




"""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	:       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 json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
			from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

			from .test_feature_extraction_speechta import floats_list


a_            =   get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Dict:
					__lowercase	:       Union[str, Any]				=   tempfile.mkdtemp()

					__lowercase	:       List[str]				=   SpeechTaTokenizer(UpperCamelCase_   )
					tokenizer.save_pretrained(self.tmpdirname   )

					__lowercase	:       Dict				=   {
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_60_00,
					    '''do_normalize''': False,
					    '''num_mel_bins''': 80,
					    '''hop_length''': 16,
					    '''win_length''': 64,
					    '''win_function''': '''hann_window''',
					    '''fmin''': 80,
					    '''fmax''': 76_00,
					    '''mel_floor''': 1E-10,
					    '''reduction_factor''': 2,
					    '''return_attention_mask''': True,
					}

					__lowercase	:       List[Any]				=   os.path.join(self.tmpdirname							,		UpperCamelCase_   )
					with open(self.feature_extraction_file							,		'''w'''							,		encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(UpperCamelCase_   ) + '''\n'''   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[Any]:
					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,		**UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       List[str]				=   self.get_tokenizer()
					__lowercase	:       int				=   self.get_feature_extractor()

					__lowercase	:       Any				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					processor.save_pretrained(self.tmpdirname   )
					__lowercase	:       List[Any]				=   SpeechTaProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=self.get_tokenizer()							,		feature_extractor=self.get_feature_extractor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       str				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Dict				=   self.get_feature_extractor(do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					__lowercase	:       Tuple				=   SpeechTaProcessor.from_pretrained(
					    self.tmpdirname							,		bos_token='''(BOS)'''							,		eos_token='''(EOS)'''							,		do_normalize=UpperCamelCase_							,		padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		UpperCamelCase_   )

					self.assertEqual(processor.feature_extractor.to_json_string()							,		feature_extractor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.feature_extractor							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Optional[Any]				=   self.get_feature_extractor()
					__lowercase	:       Optional[Any]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       int				=   floats_list((3, 10_00)   )

					__lowercase	:       str				=   feature_extractor(audio=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       Dict				=   processor(audio=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       str				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[int]				=   floats_list((3, 10_00)   )

					__lowercase	:       List[str]				=   feature_extractor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )
					__lowercase	:       List[str]				=   processor(audio_target=UpperCamelCase_							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       List[Any]				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       Union[str, Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       List[str]				=   '''This is a test string'''

					__lowercase	:       Any				=   processor(text=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       int				=   self.get_feature_extractor()
					__lowercase	:       List[str]				=   self.get_tokenizer()

					__lowercase	:       List[Any]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   '''This is a test string'''

					__lowercase	:       Tuple				=   processor(text_target=UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   tokenizer(UpperCamelCase_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   self.get_feature_extractor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       int				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__lowercase	:       str				=   processor.batch_decode(UpperCamelCase_   )
					__lowercase	:       int				=   tokenizer.batch_decode(UpperCamelCase_   )

					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.get_feature_extractor()
					__lowercase	:       int				=   self.get_tokenizer()

					__lowercase	:       Optional[int]				=   SpeechTaProcessor(tokenizer=UpperCamelCase_							,		feature_extractor=UpperCamelCase_   )

					self.assertListEqual(
					    processor.model_input_names							,		feature_extractor.model_input_names							,		msg='''`processor` and `feature_extractor` model input names do not match'''							,		)



