from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_						):
  lowercase_			=     [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']




  @register_to_config
  def __init__(      self     :	Dict     , a_     :	int     , a_     :	int     , a_     :	Optional[int] = None     , a_     :	int = 5_0257     , a_     :	int = 1024     , a_     :	int = 768     , a_     :	int = 12     , a_     :	int = 12     , a_     :	Optional[int] = None     , a_     :	str = "gelu_new"     , a_     :	float = 0.1     , a_     :	float = 0.1     , a_     :	float = 0.1     , a_     :	float = 1e-5     , a_     :	float = 0.02     , a_     :	bool = True     , a_     :	bool = True     , a_     :	bool = False     , a_     :	bool = False     , )->       Optional[Any]:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		prefix_length

       if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
            raise ValueError(
                F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                F''' `n_embd`: {n_embd} are not equal.'''				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		prefix_inner_dim
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		prefix_hidden_dim

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
           nn.Linear(self.prefix_inner_dim     , self.prefix_hidden_dim				)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(
           nn.Linear(self.prefix_hidden_dim     , a_				) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       SCREAMING_SNAKE_CASE__				:				Tuple			=		GPTaConfig(
           vocab_size=a_     , n_positions=a_     , n_embd=a_     , n_layer=a_     , n_head=a_     , n_inner=a_     , activation_function=a_     , resid_pdrop=a_     , embd_pdrop=a_     , attn_pdrop=a_     , layer_norm_epsilon=a_     , initializer_range=a_     , scale_attn_weights=a_     , use_cache=a_     , scale_attn_by_inverse_layer_idx=a_     , reorder_and_upcast_attn=a_     , )
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		GPTaLMHeadModel(a_				)




  def 	__lowercase(      self     :	Any     , a_     :	torch.Tensor     , a_     :	torch.Tensor     , a_     :	Optional[torch.Tensor] = None     , a_     :	Optional[torch.Tensor] = None     , )->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.transformer.transformer.wte(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.encode_prefix(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		self.decode_prefix(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat((prefix_embeds, embedding_text)     , dim=1				)

       if labels is not None:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_dummy_token(input_ids.shape[0]     , input_ids.device				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.cat((dummy_token, input_ids)     , dim=1				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.transformer(inputs_embeds=a_     , labels=a_     , attention_mask=a_				)
       if self.prefix_hidden_dim is not None:
            return out, hidden
       else:
            return out




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	torch.device				)->       torch.Tensor:




       """simple docstring"""
       return torch.zeros(a_     , self.prefix_length     , dtype=torch.intaa     , device=a_				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Tuple				)->       Dict:




       """simple docstring"""
       return self.encode_prefix(a_				)




  @torch.no_grad()
  def 	__lowercase(      self     :	Dict     , a_     :	Optional[int]     , a_     :	Optional[int]     , a_     :	List[Any]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.split(a_     , 1     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
       SCREAMING_SNAKE_CASE__				:				Dict			=		[]
       for feature in features:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.decode_prefix(feature.to(a_				)				)  # back to the clip feature
            # Only support beam search for now
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		self.generate_beam(
                input_embeds=a_     , device=a_     , eos_token_id=a_				)
            generated_tokens.append(output_tokens[0]				)
            generated_seq_lengths.append(seq_lengths[0]				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.stack(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		torch.stack(a_				)
       return generated_tokens, generated_seq_lengths




  @torch.no_grad()
  def 	__lowercase(      self     :	Dict     , a_     :	List[str]=None     , a_     :	str=None     , a_     :	Union[str, Any]=None     , a_     :	int = 5     , a_     :	int = 67     , a_     :	float = 1.0     , a_     :	Optional[int] = None     , )->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		eos_token_id
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				int			=		None
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.ones(a_     , device=a_     , dtype=torch.int				)
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.zeros(a_     , device=a_     , dtype=torch.bool				)

       if input_embeds is not None:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		input_embeds
       else:
            SCREAMING_SNAKE_CASE__				:				Any			=		self.transformer.transformer.wte(a_				)

       for i in range(a_				):
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.transformer(inputs_embeds=a_				)
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		outputs.logits
            SCREAMING_SNAKE_CASE__				:				Any			=		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            SCREAMING_SNAKE_CASE__				:				List[str]			=		logits.softmax(-1				).log()

            if scores is None:
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		logits.topk(a_     , -1				)
                 SCREAMING_SNAKE_CASE__				:				int			=		generated.expand(a_     , *generated.shape[1:]				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		next_tokens.permute(1     , 0				), scores.squeeze(0				)
                 if tokens is None:
                      SCREAMING_SNAKE_CASE__				:				str			=		next_tokens
                 else:
                      SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokens.expand(a_     , *tokens.shape[1:]				)
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.cat((tokens, next_tokens)     , dim=1				)
            else:
                 SCREAMING_SNAKE_CASE__				:				Any			=		-float(np.inf				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		0
                 SCREAMING_SNAKE_CASE__				:				Dict			=		scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		scores_sum / seq_lengths[:, None]
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		scores_sum_average.view(-1				).topk(a_     , -1				)
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		next_tokens // scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		seq_lengths[next_tokens_source]
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		next_tokens % scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__				:				Any			=		next_tokens.unsqueeze(1				)
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		tokens[next_tokens_source]
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.cat((tokens, next_tokens)     , dim=1				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		generated[next_tokens_source]
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		scores_sum_average * seq_lengths
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		is_stopped[next_tokens_source]

            SCREAMING_SNAKE_CASE__				:				int			=		self.transformer.transformer.wte(next_tokens.squeeze()				).view(generated.shape[0]     , 1     , -1				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.cat((generated, next_token_embed)     , dim=1				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		is_stopped + next_tokens.eq(a_				).squeeze()
            if is_stopped.all():
                 break

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scores / seq_lengths
       SCREAMING_SNAKE_CASE__				:				List[str]			=		scores.argsort(descending=a_				)
       # tokens tensors are already padded to max_seq_length
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[tokens[i] for i in order]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.stack(a_     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([seq_lengths[i] for i in order]     , dtype=seq_lengths.dtype				)
       return output_texts, seq_lengths




from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class       snake_case					:
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




  def 	__lowercase(      self     :	List[Any]				)->       Union[str, Any]:




       """simple docstring"""
       assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
       assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
       assert len(self.x.shape				) == len(self.y.shape				) == len(self.z.shape				) == len(self.origin.shape				) == 2




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return torch.from_numpy(np.array([self.width, self.height]     , dtype=np.floataa				)				)




  def 	__lowercase(      self     :	Dict				)->       Union[str, Any]:




       """simple docstring"""
       return torch.from_numpy(np.array([self.x_fov, self.y_fov]     , dtype=np.floataa				)				)




  def 	__lowercase(      self     :	Tuple				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		torch.arange(self.height * self.width				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a_     , self.width     , rounding_mode='trunc'				),
           ]     , axis=1     , )
       return coords




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.shape
       SCREAMING_SNAKE_CASE__				:				Tuple			=		int(np.prod(a_				)				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_image_coords()
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.broadcast_to(coords.unsqueeze(0				)     , [batch_size * inner_batch_size, *coords.shape]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_camera_rays(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		rays.view(a_     , inner_batch_size * self.height * self.width     , 2     , 3				)

       return rays




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       SCREAMING_SNAKE_CASE__				:				str			=		coords.view(a_     , -1     , 2				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.resolution()
       SCREAMING_SNAKE_CASE__				:				str			=		self.fov()

       SCREAMING_SNAKE_CASE__				:				Any			=		(flat.float() / (res - 1)) * 2 - 1
       SCREAMING_SNAKE_CASE__				:				Any			=		fracs * torch.tan(fov / 2				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		fracs.view(a_     , -1     , 2				)
       SCREAMING_SNAKE_CASE__				:				str			=		(
           self.z.view(a_     , 1     , 3				)
           + self.x.view(a_     , 1     , 3				) * fracs[:, :, :1]
           + self.y.view(a_     , 1     , 3				) * fracs[:, :, 1:]
       )
       SCREAMING_SNAKE_CASE__				:				Tuple			=		directions / directions.norm(dim=-1     , keepdim=a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		torch.stack(
           [
               torch.broadcast_to(self.origin.view(a_     , 1     , 3				)     , [batch_size, directions.shape[1], 3]				),
               directions,
           ]     , dim=2     , )
       return rays.view(a_     , *a_     , 2     , 3				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	int				)->       "DifferentiableProjectiveCamera":




       """simple docstring"""
       assert width * self.height == height * self.width, "The aspect ratio should not change."
       return DifferentiableProjectiveCamera(
           origin=self.origin     , x=self.x     , y=self.y     , z=self.z     , width=a_     , height=a_     , x_fov=self.x_fov     , y_fov=self.y_fov     , )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				str			=		[]
     for theta in np.linspace(0     ,  2 * np.pi     ,  num=20 ):
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
          z /= np.sqrt(np.sum(z**2 ) )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		-z * 4
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		np.cross(lowercase__     ,  lowercase__ )
          origins.append(lowercase__ )
          xs.append(lowercase__ )
          ys.append(lowercase__ )
          zs.append(lowercase__ )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  x=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  y=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  z=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  width=lowercase__     ,  height=lowercase__     ,  x_fov=0.7     ,  y_fov=0.7     ,  shape=(1, len(lowercase__ ))     ,  )



