
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def      _a     (   __UpperCamelCase       :  torch.Tensor	,__UpperCamelCase       :  int	,__UpperCamelCase       :  bool = False	,__UpperCamelCase       :  float = 1	,__UpperCamelCase       :  float = 1	,__UpperCamelCase       :  int = 1_0000	,):
			assert len(timesteps.shape    ) == 1, "Timesteps should be a 1d-array"

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

			lowerCAmelCase__ :    List[Any]				      =			torch.exp(__UpperCamelCase    )
			lowerCAmelCase__ :    Union[str, Any]				      =			timesteps[:, None].float() * emb[None, :]

			# scale embeddings
			lowerCAmelCase__ :    Tuple				      =			scale * emb

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

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

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



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

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



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Tuple    ):



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

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

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



def      _a     (   __UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  Optional[Any]    ):



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

			lowerCAmelCase__ :    List[Any]				      =			np.arange(embed_dim // 2	,dtype=np.floataa    )
			omega /= embed_dim / 2.0
			lowerCAmelCase__ :    Union[str, Any]				      =			1.0 / 1_0000**omega  # (D/2,)

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

			lowerCAmelCase__ :    List[str]				      =			np.sin(__UpperCamelCase    )  # (M, D/2)
			lowerCAmelCase__ :    Dict				      =			np.cos(__UpperCamelCase    )  # (M, D/2)

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





class 						lowercase       (  nn.Module					):
					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__()

								lowerCAmelCase__ :    List[Any]				      =			(height // patch_size) * (width // patch_size)
								lowerCAmelCase__ :    int				      =			flatten
								lowerCAmelCase__ :    Dict				      =			layer_norm

								lowerCAmelCase__ :    Tuple				      =			nn.Convad(
								    lowerCAmelCase__ ,	lowerCAmelCase__ ,	kernel_size=(patch_size, patch_size) ,	stride=lowerCAmelCase__ ,	bias=lowerCAmelCase__    )
								if layer_norm:
											lowerCAmelCase__ :    Dict				      =			nn.LayerNorm(lowerCAmelCase__ ,	elementwise_affine=lowerCAmelCase__ ,	eps=1E-6    )
								else:
											lowerCAmelCase__ :    Union[str, Any]				      =			None

								lowerCAmelCase__ :    Optional[int]				      =			get_ad_sincos_pos_embed(lowerCAmelCase__ ,	int(num_patches**0.5    )    )
								self.register_buffer('''pos_embed''' ,	torch.from_numpy(lowerCAmelCase__    ).float().unsqueeze(0    ) ,	persistent=lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







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





class 						lowercase       (  nn.Module					):
					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__()

								lowerCAmelCase__ :    Any				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )

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

								lowerCAmelCase__ :    Optional[int]				      =			get_activation(lowerCAmelCase__    )

								if out_dim is not None:
											lowerCAmelCase__ :    Union[str, Any]				      =			out_dim
								else:
											lowerCAmelCase__ :    int				      =			time_embed_dim
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )

								if post_act_fn is None:
											lowerCAmelCase__ :    int				      =			None
								else:
											lowerCAmelCase__ :    Any				      =			get_activation(lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								if condition is not None:
											lowerCAmelCase__ :    Dict				      =			sample + self.cond_proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Any				      =			self.linear_a(lowerCAmelCase__    )

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

								lowerCAmelCase__ :    str				      =			self.linear_a(lowerCAmelCase__    )

								if self.post_act is not None:
											lowerCAmelCase__ :    Optional[int]				      =			self.post_act(lowerCAmelCase__    )
								return sample





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    str				      =			num_channels
								lowerCAmelCase__ :    Optional[int]				      =			flip_sin_to_cos
								lowerCAmelCase__ :    int				      =			downscale_freq_shift
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			get_timestep_embedding(
								    lowerCAmelCase__ ,	self.num_channels ,	flip_sin_to_cos=self.flip_sin_to_cos ,	downscale_freq_shift=self.downscale_freq_shift ,	)
								return t_emb





class 						lowercase       (  nn.Module					):
					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__()
								lowerCAmelCase__ :    str				      =			nn.Parameter(torch.randn(lowerCAmelCase__    ) * scale ,	requires_grad=lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[Any]				      =			log
								lowerCAmelCase__ :    List[Any]				      =			flip_sin_to_cos

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

											lowerCAmelCase__ :    int				      =			self.W
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								if self.log:
											lowerCAmelCase__ :    List[Any]				      =			torch.log(lowerCAmelCase__    )

								lowerCAmelCase__ :    Union[str, Any]				      =			x[:, None] * self.weight[None, :] * 2 * np.pi

								if self.flip_sin_to_cos:
											lowerCAmelCase__ :    List[str]				      =			torch.cat([torch.cos(lowerCAmelCase__    ), torch.sin(lowerCAmelCase__    )] ,	dim=-1    )
								else:
											lowerCAmelCase__ :    Optional[int]				      =			torch.cat([torch.sin(lowerCAmelCase__    ), torch.cos(lowerCAmelCase__    )] ,	dim=-1    )
								return out





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	):
								"""simple docstring"""







								super().__init__()

								lowerCAmelCase__ :    Optional[Any]				      =			height
								lowerCAmelCase__ :    Tuple				      =			width
								lowerCAmelCase__ :    Dict				      =			num_embed
								lowerCAmelCase__ :    int				      =			embed_dim

								lowerCAmelCase__ :    Optional[Any]				      =			nn.Embedding(self.num_embed ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Embedding(self.height ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    List[Any]				      =			nn.Embedding(self.width ,	lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.emb(lowerCAmelCase__    )

								lowerCAmelCase__ :    List[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
								lowerCAmelCase__ :    List[str]				      =			height_emb.unsqueeze(2    )

								lowerCAmelCase__ :    Optional[int]				      =			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
								lowerCAmelCase__ :    Union[str, Any]				      =			width_emb.unsqueeze(1    )

								lowerCAmelCase__ :    Tuple				      =			height_emb + width_emb

								# 1 x H x W x D -> 1 x L xD
								lowerCAmelCase__ :    Optional[int]				      =			pos_emb.view(1 ,	self.height * self.width ,	-1    )

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

								return emb





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Any				      =			dropout_prob > 0
								lowerCAmelCase__ :    Tuple				      =			nn.Embedding(num_classes + use_cfg_embedding ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			num_classes
								lowerCAmelCase__ :    Optional[int]				      =			dropout_prob
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								if force_drop_ids is None:
											lowerCAmelCase__ :    Any				      =			torch.rand(labels.shape[0] ,	device=labels.device    ) < self.dropout_prob
								else:
											lowerCAmelCase__ :    Dict				      =			torch.tensor(force_drop_ids == 1    )
								lowerCAmelCase__ :    Any				      =			torch.where(lowerCAmelCase__ ,	self.num_classes ,	lowerCAmelCase__    )
								return labels
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.dropout_prob > 0
								if (self.training and use_dropout) or (force_drop_ids is not None):
											lowerCAmelCase__ :    Any				      =			self.token_drop(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Dict				      =			self.embedding_table(lowerCAmelCase__    )
								return embeddings





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ = 1024 ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 10 ,	):
								"""simple docstring"""







								super().__init__()

								lowerCAmelCase__ :    List[Any]				      =			num_image_text_embeds
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Linear(lowerCAmelCase__ ,	self.num_image_text_embeds * cross_attention_dim    )
								lowerCAmelCase__ :    Any				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			text_embeds.shape[0]

								# image
								lowerCAmelCase__ :    Dict				      =			self.image_embeds(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[Any]				      =			image_text_embeds.reshape(lowerCAmelCase__ ,	self.num_image_text_embeds ,	-1    )

								# text
								lowerCAmelCase__ :    List[Any]				      =			self.text_proj(lowerCAmelCase__    )

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





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 32 ,	):
								"""simple docstring"""







								super().__init__()

								lowerCAmelCase__ :    List[Any]				      =			num_image_text_embeds
								lowerCAmelCase__ :    Any				      =			nn.Linear(lowerCAmelCase__ ,	self.num_image_text_embeds * cross_attention_dim    )
								lowerCAmelCase__ :    str				      =			nn.LayerNorm(lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







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

								# image
								lowerCAmelCase__ :    Tuple				      =			self.image_embeds(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[int]				      =			image_embeds.reshape(lowerCAmelCase__ ,	self.num_image_text_embeds ,	-1    )
								lowerCAmelCase__ :    Optional[int]				      =			self.norm(lowerCAmelCase__    )
								return image_embeds





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=0.1    ):
								"""simple docstring"""







								super().__init__()

								lowerCAmelCase__ :    List[Any]				      =			Timesteps(num_channels=256 ,	flip_sin_to_cos=lowerCAmelCase__ ,	downscale_freq_shift=1    )
								lowerCAmelCase__ :    int				      =			TimestepEmbedding(in_channels=256 ,	time_embed_dim=lowerCAmelCase__    )
								lowerCAmelCase__ :    List[Any]				      =			LabelEmbedding(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			self.time_proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__    )    )  # (N, D)

								lowerCAmelCase__ :    List[Any]				      =			self.class_embedder(lowerCAmelCase__    )  # (N, D)

								lowerCAmelCase__ :    int				      =			timesteps_emb + class_labels  # (N, D)

								return conditioning





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = 64    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    int				      =			nn.LayerNorm(lowerCAmelCase__    )
								lowerCAmelCase__ :    Tuple				      =			AttentionPooling(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[Any]				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Any				      =			nn.LayerNorm(lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.norma(lowerCAmelCase__    )
								lowerCAmelCase__ :    int				      =			self.pool(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[Any]				      =			self.proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[int]				      =			self.norma(lowerCAmelCase__    )
								return hidden_states





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 1536    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Dict				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.LayerNorm(lowerCAmelCase__    )
								lowerCAmelCase__ :    str				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.text_proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[int]				      =			self.text_norm(lowerCAmelCase__    )

								# image
								lowerCAmelCase__ :    Any				      =			self.image_proj(lowerCAmelCase__    )

								return time_image_embeds + time_text_embeds





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 1536    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    str				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Tuple				      =			nn.LayerNorm(lowerCAmelCase__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			self.image_proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Dict				      =			self.image_norm(lowerCAmelCase__    )
								return time_image_embeds





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ = 768 ,	SCREAMING_SNAKE_CASE__ = 1536    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.LayerNorm(lowerCAmelCase__    )
								lowerCAmelCase__ :    int				      =			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"""







								lowerCAmelCase__ :    int				      =			self.image_proj(lowerCAmelCase__    )
								lowerCAmelCase__ :    Optional[int]				      =			self.image_norm(lowerCAmelCase__    )
								lowerCAmelCase__ :    List[Any]				      =			self.input_hint_block(lowerCAmelCase__    )
								return time_image_embeds, hint





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Dict				      =			dtype
								lowerCAmelCase__ :    Dict				      =			nn.Parameter(torch.randn(1 ,	lowerCAmelCase__    ) / embed_dim**0.5    )
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__ ,	dtype=self.dtype    )
								lowerCAmelCase__ :    Tuple				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__ ,	dtype=self.dtype    )
								lowerCAmelCase__ :    Union[str, Any]				      =			nn.Linear(lowerCAmelCase__ ,	lowerCAmelCase__ ,	dtype=self.dtype    )
								lowerCAmelCase__ :    str				      =			num_heads
								lowerCAmelCase__ :    Optional[Any]				      =			embed_dim // self.num_heads
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			x.size()

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

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

								# (bs*n_heads, class_token_length, dim_per_head)
								lowerCAmelCase__ :    int				      =			shape(self.q_proj(lowerCAmelCase__    )    )
								# (bs*n_heads, length+class_token_length, dim_per_head)
								lowerCAmelCase__ :    Optional[Any]				      =			shape(self.k_proj(lowerCAmelCase__    )    )
								lowerCAmelCase__ :    Optional[int]				      =			shape(self.v_proj(lowerCAmelCase__    )    )

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

								# (bs*n_heads, dim_per_head, class_token_length)
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.einsum('''bts,bcs->bct''' ,	lowerCAmelCase__ ,	lowerCAmelCase__    )

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

								return a[:, 0, :]  # cls_token





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							A__       :   Tuple   						=					"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Union[str, Any]=None	,__UpperCamelCase       :  Optional[int]=None    ):
			if attention_mask is None:
						lowerCAmelCase__ :    Any				      =			np.where(input_ids != config.pad_token_id	,1	,0    )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}





@require_flax
class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=13 ,	SCREAMING_SNAKE_CASE__=7 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=False ,	SCREAMING_SNAKE_CASE__=99 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=20 ,	SCREAMING_SNAKE_CASE__=2 ,	SCREAMING_SNAKE_CASE__=1 ,	SCREAMING_SNAKE_CASE__=0 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=16 ,	SCREAMING_SNAKE_CASE__=0.02 ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			parent
								lowerCAmelCase__ :    Optional[Any]				      =			batch_size
								lowerCAmelCase__ :    Any				      =			seq_length
								lowerCAmelCase__ :    str				      =			is_training
								lowerCAmelCase__ :    List[Any]				      =			use_labels
								lowerCAmelCase__ :    List[Any]				      =			vocab_size
								lowerCAmelCase__ :    List[str]				      =			hidden_size
								lowerCAmelCase__ :    str				      =			num_hidden_layers
								lowerCAmelCase__ :    List[Any]				      =			num_attention_heads
								lowerCAmelCase__ :    Optional[Any]				      =			intermediate_size
								lowerCAmelCase__ :    int				      =			hidden_act
								lowerCAmelCase__ :    Optional[int]				      =			hidden_dropout_prob
								lowerCAmelCase__ :    Any				      =			attention_probs_dropout_prob
								lowerCAmelCase__ :    str				      =			max_position_embeddings
								lowerCAmelCase__ :    List[str]				      =			eos_token_id
								lowerCAmelCase__ :    List[Any]				      =			pad_token_id
								lowerCAmelCase__ :    Tuple				      =			bos_token_id
								lowerCAmelCase__ :    str				      =			embed_dim
								lowerCAmelCase__ :    Optional[int]				      =			word_embed_proj_dim
								lowerCAmelCase__ :    List[Any]				      =			initializer_range
								lowerCAmelCase__ :    Tuple				      =			False
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,	self.vocab_size    ) ,	3 ,	self.vocab_size    )
								lowerCAmelCase__ :    Union[str, Any]				      =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,	dtype=np.intaa    )) ,	-1    )

								lowerCAmelCase__ :    Union[str, Any]				      =			OPTConfig(
								    vocab_size=self.vocab_size ,	hidden_size=self.hidden_size ,	num_hidden_layers=self.num_hidden_layers ,	num_attention_heads=self.num_attention_heads ,	ffn_dim=self.intermediate_size ,	dropout=self.hidden_dropout_prob ,	attention_dropout=self.attention_probs_dropout_prob ,	max_position_embeddings=self.max_position_embeddings ,	eos_token_id=self.eos_token_id ,	bos_token_id=self.bos_token_id ,	pad_token_id=self.pad_token_id ,	embed_dim=self.embed_dim ,	is_encoder_decoder=SCREAMING_SNAKE_CASE__ ,	word_embed_proj_dim=self.word_embed_proj_dim ,	initializer_range=self.initializer_range ,	use_cache=SCREAMING_SNAKE_CASE__ ,	)
								lowerCAmelCase__ :    Optional[int]				      =			prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								return config, inputs_dict
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.prepare_config_and_inputs()
								return config, inputs_dict
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			20
								lowerCAmelCase__ :    Optional[int]				      =			model_class_name(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			inputs_dict['''input_ids''']
								lowerCAmelCase__ :    Optional[Any]				      =			inputs_dict['''attention_mask''']

								lowerCAmelCase__ :    Tuple				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Tuple				      =			jnp.ones((input_ids.shape[0], max_length) ,	dtype='''i4'''    )

								lowerCAmelCase__ :    Any				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)
								lowerCAmelCase__ :    str				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    int				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    List[str]				      =			model(
								    input_ids[:, -1:] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=outputs_cache.past_key_values ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    List[str]				      =			model(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
								self.parent.assertTrue(diff < 1E-3 ,	msg=f'''Max diff is {diff}'''    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    int				      =			20
								lowerCAmelCase__ :    Optional[Any]				      =			model_class_name(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			(
								    inputs_dict['''input_ids'''],
								    inputs_dict['''attention_mask'''],
								)

								lowerCAmelCase__ :    Optional[Any]				      =			jnp.concatenate(
								    [
								        attention_mask,
								        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
								    ] ,	axis=-1 ,	)

								lowerCAmelCase__ :    Any				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Union[str, Any]				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1) ,	)

								lowerCAmelCase__ :    Tuple				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	past_key_values=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)
								lowerCAmelCase__ :    int				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    List[Any]				      =			model(
								    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=SCREAMING_SNAKE_CASE__ ,	position_ids=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	attention_mask=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    int				      =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])    )    )
								self.parent.assertTrue(diff < 1E-3 ,	msg=f'''Max diff is {diff}'''    )





@require_flax
class 						lowercase       (  __UpperCamelCase	,		unittest.TestCase	,		__UpperCamelCase					):
					__a					=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					__a					=    () if is_flax_available() else ()
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Union[str, Any]				      =			FlaxOPTModelTester(self    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[Any]				      =			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
											lowerCAmelCase__ :    Tuple				      =			model_class_name.from_pretrained('''facebook/opt-125m'''    )
											lowerCAmelCase__ :    Dict				      =			np.ones((1, 1)    ) * model.config.eos_token_id
											lowerCAmelCase__ :    List[str]				      =			model(SCREAMING_SNAKE_CASE__    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )





@require_sentencepiece
@require_flax
class 						lowercase       (  unittest.TestCase					):
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			FlaxOPTModel.from_pretrained('''facebook/opt-350m'''    )
								lowerCAmelCase__ :    List[Any]				      =			jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
								lowerCAmelCase__ :    Union[str, Any]				      =			model(input_ids=SCREAMING_SNAKE_CASE__    ).last_hidden_state
								lowerCAmelCase__ :    Any				      =			(1, 11, 512)
								self.assertEqual(output.shape ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			jnp.array(
								    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
								self.assertTrue(jnp.allclose(output[:, :3, :3] ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )





@require_flax
@slow
class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







								super().setUp()
								lowerCAmelCase__ :    Union[str, Any]				      =			'''facebook/opt-350m'''
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			FlaxOPTForCausalLM.from_pretrained(self.path_model    )
								lowerCAmelCase__ :    Any				      =			GPTaTokenizer.from_pretrained(self.path_model    )

								lowerCAmelCase__ :    Tuple				      =			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of''',
								    '''Paris is the capital of France and''',
								    '''Computers and mobile phones have taken''',
								]
								# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
								lowerCAmelCase__ :    Dict				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__ ,	add_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Optional[Any]				      =			model(inputs.input_ids ,	attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
								lowerCAmelCase__ :    Optional[Any]				      =			jnp.array(
								    [
								        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
								        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
								        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
								        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
								    ]    )
								self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )

								lowerCAmelCase__ :    List[Any]				      =			jax.jit(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			model(inputs.input_ids ,	attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
								self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	atol=4E-2    )    )





@require_flax
@slow
class 						lowercase       (  unittest.TestCase					):
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return [
								    "Today is a beautiful day and I want",
								    "In the city of",
								    "Paris is the capital of France and",
								    "Computers and mobile phones have taken",
								]
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Dict				      =			'''facebook/opt-125m'''

								lowerCAmelCase__ :    List[Any]				      =			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of New York, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								lowerCAmelCase__ :    Any				      =			[]

								lowerCAmelCase__ :    Optional[int]				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    List[Any]				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

								for prompt in self.prompts:
											lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax'''    ).input_ids

											lowerCAmelCase__ :    Dict				      =			model.generate(SCREAMING_SNAKE_CASE__ ,	max_length=10    )
											lowerCAmelCase__ :    Tuple				      =			generated_ids[0]

											lowerCAmelCase__ :    List[str]				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											predicted_outputs += generated_string

								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			'''facebook/opt-350m'''

								lowerCAmelCase__ :    Dict				      =			[
								    '''Today is a beautiful day and I want to''',
								    '''In the city of San Francisco, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								lowerCAmelCase__ :    Any				      =			[]
								lowerCAmelCase__ :    List[str]				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

								for prompt in self.prompts:
											lowerCAmelCase__ :    str				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax'''    ).input_ids

											lowerCAmelCase__ :    Optional[Any]				      =			model.generate(SCREAMING_SNAKE_CASE__ ,	max_length=10    )
											lowerCAmelCase__ :    List[Any]				      =			generated_ids[0]

											lowerCAmelCase__ :    Optional[Any]				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
											predicted_outputs += generated_string

								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			'''facebook/opt-125m'''
								lowerCAmelCase__ :    Dict				      =			[
								    '''Today is a beautiful day and I want to thank''',
								    '''In the city of Rome Canaver Canaver Canaver Canaver''',
								]
								lowerCAmelCase__ :    Tuple				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Optional[Any]				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			tokenizer(
								    [
								        '''Today is a beautiful day and I want to''',
								        '''In the city of''',
								    ] ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__ ,	)

								lowerCAmelCase__ :    Union[str, Any]				      =			jax.jit(model.generate    )

								lowerCAmelCase__ :    Optional[Any]				      =			jit_generate(inputs['''input_ids'''] ,	attention_mask=inputs['''attention_mask''']    ).sequences

								lowerCAmelCase__ :    str				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )

								self.assertIsNotNone(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			'''facebook/opt-350m'''

								lowerCAmelCase__ :    Tuple				      =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    int				      =			FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Union[str, Any]				      =			'''left'''

								# use different length sentences to test batching
								lowerCAmelCase__ :    Optional[int]				      =			[
								    '''Hello, my dog is a little''',
								    '''Today, I''',
								]

								lowerCAmelCase__ :    Optional[int]				      =			tokenizer(SCREAMING_SNAKE_CASE__ ,	return_tensors='''jax''' ,	padding=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Any				      =			inputs['''input_ids''']

								lowerCAmelCase__ :    Optional[Any]				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__ ,	attention_mask=inputs['''attention_mask'''] ,	trace=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    List[Any]				      =			tokenizer(sentences[0] ,	return_tensors='''jax'''    ).input_ids
								lowerCAmelCase__ :    List[Any]				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Dict				      =			inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
								lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer(sentences[1] ,	return_tensors='''jax'''    ).input_ids
								lowerCAmelCase__ :    Dict				      =			model.generate(input_ids=SCREAMING_SNAKE_CASE__ ,	max_length=model.config.max_length - num_paddings    )

								lowerCAmelCase__ :    Dict				      =			tokenizer.batch_decode(outputs[0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Dict				      =			tokenizer.decode(output_non_padded[0][0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Tuple				      =			tokenizer.decode(output_padded[0][0] ,	skip_special_tokens=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    int				      =			[
								    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
								    '''Today, I was in the middle of a conversation with a friend about the''',
								]
								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								self.assertListEqual(SCREAMING_SNAKE_CASE__ ,	[non_padded_sentence, padded_sentence]    )



