

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation

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

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

				_UpperCAmelCase				:						Any       						=			torch.exp(lowerCAmelCase       )
				_UpperCAmelCase				:						Dict       						=			timesteps[:, None].float() * emb[None, :]

				# scale embeddings
				_UpperCAmelCase				:						int       						=			scale * emb

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

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

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

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Tuple     ,					lowerCAmelCase:     List[str]=False     ,					lowerCAmelCase:     str=0       )      ->				Tuple:
				_UpperCAmelCase				:						Dict       						=			np.arange(lowerCAmelCase     ,					dtype=np.floataa       )
				_UpperCAmelCase				:						int       						=			np.arange(lowerCAmelCase     ,					dtype=np.floataa       )
				_UpperCAmelCase				:						int       						=			np.meshgrid(lowerCAmelCase     ,					lowerCAmelCase       )  # here w goes first
				_UpperCAmelCase				:						Union[str, Any]       						=			np.stack(lowerCAmelCase     ,					axis=0       )

				_UpperCAmelCase				:						int       						=			grid.reshape([2, 1, grid_size, grid_size]       )
				_UpperCAmelCase				:						Tuple       						=			get_ad_sincos_pos_embed_from_grid(lowerCAmelCase     ,					lowerCAmelCase       )
				if cls_token and extra_tokens > 0:
								_UpperCAmelCase				:						List[str]       						=			np.concatenate([np.zeros([extra_tokens, embed_dim]       ), pos_embed]     ,					axis=0       )
				return pos_embed

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Any     ,					lowerCAmelCase:     Tuple       )      ->				int:
				if embed_dim % 2 != 0:
								raise ValueError("embed_dim must be divisible by 2"       )

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

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

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Union[str, Any]     ,					lowerCAmelCase:     Dict       )      ->				str:
				if embed_dim % 2 != 0:
								raise ValueError("embed_dim must be divisible by 2"       )

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

				_UpperCAmelCase				:						Dict       						=			pos.reshape(-1       )  # (M,)
				_UpperCAmelCase				:						str       						=			np.einsum("m,d->md"     ,					lowerCAmelCase     ,					lowerCAmelCase       )  # (M, D/2), outer product

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

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

class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()

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

										_UpperCAmelCase				:						List[str]       						=			nn.Convad(
										    A_  ,    A_  ,    kernel_size=(patch_size, patch_size)  ,    stride=A_  ,    bias=A_		)
										if layer_norm:
														_UpperCAmelCase				:						Optional[int]       						=			nn.LayerNorm(A_  ,    elementwise_affine=A_  ,    eps=1e-6		)
										else:
														_UpperCAmelCase				:						Union[str, Any]       						=			None

										_UpperCAmelCase				:						Tuple       						=			get_ad_sincos_pos_embed(A_  ,    int(num_patches**0.5		)		)
										self.register_buffer("pos_embed"  ,    torch.from_numpy(A_		).float().unsqueeze(0		)  ,    persistent=A_		)





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


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







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						Tuple       						=			nn.Linear(A_  ,    A_		)

										if cond_proj_dim is not None:
														_UpperCAmelCase				:						int       						=			nn.Linear(A_  ,    A_  ,    bias=A_		)
										else:
														_UpperCAmelCase				:						str       						=			None

										_UpperCAmelCase				:						List[Any]       						=			get_activation(A_		)

										if out_dim is not None:
														_UpperCAmelCase				:						Optional[Any]       						=			out_dim
										else:
														_UpperCAmelCase				:						Dict       						=			time_embed_dim
										_UpperCAmelCase				:						Dict       						=			nn.Linear(A_  ,    A_		)

										if post_act_fn is None:
														_UpperCAmelCase				:						int       						=			None
										else:
														_UpperCAmelCase				:						Optional[Any]       						=			get_activation(A_		)





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







										'''simple docstring'''


										if condition is not None:
														_UpperCAmelCase				:						Any       						=			sample + self.cond_proj(A_		)
										_UpperCAmelCase				:						Tuple       						=			self.linear_a(A_		)

										if self.act is not None:
														_UpperCAmelCase				:						List[str]       						=			self.act(A_		)

										_UpperCAmelCase				:						Dict       						=			self.linear_a(A_		)

										if self.post_act is not None:
														_UpperCAmelCase				:						Any       						=			self.post_act(A_		)
										return sample







class  a     (		nn.Module ):


						def __init__(				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


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





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			get_timestep_embedding(
										    A_  ,    self.num_channels  ,    flip_sin_to_cos=self.flip_sin_to_cos  ,    downscale_freq_shift=self.downscale_freq_shift  ,    )
										return t_emb







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Any       						=			nn.Parameter(torch.randn(A_		) * scale  ,    requires_grad=A_		)
										_UpperCAmelCase				:						List[Any]       						=			log
										_UpperCAmelCase				:						List[Any]       						=			flip_sin_to_cos

										if set_W_to_weight:
														# to delete later
														_UpperCAmelCase				:						Union[str, Any]       						=			nn.Parameter(torch.randn(A_		) * scale  ,    requires_grad=A_		)

														_UpperCAmelCase				:						Any       						=			self.W





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										if self.log:
														_UpperCAmelCase				:						str       						=			torch.log(A_		)

										_UpperCAmelCase				:						List[Any]       						=			x[:, None] * self.weight[None, :] * 2 * np.pi

										if self.flip_sin_to_cos:
														_UpperCAmelCase				:						Optional[Any]       						=			torch.cat([torch.cos(A_		), torch.sin(A_		)]  ,    dim=-1		)
										else:
														_UpperCAmelCase				:						str       						=			torch.cat([torch.sin(A_		), torch.cos(A_		)]  ,    dim=-1		)
										return out







class  a     (		nn.Module ):


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    ):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						Tuple       						=			height
										_UpperCAmelCase				:						str       						=			width
										_UpperCAmelCase				:						Any       						=			num_embed
										_UpperCAmelCase				:						List[Any]       						=			embed_dim

										_UpperCAmelCase				:						str       						=			nn.Embedding(self.num_embed  ,    A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.Embedding(self.height  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			nn.Embedding(self.width  ,    A_		)





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			self.emb(A_		)

										_UpperCAmelCase				:						int       						=			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				:						int       						=			height_emb.unsqueeze(2		)

										_UpperCAmelCase				:						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
										_UpperCAmelCase				:						str       						=			width_emb.unsqueeze(1		)

										_UpperCAmelCase				:						List[str]       						=			height_emb + width_emb

										# 1 x H x W x D -> 1 x L xD
										_UpperCAmelCase				:						Union[str, Any]       						=			pos_emb.view(1  ,    self.height * self.width  ,    -1		)

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

										return emb







class  a     (		nn.Module ):


						def __init__(				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


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


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







										'''simple docstring'''


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





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







										'''simple docstring'''


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







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						Optional[Any]       						=			num_image_text_embeds
										_UpperCAmelCase				:						List[Any]       						=			nn.Linear(A_  ,    self.num_image_text_embeds * cross_attention_dim		)
										_UpperCAmelCase				:						str       						=			nn.Linear(A_  ,    A_		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			text_embeds.shape[0]

										# image
										_UpperCAmelCase				:						Tuple       						=			self.image_embeds(A_		)
										_UpperCAmelCase				:						Any       						=			image_text_embeds.reshape(A_  ,    self.num_image_text_embeds  ,    -1		)

										# text
										_UpperCAmelCase				:						Optional[Any]       						=			self.text_proj(A_		)

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







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						str       						=			num_image_text_embeds
										_UpperCAmelCase				:						List[Any]       						=			nn.Linear(A_  ,    self.num_image_text_embeds * cross_attention_dim		)
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.LayerNorm(A_		)





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			image_embeds.shape[0]

										# image
										_UpperCAmelCase				:						str       						=			self.image_embeds(A_		)
										_UpperCAmelCase				:						Any       						=			image_embeds.reshape(A_  ,    self.num_image_text_embeds  ,    -1		)
										_UpperCAmelCase				:						Union[str, Any]       						=			self.norm(A_		)
										return image_embeds







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase				:						int       						=			Timesteps(num_channels=256  ,    flip_sin_to_cos=A_  ,    downscale_freq_shift=1		)
										_UpperCAmelCase				:						int       						=			TimestepEmbedding(in_channels=256  ,    time_embed_dim=A_		)
										_UpperCAmelCase				:						int       						=			LabelEmbedding(A_  ,    A_  ,    A_		)





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







										'''simple docstring'''


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

										_UpperCAmelCase				:						Optional[Any]       						=			self.class_embedder(A_		)  # (N, D)

										_UpperCAmelCase				:						Tuple       						=			timesteps_emb + class_labels  # (N, D)

										return conditioning







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Dict       						=			nn.LayerNorm(A_		)
										_UpperCAmelCase				:						List[str]       						=			AttentionPooling(A_  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			nn.Linear(A_  ,    A_		)
										_UpperCAmelCase				:						int       						=			nn.LayerNorm(A_		)





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			self.norma(A_		)
										_UpperCAmelCase				:						List[Any]       						=			self.pool(A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.proj(A_		)
										_UpperCAmelCase				:						Tuple       						=			self.norma(A_		)
										return hidden_states







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Tuple       						=			nn.Linear(A_  ,    A_		)
										_UpperCAmelCase				:						Optional[int]       						=			nn.LayerNorm(A_		)
										_UpperCAmelCase				:						str       						=			nn.Linear(A_  ,    A_		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


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

										# image
										_UpperCAmelCase				:						Any       						=			self.image_proj(A_		)

										return time_image_embeds + time_text_embeds







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Tuple       						=			nn.Linear(A_  ,    A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.LayerNorm(A_		)





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			self.image_proj(A_		)
										_UpperCAmelCase				:						Optional[int]       						=			self.image_norm(A_		)
										return time_image_embeds







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.Linear(A_  ,    A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			nn.LayerNorm(A_		)
										_UpperCAmelCase				:						Union[str, 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  ,    256  ,    3  ,    padding=1  ,    stride=2		)  ,    nn.SiLU()  ,    nn.Convad(256  ,    4  ,    3  ,    padding=1		)  ,    )





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			self.image_proj(A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.image_norm(A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			self.input_hint_block(A_		)
										return time_image_embeds, hint







class  a     (		nn.Module ):


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







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase				:						Tuple       						=			dtype
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.Parameter(torch.randn(1  ,    A_		) / embed_dim**0.5		)
										_UpperCAmelCase				:						Dict       						=			nn.Linear(A_  ,    A_  ,    dtype=self.dtype		)
										_UpperCAmelCase				:						int       						=			nn.Linear(A_  ,    A_  ,    dtype=self.dtype		)
										_UpperCAmelCase				:						Union[str, Any]       						=			nn.Linear(A_  ,    A_  ,    dtype=self.dtype		)
										_UpperCAmelCase				:						Optional[int]       						=			num_heads
										_UpperCAmelCase				:						Optional[int]       						=			embed_dim // self.num_heads





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						int       						=			x.size()

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

										_UpperCAmelCase				:						List[Any]       						=			x.mean(dim=1  ,    keepdim=A_		) + self.positional_embedding.to(x.dtype		)
										_UpperCAmelCase				:						Tuple       						=			torch.cat([class_token, x]  ,    dim=1		)  # (bs, length+1, width)

										# (bs*n_heads, class_token_length, dim_per_head)
										_UpperCAmelCase				:						int       						=			shape(self.q_proj(A_		)		)
										# (bs*n_heads, length+class_token_length, dim_per_head)
										_UpperCAmelCase				:						Dict       						=			shape(self.k_proj(A_		)		)
										_UpperCAmelCase				:						List[str]       						=			shape(self.v_proj(A_		)		)

										# (bs*n_heads, class_token_length, length+class_token_length):
										_UpperCAmelCase				:						Any       						=			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				:						Dict       						=			torch.softmax(weight.float()  ,    dim=-1		).type(weight.dtype		)

										# (bs*n_heads, dim_per_head, class_token_length)
										_UpperCAmelCase				:						int       						=			torch.einsum("bts,bcs->bct"  ,    A_  ,    A_		)

										# (bs, length+1, width)
										_UpperCAmelCase				:						int       						=			a.reshape(A_  ,    -1  ,    1		).transpose(1  ,    2		)

										return a[:, 0, :]  # cls_token



import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



