




"""simple docstring"""




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 				lowerCamelCase		(			a__						,	a__						,	a__					):
   lowercase							:	Optional[int]  =					[R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']







   @register_to_config
   def __init__(				self     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_ = None     ,   SCREAMING_SNAKE_CASE_ = 5_0257     ,   SCREAMING_SNAKE_CASE_ = 1024     ,   SCREAMING_SNAKE_CASE_ = 768     ,   SCREAMING_SNAKE_CASE_ = 12     ,   SCREAMING_SNAKE_CASE_ = 12     ,   SCREAMING_SNAKE_CASE_ = None     ,   SCREAMING_SNAKE_CASE_ = "gelu_new"     ,   SCREAMING_SNAKE_CASE_ = 0.1     ,   SCREAMING_SNAKE_CASE_ = 0.1     ,   SCREAMING_SNAKE_CASE_ = 0.1     ,   SCREAMING_SNAKE_CASE_ = 1e-5     ,   SCREAMING_SNAKE_CASE_ = 0.02     ,   SCREAMING_SNAKE_CASE_ = True     ,   SCREAMING_SNAKE_CASE_ = True     ,   SCREAMING_SNAKE_CASE_ = False     ,   SCREAMING_SNAKE_CASE_ = False     ,   ):
       super().__init__()

       UpperCamelCase					: List[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.'			)

       UpperCamelCase					: int 							=				prefix_inner_dim
       UpperCamelCase					: Dict 							=				prefix_hidden_dim

       UpperCamelCase					: Union[str, Any] 							=				(
           nn.Linear(self.prefix_inner_dim     ,   self.prefix_hidden_dim			)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       UpperCamelCase					: Tuple 							=				(
           nn.Linear(self.prefix_hidden_dim     ,   _snake_case			) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       UpperCamelCase					: Any 							=				GPTaConfig(
           vocab_size=_snake_case     ,   n_positions=_snake_case     ,   n_embd=_snake_case     ,   n_layer=_snake_case     ,   n_head=_snake_case     ,   n_inner=_snake_case     ,   activation_function=_snake_case     ,   resid_pdrop=_snake_case     ,   embd_pdrop=_snake_case     ,   attn_pdrop=_snake_case     ,   layer_norm_epsilon=_snake_case     ,   initializer_range=_snake_case     ,   scale_attn_weights=_snake_case     ,   use_cache=_snake_case     ,   scale_attn_by_inverse_layer_idx=_snake_case     ,   reorder_and_upcast_attn=_snake_case     ,   )
       UpperCamelCase					: str 							=				GPTaLMHeadModel(_snake_case			)







   def    a_				(				self     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_ = None     ,   SCREAMING_SNAKE_CASE_ = None     ,   ):
       UpperCamelCase					: Optional[int] 							=				self.transformer.transformer.wte(_snake_case			)
       UpperCamelCase					: List[str] 							=				self.encode_prefix(_snake_case			)
       UpperCamelCase					: Optional[Any] 							=				self.decode_prefix(_snake_case			)
       UpperCamelCase					: str 							=				torch.cat((prefix_embeds, embedding_text)     ,   dim=1			)

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







   def    a_				(				self     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_			):
       return torch.zeros(_snake_case     ,   self.prefix_length     ,   dtype=torch.intaa     ,   device=_snake_case			)







   def    a_				(				self     ,   SCREAMING_SNAKE_CASE_			):
       return self.encode_prefix(_snake_case			)







   @torch.no_grad()
   def    a_				(				self     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_			):
       UpperCamelCase					: Dict 							=				torch.split(_snake_case     ,   1     ,   dim=0			)
       UpperCamelCase					: List[Any] 							=				[]
       UpperCamelCase					: Tuple 							=				[]
       for feature in features:
           UpperCamelCase					: Optional[int] 							=				self.decode_prefix(feature.to(_snake_case			)			)  # back to the clip feature
           # Only support beam search for now
           UpperCamelCase							,				UpperCamelCase					: str 							=				self.generate_beam(
               input_embeds=_snake_case     ,   device=_snake_case     ,   eos_token_id=_snake_case			)
           generated_tokens.append(output_tokens[0]			)
           generated_seq_lengths.append(seq_lengths[0]			)
       UpperCamelCase					: Dict 							=				torch.stack(_snake_case			)
       UpperCamelCase					: Dict 							=				torch.stack(_snake_case			)
       return generated_tokens, generated_seq_lengths







   @torch.no_grad()
   def    a_				(				self     ,   SCREAMING_SNAKE_CASE_=None     ,   SCREAMING_SNAKE_CASE_=None     ,   SCREAMING_SNAKE_CASE_=None     ,   SCREAMING_SNAKE_CASE_ = 5     ,   SCREAMING_SNAKE_CASE_ = 67     ,   SCREAMING_SNAKE_CASE_ = 1.0     ,   SCREAMING_SNAKE_CASE_ = None     ,   ):
       UpperCamelCase					: Tuple 							=				eos_token_id
       UpperCamelCase					: int 							=				None
       UpperCamelCase					: Union[str, Any] 							=				None
       UpperCamelCase					: Union[str, Any] 							=				torch.ones(_snake_case     ,   device=_snake_case     ,   dtype=torch.int			)
       UpperCamelCase					: Optional[Any] 							=				torch.zeros(_snake_case     ,   device=_snake_case     ,   dtype=torch.bool			)

       if input_embeds is not None:
           UpperCamelCase					: Tuple 							=				input_embeds
       else:
           UpperCamelCase					: Union[str, Any] 							=				self.transformer.transformer.wte(_snake_case			)

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

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

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

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











"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class       a							(   a__  ):
 snake_case__             =       42







class       a							(   a__   , a__  ):



 @register_to_config
 def __init__(     self				,       _snake_case = 3				,       _snake_case = 3				,       _snake_case = ("DownEncoderBlock2D",)				,       _snake_case = ("UpDecoderBlock2D",)				,       _snake_case = (64,)				,       _snake_case = 1				,       _snake_case = "silu"				,       _snake_case = 3				,       _snake_case = 32				,       _snake_case = 2_56				,       _snake_case = 32				,       _snake_case = None				,       _snake_case = 0.18_215				,       _snake_case = "group"				,       ):







  """simple docstring"""






  super().__init__()

  # pass init params to Encoder
  lowerCAmelCase    				= Encoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       down_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       double_z=_snake_case				,       )

  lowerCAmelCase    				= vq_embed_dim if vq_embed_dim is not None else latent_channels

  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )
  lowerCAmelCase    				= VectorQuantizer(_snake_case				,       _snake_case				,       beta=0.25				,       remap=_snake_case				,       sane_index_shape=_snake_case     )
  lowerCAmelCase    				= nn.Convad(_snake_case				,       _snake_case				,       1     )

  # pass init params to Decoder
  lowerCAmelCase    				= Decoder(
      in_channels=_snake_case				,       out_channels=_snake_case				,       up_block_types=_snake_case				,       block_out_channels=_snake_case				,       layers_per_block=_snake_case				,       act_fn=_snake_case				,       norm_num_groups=_snake_case				,       norm_type=_snake_case				,       )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= self.encoder(_snake_case     )
  lowerCAmelCase    				= self.quant_conv(_snake_case     )

  if not return_dict:
   return (h,)

  return VQEncoderOutput(latents=_snake_case     )



 @apply_forward_hook
 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = False				,       _snake_case = True     ):







  """simple docstring"""






  if not force_not_quantize:
   lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self.quantize(_snake_case     )
  else:
   lowerCAmelCase    				= h
  lowerCAmelCase    				= self.post_quant_conv(_snake_case     )
  lowerCAmelCase    				= self.decoder(_snake_case				,       quant if self.config.norm_type == 'spatial' else None     )

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = True     ):







  """simple docstring"""






  lowerCAmelCase    				= sample
  lowerCAmelCase    				= self.encode(_snake_case     ).latents
  lowerCAmelCase    				= self.decode(_snake_case     ).sample

  if not return_dict:
   return (dec,)

  return DecoderOutput(sample=_snake_case     )





