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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						lowerCAmelCase_   ,						lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Tuple            =				[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]







  @register_to_config
  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 5_02_57      ,	SCREAMING_SNAKE_CASE__    :					int = 10_24      ,	SCREAMING_SNAKE_CASE__    :					int = 7_68      ,	SCREAMING_SNAKE_CASE__    :					int = 12      ,	SCREAMING_SNAKE_CASE__    :					int = 12      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					str = "gelu_new"      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1      ,	SCREAMING_SNAKE_CASE__    :					float = 1e-5      ,	SCREAMING_SNAKE_CASE__    :					float = 0.0_2      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	)    ->				List[str]:
         super().__init__()

         A						:     str         =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.'     )

         A						:     List[Any]         =prefix_inner_dim
         A						:     Dict         =prefix_hidden_dim

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

         A						:     Dict         =GPTaConfig(
             vocab_size=SCREAMING_SNAKE_CASE__      ,	n_positions=SCREAMING_SNAKE_CASE__      ,	n_embd=SCREAMING_SNAKE_CASE__      ,	n_layer=SCREAMING_SNAKE_CASE__      ,	n_head=SCREAMING_SNAKE_CASE__      ,	n_inner=SCREAMING_SNAKE_CASE__      ,	activation_function=SCREAMING_SNAKE_CASE__      ,	resid_pdrop=SCREAMING_SNAKE_CASE__      ,	embd_pdrop=SCREAMING_SNAKE_CASE__      ,	attn_pdrop=SCREAMING_SNAKE_CASE__      ,	layer_norm_epsilon=SCREAMING_SNAKE_CASE__      ,	initializer_range=SCREAMING_SNAKE_CASE__      ,	scale_attn_weights=SCREAMING_SNAKE_CASE__      ,	use_cache=SCREAMING_SNAKE_CASE__      ,	scale_attn_by_inverse_layer_idx=SCREAMING_SNAKE_CASE__      ,	reorder_and_upcast_attn=SCREAMING_SNAKE_CASE__      ,	)
         A						:     Dict         =GPTaLMHeadModel(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					torch.Tensor      ,	SCREAMING_SNAKE_CASE__    :					torch.Tensor      ,	SCREAMING_SNAKE_CASE__    :					Optional[torch.Tensor] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[torch.Tensor] = None      ,	)    ->				Optional[Any]:
         A						:     str         =self.transformer.transformer.wte(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =self.encode_prefix(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =self.decode_prefix(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =torch.cat((prefix_embeds, embedding_text)      ,	dim=1     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					torch.device     )    ->				torch.Tensor:
         return torch.zeros(SCREAMING_SNAKE_CASE__      ,	self.prefix_length      ,	dtype=torch.intaa      ,	device=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				List[str]:
         return self.encode_prefix(SCREAMING_SNAKE_CASE__     )







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







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

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

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

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

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

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






