



"""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  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]






					@register_to_config
					def __init__(       self:							Union[str, Any]						,     snake_case:							int						,     snake_case:							int						,     snake_case:							Optional[int] = None						,     snake_case:							int = 50_257						,     snake_case:							int = 1_024						,     snake_case:							int = 768						,     snake_case:							int = 12						,     snake_case:							int = 12						,     snake_case:							Optional[int] = None						,     snake_case:							str = "gelu_new"						,     snake_case:							float = 0.1						,     snake_case:							float = 0.1						,     snake_case:							float = 0.1						,     snake_case:							float = 1E-5						,     snake_case:							float = 0.0_2						,     snake_case:							bool = True						,     snake_case:							bool = True						,     snake_case:							bool = False						,     snake_case:							bool = False						,     )   ->	Tuple:
											super().__init__()

											snake_case_ :Tuple						=			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."""						)

											snake_case_ :Union[str, Any]						=			prefix_inner_dim
											snake_case_ :Optional[Any]						=			prefix_hidden_dim

											snake_case_ :Dict						=			(
											    nn.Linear(self.prefix_inner_dim						,     self.prefix_hidden_dim						)
											    if self.prefix_hidden_dim is not None
											    else nn.Identity()
											)
											snake_case_ :str						=			(
											    nn.Linear(self.prefix_hidden_dim						,     snake_case						) if self.prefix_hidden_dim is not None else nn.Identity()
											)

											snake_case_ :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						,     )
											snake_case_ :Dict						=			GPTaLMHeadModel(snake_case						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							torch.Tensor						,     snake_case:							torch.Tensor						,     snake_case:							Optional[torch.Tensor] = None						,     snake_case:							Optional[torch.Tensor] = None						,     )   ->	Union[str, Any]:
											snake_case_ :Tuple						=			self.transformer.transformer.wte(snake_case						)
											snake_case_ :str						=			self.encode_prefix(snake_case						)
											snake_case_ :List[Any]						=			self.decode_prefix(snake_case						)
											snake_case_ :Dict						=			torch.cat((prefix_embeds, embedding_text)						,     dim=1						)

											if labels is not None:
																	snake_case_ :Tuple						=			self.get_dummy_token(input_ids.shape[0]						,     input_ids.device						)
																	snake_case_ :Union[str, Any]						=			torch.cat((dummy_token, input_ids)						,     dim=1						)
											snake_case_ :Tuple						=			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    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							int						,     snake_case:							torch.device						)   ->	torch.Tensor:
											return torch.zeros(snake_case						,     self.prefix_length						,     dtype=torch.intaa						,     device=snake_case						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							int						)   ->	List[Any]:
											return self.encode_prefix(snake_case						)






					@torch.no_grad()
					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Tuple						,     snake_case:							int						,     snake_case:							List[Any]						)   ->	Dict:
											snake_case_ :List[Any]						=			torch.split(snake_case						,     1						,     dim=0						)
											snake_case_ :Optional[int]						=			[]
											snake_case_ :str						=			[]
											for feature in features:
																	snake_case_ :Tuple						=			self.decode_prefix(feature.to(snake_case						)						)  # back to the clip feature
																	# Only support beam search for now
																	snake_case_,       snake_case_ :Union[str, Any]						=			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]						)
											snake_case_ :Optional[int]						=			torch.stack(snake_case						)
											snake_case_ :Tuple						=			torch.stack(snake_case						)
											return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							List[Any]=None						,     snake_case:							Dict=None						,     snake_case:							List[Any]=None						,     snake_case:							int = 5						,     snake_case:							int = 67						,     snake_case:							float = 1.0						,     snake_case:							Optional[int] = None						,     )   ->	Tuple:
											snake_case_ :int						=			eos_token_id
											snake_case_ :Tuple						=			None
											snake_case_ :Union[str, Any]						=			None
											snake_case_ :int						=			torch.ones(snake_case						,     device=snake_case						,     dtype=torch.int						)
											snake_case_ :List[Any]						=			torch.zeros(snake_case						,     device=snake_case						,     dtype=torch.bool						)

											if input_embeds is not None:
																	snake_case_ :str						=			input_embeds
											else:
																	snake_case_ :Optional[int]						=			self.transformer.transformer.wte(snake_case						)

											for i in range(snake_case						):
																	snake_case_ :str						=			self.transformer(inputs_embeds=snake_case						)
																	snake_case_ :int						=			outputs.logits
																	snake_case_ :Tuple						=			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																	snake_case_ :List[str]						=			logits.softmax(-1						).log()

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

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

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




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

