							,		2_56							,		3							,		padding=1							,		stride=2   )							,		nn.SiLU()							,		nn.Convad(2_56							,		4							,		3							,		padding=1   )							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Any:
					# image
					__lowercase	:       Union[str, Any]				=   self.image_proj(UpperCamelCase_   )
					__lowercase	:       Union[str, Any]				=   self.image_norm(UpperCamelCase_   )
					__lowercase	:       Tuple				=   self.input_hint_block(UpperCamelCase_   )
					return time_image_embeds, hint


class        UpperCAmelCase_  (	nn.Module						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   Optional[Any]:
					super().__init__()
					__lowercase	:       int				=   dtype
					__lowercase	:       int				=   nn.Parameter(torch.randn(1							,		UpperCamelCase_   ) / embed_dim**0.5   )
					__lowercase	:       Dict				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_							,		dtype=self.dtype   )
					__lowercase	:       Optional[int]				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_							,		dtype=self.dtype   )
					__lowercase	:       Dict				=   nn.Linear(UpperCamelCase_							,		UpperCamelCase_							,		dtype=self.dtype   )
					__lowercase	:       Optional[Any]				=   num_heads
					__lowercase	:       Union[str, Any]				=   embed_dim // self.num_heads
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Optional[Any]:
					__lowercase	,__lowercase	,__lowercase	:       List[Any]				=   x.size()

					def shape(UpperCamelCase_   ):
								# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
								__lowercase	:       Union[str, Any]				=   x.view(UpperCamelCase_							,		-1							,		self.num_heads							,		self.dim_per_head   )
								# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
								__lowercase	:       Optional[int]				=   x.transpose(1							,		2   )
								# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
								__lowercase	:       List[Any]				=   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	:       Union[str, Any]				=   x.transpose(1							,		2   )
								return x

					__lowercase	:       Dict				=   x.mean(dim=1							,		keepdim=UpperCamelCase_   ) + self.positional_embedding.to(x.dtype   )
					__lowercase	:       Any				=   torch.cat([class_token, x]							,		dim=1   )  # (bs, length+1, width)

					# (bs*n_heads, class_token_length, dim_per_head)
					__lowercase	:       int				=   shape(self.q_proj(UpperCamelCase_   )   )
					# (bs*n_heads, length+class_token_length, dim_per_head)
					__lowercase	:       Any				=   shape(self.k_proj(UpperCamelCase_   )   )
					__lowercase	:       str				=   shape(self.v_proj(UpperCamelCase_   )   )

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

					# (bs*n_heads, dim_per_head, class_token_length)
					__lowercase	:       Tuple				=   torch.einsum('''bts,bcs->bct'''							,		UpperCamelCase_							,		UpperCamelCase_   )

					# (bs, length+1, width)
					__lowercase	:       Union[str, Any]				=   a.reshape(UpperCamelCase_							,		-1							,		1   ).transpose(1							,		2   )

					return a[:, 0, :]  # cls_token









"""simple docstring"""




import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
			print('#################### AVAILABLE DEVICES ####################')
			print(jax.devices())
			print('###########################################################')

			# setup for wandb sweep
			a_            =   Args()
			a_            =   wandb.init(project='bigbird-natural-questions', config=args.__dict__)
			a_            =   dict(logger.config)
			del wandb_args["batch_size"]
			a_            =   replace(args, **wandb_args)
			a_            =   args.base_dir + '-' + wandb.run.id
			a_            =   replace(args, base_dir=base_dir)
			print(args)

			a_            =   load_dataset('json', data_files=args.tr_data_path)['train']
			a_            =   load_dataset('json', data_files=args.val_data_path)['train']

			# drop extra batch for now
			a_            =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
			a_            =   tr_dataset.shuffle().select(indices)
			a_            =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
			a_            =   val_dataset.shuffle().select(indices)

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

			a_            =   {
			    'lr': args.lr,
			    'init_lr': args.init_lr,
			    'warmup_steps': args.warmup_steps,
			    'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
			    'weight_decay': args.weight_decay,
			}
			a_       ,	a_            =   build_tx(**tx_args)

			a_            =   Trainer(
			    args=args,
			    data_collator=data_collator,
			    model_save_fn=model.save_pretrained,
			    train_step_fn=train_step,
			    val_step_fn=val_step,
			    logger=logger,
			    scheduler_fn=lr,
			)

			a_            =   None
			a_            =   trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
			try:
						trainer.train(state, tr_dataset, val_dataset)
			except KeyboardInterrupt:
						print('Oooops; TRAINING STOPPED UNFORTUNATELY')

			print('SAVING WEIGHTS IN `final-weights`')
			a_            =   jax_utils.unreplicate(state.params)
			model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)



