


"""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 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Optional[int]        =   [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






       @register_to_config
       def __init__(   self  :    Dict  ,     snake_case__  :    int  ,     snake_case__  :    int  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    int = 5_0_2_5_7  ,     snake_case__  :    int = 1_0_2_4  ,     snake_case__  :    int = 7_6_8  ,     snake_case__  :    int = 1_2  ,     snake_case__  :    int = 1_2  ,     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 = 0.02  ,     snake_case__  :    bool = True  ,     snake_case__  :    bool = True  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = False  ,     ):

              '''simple docstring'''






              super().__init__()

              lowercase		:Optional[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."""				)

              lowercase		:List[Any]       							=				prefix_inner_dim
              lowercase		:List[Any]       							=				prefix_hidden_dim

              lowercase		:str       							=				(
                  nn.Linear(self.prefix_inner_dim  ,     self.prefix_hidden_dim				)
                  if self.prefix_hidden_dim is not None
                  else nn.Identity()
              )
              lowercase		:Optional[Any]       							=				(
                  nn.Linear(self.prefix_hidden_dim  ,     snake_case__				) if self.prefix_hidden_dim is not None else nn.Identity()
              )

              lowercase		:Optional[int]       							=				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__  ,     )
              lowercase		:Union[str, Any]       							=				GPTaLMHeadModel(snake_case__				)






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.Tensor  ,     snake_case__  :    torch.Tensor  ,     snake_case__  :    Optional[torch.Tensor] = None  ,     snake_case__  :    Optional[torch.Tensor] = None  ,     ):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				self.transformer.transformer.wte(snake_case__				)
              lowercase		:str       							=				self.encode_prefix(snake_case__				)
              lowercase		:Any       							=				self.decode_prefix(snake_case__				)
              lowercase		:Optional[int]       							=				torch.cat((prefix_embeds, embedding_text)  ,     dim=1				)

              if labels is not None:
                     lowercase		:Dict       							=				self.get_dummy_token(input_ids.shape[0]  ,     input_ids.device				)
                     lowercase		:List[str]       							=				torch.cat((dummy_token, input_ids)  ,     dim=1				)
              lowercase		:Any       							=				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  __snake_case						(   self  :    Dict  ,     snake_case__  :    int  ,     snake_case__  :    torch.device				):

              '''simple docstring'''






              return torch.zeros(snake_case__  ,     self.prefix_length  ,     dtype=torch.intaa  ,     device=snake_case__				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    int				):

              '''simple docstring'''






              return self.encode_prefix(snake_case__				)






       @torch.no_grad()
       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    str  ,     snake_case__  :    int  ,     snake_case__  :    Any				):

              '''simple docstring'''






              lowercase		:str       							=				torch.split(snake_case__  ,     1  ,     dim=0				)
              lowercase		:Optional[int]       							=				[]
              lowercase		:Any       							=				[]
              for feature in features:
                     lowercase		:Any       							=				self.decode_prefix(feature.to(snake_case__				)				)  # back to the clip feature
                     # Only support beam search for now
                     lowercase							,  lowercase		:List[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]				)
              lowercase		:Optional[int]       							=				torch.stack(snake_case__				)
              lowercase		:List[Any]       							=				torch.stack(snake_case__				)
              return generated_tokens, generated_seq_lengths







       @torch.no_grad()
       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    str=None  ,     snake_case__  :    Union[str, Any]=None  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    int = 5  ,     snake_case__  :    int = 6_7  ,     snake_case__  :    float = 1.0  ,     snake_case__  :    Optional[int] = None  ,     ):

              '''simple docstring'''






              lowercase		:Tuple       							=				eos_token_id
              lowercase		:str       							=				None
              lowercase		:str       							=				None
              lowercase		:Dict       							=				torch.ones(snake_case__  ,     device=snake_case__  ,     dtype=torch.int				)
              lowercase		:Optional[int]       							=				torch.zeros(snake_case__  ,     device=snake_case__  ,     dtype=torch.bool				)

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

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

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

                     lowercase		:Union[str, Any]       							=				self.transformer.transformer.wte(next_tokens.squeeze()				).view(generated.shape[0]  ,     1  ,     -1				)
                     lowercase		:List[Any]       							=				torch.cat((generated, next_token_embed)  ,     dim=1				)
                     lowercase		:List[str]       							=				is_stopped + next_tokens.eq(snake_case__				).squeeze()
                     if is_stopped.all():
                            break

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





"""simple docstring"""



import unittest

import numpy as np






def       lowerCamelCase					(a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray | None = None				,				)     ->							np.ndarray:
       lowercase		:str       							=				np.shape(a_)
       lowercase		:Dict       							=				np.shape(a_)
       lowercase		:Optional[int]       							=				np.shape(a_)

       if shape_a[0] != shape_b[0]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of rows for A and B. '''
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(a_)

       if shape_b[1] != shape_c[1]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of columns for B and C. '''
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(a_)

       lowercase		:Optional[Any]       							=				pseudo_inv
       if a_inv is None:
              try:
                     lowercase		:int       							=				np.linalg.inv(a_)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.''')

       return mat_c - mat_b.T @ a_inv @ mat_b
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Dict       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:List[str]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              lowercase		:Any       							=				schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:int       							=				np.block([[a, b], [b.T, c]]				)

              lowercase		:Tuple       							=				np.linalg.det(snake_case__				)
              lowercase		:Any       							=				np.linalg.det(snake_case__				)
              lowercase		:List[Any]       							=				np.linalg.det(snake_case__				)

              self.assertAlmostEqual(snake_case__  ,     det_a * det_s				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:Union[str, Any]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:int       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[Any]       							=				np.array([[2, 1, 3], [6, 3, 5]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

