


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def   a						(				SCREAMING_SNAKE_CASE_   :		torch.Tensor				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		bool = False				,      SCREAMING_SNAKE_CASE_   :		float = 1				,      SCREAMING_SNAKE_CASE_   :		float = 1				,      SCREAMING_SNAKE_CASE_   :		int = 1_0_0_0_0				,      ):






       """simple docstring"""





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

       UpperCamelCase     :						List[Any] 						=		embedding_dim // 2
       UpperCamelCase     :						Optional[int] 						=		-math.log(SCREAMING_SNAKE_CASE_							) * torch.arange(
           start=0				,      end=SCREAMING_SNAKE_CASE_				,      dtype=torch.floataa				,      device=timesteps.device							)
       UpperCamelCase     :						str 						=		exponent / (half_dim - downscale_freq_shift)

       UpperCamelCase     :						Tuple 						=		torch.exp(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Tuple 						=		timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       UpperCamelCase     :						List[Any] 						=		scale * emb

       # concat sine and cosine embeddings
       UpperCamelCase     :						Union[str, Any] 						=		torch.cat([torch.sin(SCREAMING_SNAKE_CASE_							), torch.cos(SCREAMING_SNAKE_CASE_							)]				,      dim=-1							)

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

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


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=False				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=0							):






       """simple docstring"""





       UpperCamelCase     :						Optional[Any] 						=		np.arange(SCREAMING_SNAKE_CASE_				,      dtype=np.floataa							)
       UpperCamelCase     :						Optional[int] 						=		np.arange(SCREAMING_SNAKE_CASE_				,      dtype=np.floataa							)
       UpperCamelCase     :						List[Any] 						=		np.meshgrid(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)  # here w goes first
       UpperCamelCase     :						List[Any] 						=		np.stack(SCREAMING_SNAKE_CASE_				,      axis=0							)

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


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		List[str]							):






       """simple docstring"""





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

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

       UpperCamelCase     :						Union[str, Any] 						=		np.concatenate([emb_h, emb_w]				,      axis=1							)  # (H*W, D)
       return emb


def   a						(				SCREAMING_SNAKE_CASE_   :		List[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





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

       UpperCamelCase     :						int 						=		np.arange(embed_dim // 2				,      dtype=np.floataa							)
       omega /= embed_dim / 2.0
       UpperCamelCase     :						int 						=		1.0 / 1_0_0_0_0**omega  # (D/2,)

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

       UpperCamelCase     :						List[str] 						=		np.sin(SCREAMING_SNAKE_CASE_							)  # (M, D/2)
       UpperCamelCase     :						Tuple 						=		np.cos(SCREAMING_SNAKE_CASE_							)  # (M, D/2)

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



class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						Optional[Any] 						=		(height // patch_size) * (width // patch_size)
              UpperCamelCase     :						int 						=		flatten
              UpperCamelCase     :						Union[str, Any] 						=		layer_norm

              UpperCamelCase     :						str 						=		nn.Convad(
                  __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       kernel_size=(patch_size, patch_size)			,       stride=__SCREAMING_SNAKE_CASE			,       bias=__SCREAMING_SNAKE_CASE    )
              if layer_norm:
                     UpperCamelCase     :						Any 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE			,       elementwise_affine=__SCREAMING_SNAKE_CASE			,       eps=1e-6    )
              else:
                     UpperCamelCase     :						Dict 						=		None

              UpperCamelCase     :						List[str] 						=		get_ad_sincos_pos_embed(__SCREAMING_SNAKE_CASE			,       int(num_patches**0.5    )    )
              self.register_buffer('''pos_embed'''			,       torch.from_numpy(__SCREAMING_SNAKE_CASE    ).float().unsqueeze(0    )			,       persistent=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		self.proj(__SCREAMING_SNAKE_CASE    )
              if self.flatten:
                     UpperCamelCase     :						str 						=		latent.flatten(2    ).transpose(1			,       2    )  # BCHW -> BNC
              if self.layer_norm:
                     UpperCamelCase     :						List[Any] 						=		self.norm(__SCREAMING_SNAKE_CASE    )
              return latent + self.pos_embed




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						int 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              if cond_proj_dim is not None:
                     UpperCamelCase     :						Optional[Any] 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       bias=__SCREAMING_SNAKE_CASE    )
              else:
                     UpperCamelCase     :						List[Any] 						=		None

              UpperCamelCase     :						List[Any] 						=		get_activation(__SCREAMING_SNAKE_CASE    )

              if out_dim is not None:
                     UpperCamelCase     :						Tuple 						=		out_dim
              else:
                     UpperCamelCase     :						List[Any] 						=		time_embed_dim
              UpperCamelCase     :						Optional[int] 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              if post_act_fn is None:
                     UpperCamelCase     :						int 						=		None
              else:
                     UpperCamelCase     :						Optional[int] 						=		get_activation(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              if condition is not None:
                     UpperCamelCase     :						int 						=		sample + self.cond_proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		self.linear_a(__SCREAMING_SNAKE_CASE    )

              if self.act is not None:
                     UpperCamelCase     :						List[Any] 						=		self.act(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						int 						=		self.linear_a(__SCREAMING_SNAKE_CASE    )

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




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Optional[Any] 						=		num_channels
              UpperCamelCase     :						int 						=		flip_sin_to_cos
              UpperCamelCase     :						Optional[int] 						=		downscale_freq_shift






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		get_timestep_embedding(
                  __SCREAMING_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  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Any 						=		nn.Parameter(torch.randn(__SCREAMING_SNAKE_CASE    ) * scale			,       requires_grad=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[Any] 						=		log
              UpperCamelCase     :						int 						=		flip_sin_to_cos

              if set_W_to_weight:
                     # to delete later
                     UpperCamelCase     :						Optional[Any] 						=		nn.Parameter(torch.randn(__SCREAMING_SNAKE_CASE    ) * scale			,       requires_grad=__SCREAMING_SNAKE_CASE    )

                     UpperCamelCase     :						List[str] 						=		self.W






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if self.log:
                     UpperCamelCase     :						Optional[int] 						=		torch.log(__SCREAMING_SNAKE_CASE    )

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

              if self.flip_sin_to_cos:
                     UpperCamelCase     :						Any 						=		torch.cat([torch.cos(__SCREAMING_SNAKE_CASE    ), torch.sin(__SCREAMING_SNAKE_CASE    )]			,       dim=-1    )
              else:
                     UpperCamelCase     :						List[str] 						=		torch.cat([torch.sin(__SCREAMING_SNAKE_CASE    ), torch.cos(__SCREAMING_SNAKE_CASE    )]			,       dim=-1    )
              return out




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						List[Any] 						=		height
              UpperCamelCase     :						List[str] 						=		width
              UpperCamelCase     :						Any 						=		num_embed
              UpperCamelCase     :						Optional[Any] 						=		embed_dim

              UpperCamelCase     :						Any 						=		nn.Embedding(self.num_embed			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		nn.Embedding(self.height			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		nn.Embedding(self.width			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.emb(__SCREAMING_SNAKE_CASE    )

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

              UpperCamelCase     :						Tuple 						=		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
              UpperCamelCase     :						List[str] 						=		width_emb.unsqueeze(1    )

              UpperCamelCase     :						Dict 						=		height_emb + width_emb

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

              UpperCamelCase     :						List[Any] 						=		emb + pos_emb[:, : emb.shape[1], :]

              return emb




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Union[str, Any] 						=		dropout_prob > 0
              UpperCamelCase     :						Union[str, Any] 						=		nn.Embedding(num_classes + use_cfg_embedding			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		num_classes
              UpperCamelCase     :						Tuple 						=		dropout_prob






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              if force_drop_ids is None:
                     UpperCamelCase     :						Optional[int] 						=		torch.rand(labels.shape[0]			,       device=labels.device    ) < self.dropout_prob
              else:
                     UpperCamelCase     :						str 						=		torch.tensor(force_drop_ids == 1    )
              UpperCamelCase     :						str 						=		torch.where(__SCREAMING_SNAKE_CASE			,       self.num_classes			,       __SCREAMING_SNAKE_CASE    )
              return labels






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.dropout_prob > 0
              if (self.training and use_dropout) or (force_drop_ids is not None):
                     UpperCamelCase     :						List[str] 						=		self.token_drop(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		self.embedding_table(__SCREAMING_SNAKE_CASE    )
              return embeddings




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 1_024			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 10			,       ):




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						int 						=		num_image_text_embeds
              UpperCamelCase     :						str 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       self.num_image_text_embeds * cross_attention_dim    )
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		text_embeds.shape[0]

              # image
              UpperCamelCase     :						List[str] 						=		self.image_embeds(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		image_text_embeds.reshape(__SCREAMING_SNAKE_CASE			,       self.num_image_text_embeds			,       -1    )

              # text
              UpperCamelCase     :						int 						=		self.text_proj(__SCREAMING_SNAKE_CASE    )

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




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						List[str] 						=		num_image_text_embeds
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       self.num_image_text_embeds * cross_attention_dim    )
              UpperCamelCase     :						int 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		image_embeds.shape[0]

              # image
              UpperCamelCase     :						List[Any] 						=		self.image_embeds(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		image_embeds.reshape(__SCREAMING_SNAKE_CASE			,       self.num_image_text_embeds			,       -1    )
              UpperCamelCase     :						int 						=		self.norm(__SCREAMING_SNAKE_CASE    )
              return image_embeds




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()

              UpperCamelCase     :						List[Any] 						=		Timesteps(num_channels=256			,       flip_sin_to_cos=__SCREAMING_SNAKE_CASE			,       downscale_freq_shift=1    )
              UpperCamelCase     :						str 						=		TimestepEmbedding(in_channels=256			,       time_embed_dim=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		LabelEmbedding(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self.time_proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE    )    )  # (N, D)

              UpperCamelCase     :						Optional[Any] 						=		self.class_embedder(__SCREAMING_SNAKE_CASE    )  # (N, D)

              UpperCamelCase     :						str 						=		timesteps_emb + class_labels  # (N, D)

              return conditioning




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Optional[int] 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		AttentionPooling(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		self.norma(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		self.pool(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[Any] 						=		self.proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		self.norma(__SCREAMING_SNAKE_CASE    )
              return hidden_states




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 1_536    ):




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		self.text_proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		self.text_norm(__SCREAMING_SNAKE_CASE    )

              # image
              UpperCamelCase     :						Dict 						=		self.image_proj(__SCREAMING_SNAKE_CASE    )

              return time_image_embeds + time_text_embeds




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 1_536    ):




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						List[str] 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.image_proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		self.image_norm(__SCREAMING_SNAKE_CASE    )
              return time_image_embeds




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 768			,       __SCREAMING_SNAKE_CASE = 1_536    ):




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Optional[int] 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		nn.LayerNorm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.image_proj(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		self.image_norm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		self.input_hint_block(__SCREAMING_SNAKE_CASE    )
              return time_image_embeds, hint




class  UpperCAmelCase_						(				nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__()
              UpperCamelCase     :						Optional[int] 						=		dtype
              UpperCamelCase     :						List[Any] 						=		nn.Parameter(torch.randn(1			,       __SCREAMING_SNAKE_CASE    ) / embed_dim**0.5    )
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       dtype=self.dtype    )
              UpperCamelCase     :						Optional[int] 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       dtype=self.dtype    )
              UpperCamelCase     :						Dict 						=		nn.Linear(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       dtype=self.dtype    )
              UpperCamelCase     :						List[Any] 						=		num_heads
              UpperCamelCase     :						Any 						=		embed_dim // self.num_heads






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						str 						=		x.size()

              def shape(__SCREAMING_SNAKE_CASE    ):
                     # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                     UpperCamelCase     :						Dict 						=		x.view(__SCREAMING_SNAKE_CASE			,       -1			,       self.num_heads			,       self.dim_per_head    )
                     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
                     UpperCamelCase     :						Optional[Any] 						=		x.transpose(1			,       2    )
                     # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                     UpperCamelCase     :						Dict 						=		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)
                     UpperCamelCase     :						Dict 						=		x.transpose(1			,       2    )
                     return x

              UpperCamelCase     :						Optional[int] 						=		x.mean(dim=1			,       keepdim=__SCREAMING_SNAKE_CASE    ) + self.positional_embedding.to(x.dtype    )
              UpperCamelCase     :						List[Any] 						=		torch.cat([class_token, x]			,       dim=1    )  # (bs, length+1, width)

              # (bs*n_heads, class_token_length, dim_per_head)
              UpperCamelCase     :						Union[str, Any] 						=		shape(self.q_proj(__SCREAMING_SNAKE_CASE    )    )
              # (bs*n_heads, length+class_token_length, dim_per_head)
              UpperCamelCase     :						Tuple 						=		shape(self.k_proj(__SCREAMING_SNAKE_CASE    )    )
              UpperCamelCase     :						List[Any] 						=		shape(self.v_proj(__SCREAMING_SNAKE_CASE    )    )

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

              # (bs*n_heads, dim_per_head, class_token_length)
              UpperCamelCase     :						Optional[int] 						=		torch.einsum('''bts,bcs->bct'''			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # (bs, length+1, width)
              UpperCamelCase     :						str 						=		a.reshape(__SCREAMING_SNAKE_CASE			,       -1			,       1    ).transpose(1			,       2    )

              return a[:, 0, :]  # cls_token





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
  pass

if is_speech_available():
  from transformers import EncodecFeatureExtractor, MusicgenProcessor


__UpperCAmelCase			:			Union[str, Any]			      =					random.Random()


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=1.0				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=None				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=None							):






       """simple docstring"""





       if rng is None:
              UpperCamelCase     :						str 						=		global_rng

       UpperCamelCase     :						Union[str, Any] 						=		[]
       for batch_idx in range(shape[0]							):
              values.append([]							)
              for _ in range(shape[1]							):
                     values[-1].append(rng.random() * scale							)

       return values



@require_torch
@require_sentencepiece
class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		'''facebook/musicgen-small'''
              UpperCamelCase     :						str 						=		tempfile.mkdtemp()






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return TaTokenizer.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              shutil.rmtree(self.tmpdirname    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()
              UpperCamelCase     :						Any 						=		self.get_feature_extractor()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              processor.save_pretrained(self.tmpdirname    )
              UpperCamelCase     :						int 						=		MusicgenProcessor.from_pretrained(self.tmpdirname    )

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

              self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor.to_json_string()    )
              self.assertIsInstance(processor.feature_extractor			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
              processor.save_pretrained(self.tmpdirname    )

              UpperCamelCase     :						Any 						=		self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)'''    )
              UpperCamelCase     :						Union[str, Any] 						=		self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

              UpperCamelCase     :						Union[str, Any] 						=		MusicgenProcessor.from_pretrained(
                  self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

              self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor_add_kwargs.to_json_string()    )
              self.assertIsInstance(processor.feature_extractor			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						Tuple 						=		self.get_tokenizer()

              UpperCamelCase     :						Any 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		floats_list((3, 1_000)    )

              UpperCamelCase     :						Tuple 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       return_tensors='''np'''    )
              UpperCamelCase     :						Optional[Any] 						=		processor(__SCREAMING_SNAKE_CASE			,       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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						List[str] 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		'''This is a test string'''

              UpperCamelCase     :						Dict 						=		processor(text=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		tokenizer(__SCREAMING_SNAKE_CASE    )

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]			,       encoded_processor[key]    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.get_feature_extractor()
              UpperCamelCase     :						int 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						int 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              UpperCamelCase     :						int 						=		processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    )

              self.assertListEqual(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		self.get_feature_extractor()
              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.get_feature_extractor(padding_side='''left'''    )
              UpperCamelCase     :						Optional[Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						List[Any] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[str] 						=		[floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
              UpperCamelCase     :						Tuple 						=		processor(__SCREAMING_SNAKE_CASE    ).padding_mask

              UpperCamelCase     :						List[str] 						=		np.asarray(floats_list((3, 20)    )    )[:, None, :]
              UpperCamelCase     :						Optional[int] 						=		processor.batch_decode(__SCREAMING_SNAKE_CASE			,       padding_mask=__SCREAMING_SNAKE_CASE    )

              self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              for audio in decoded_audios:
                     self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       np.ndarray    )

              self.assertTrue(decoded_audios[0].shape == (1, 10)    )
              self.assertTrue(decoded_audios[1].shape == (1, 15)    )
              self.assertTrue(decoded_audios[2].shape == (1, 20)    )

