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





     @register_to_config
     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 5_0257			,  __UpperCAmelCase = 1024			,  __UpperCAmelCase = 768			,  __UpperCAmelCase = 12			,  __UpperCAmelCase = 12			,  __UpperCAmelCase = None			,  __UpperCAmelCase = "gelu_new"			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = False			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__()

      __UpperCamelCase       						= 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       						= prefix_inner_dim
      __UpperCamelCase       						= prefix_hidden_dim

      __UpperCamelCase       						= (
          nn.Linear(self.prefix_inner_dim			,  self.prefix_hidden_dim   )
          if self.prefix_hidden_dim is not None
          else nn.Identity()
      )
      __UpperCamelCase       						= (
          nn.Linear(self.prefix_hidden_dim			,  __UpperCAmelCase   ) if self.prefix_hidden_dim is not None else nn.Identity()
      )

      __UpperCamelCase       						= GPTaConfig(
          vocab_size=__UpperCAmelCase			,  n_positions=__UpperCAmelCase			,  n_embd=__UpperCAmelCase			,  n_layer=__UpperCAmelCase			,  n_head=__UpperCAmelCase			,  n_inner=__UpperCAmelCase			,  activation_function=__UpperCAmelCase			,  resid_pdrop=__UpperCAmelCase			,  embd_pdrop=__UpperCAmelCase			,  attn_pdrop=__UpperCAmelCase			,  layer_norm_epsilon=__UpperCAmelCase			,  initializer_range=__UpperCAmelCase			,  scale_attn_weights=__UpperCAmelCase			,  use_cache=__UpperCAmelCase			,  scale_attn_by_inverse_layer_idx=__UpperCAmelCase			,  reorder_and_upcast_attn=__UpperCAmelCase			,  )
      __UpperCamelCase       						= GPTaLMHeadModel(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= self.transformer.transformer.wte(__UpperCAmelCase   )
      __UpperCamelCase       						= self.encode_prefix(__UpperCAmelCase   )
      __UpperCamelCase       						= self.decode_prefix(__UpperCAmelCase   )
      __UpperCamelCase       						= torch.cat((prefix_embeds, embedding_text)			,  dim=1   )

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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.encode_prefix(__UpperCAmelCase   )





     @torch.no_grad()
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= torch.split(__UpperCAmelCase			,  1			,  dim=0   )
      __UpperCamelCase       						= []
      __UpperCamelCase       						= []
      for feature in features:
       __UpperCamelCase       						= self.decode_prefix(feature.to(__UpperCAmelCase   )   )  # back to the clip feature
       # Only support beam search for now
       __UpperCamelCase						,			__UpperCamelCase       						= self.generate_beam(
           input_embeds=__UpperCAmelCase			,  device=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase   )
       generated_tokens.append(output_tokens[0]   )
       generated_seq_lengths.append(seq_lengths[0]   )
      __UpperCamelCase       						= torch.stack(__UpperCAmelCase   )
      __UpperCamelCase       						= torch.stack(__UpperCAmelCase   )
      return generated_tokens, generated_seq_lengths







     @torch.no_grad()
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 67			,  __UpperCAmelCase = 1.0			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= eos_token_id
      __UpperCamelCase       						= None
      __UpperCamelCase       						= None
      __UpperCamelCase       						= torch.ones(__UpperCAmelCase			,  device=__UpperCAmelCase			,  dtype=torch.int   )
      __UpperCamelCase       						= torch.zeros(__UpperCAmelCase			,  device=__UpperCAmelCase			,  dtype=torch.bool   )

      if input_embeds is not None:
       __UpperCamelCase       						= input_embeds
      else:
       __UpperCamelCase       						= self.transformer.transformer.wte(__UpperCAmelCase   )

      for i in range(__UpperCAmelCase   ):
       __UpperCamelCase       						= self.transformer(inputs_embeds=__UpperCAmelCase   )
       __UpperCamelCase       						= outputs.logits
       __UpperCamelCase       						= logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
       __UpperCamelCase       						= logits.softmax(-1   ).log()

       if scores is None:
        __UpperCamelCase						,			__UpperCamelCase       						= logits.topk(__UpperCAmelCase			,  -1   )
        __UpperCamelCase       						= generated.expand(__UpperCAmelCase			,  *generated.shape[1:]   )
        __UpperCamelCase						,			__UpperCamelCase       						= next_tokens.permute(1			,  0   ), scores.squeeze(0   )
        if tokens is None:
         __UpperCamelCase       						= next_tokens
        else:
         __UpperCamelCase       						= tokens.expand(__UpperCAmelCase			,  *tokens.shape[1:]   )
         __UpperCamelCase       						= torch.cat((tokens, next_tokens)			,  dim=1   )
       else:
        __UpperCamelCase       						= -float(np.inf   )
        __UpperCamelCase       						= 0
        __UpperCamelCase       						= scores[:, None] + logits
        seq_lengths[~is_stopped] += 1
        __UpperCamelCase       						= scores_sum / seq_lengths[:, None]
        __UpperCamelCase						,			__UpperCamelCase       						= scores_sum_average.view(-1   ).topk(__UpperCAmelCase			,  -1   )
        __UpperCamelCase       						= next_tokens // scores_sum.shape[1]
        __UpperCamelCase       						= seq_lengths[next_tokens_source]
        __UpperCamelCase       						= next_tokens % scores_sum.shape[1]
        __UpperCamelCase       						= next_tokens.unsqueeze(1   )
        __UpperCamelCase       						= tokens[next_tokens_source]
        __UpperCamelCase       						= torch.cat((tokens, next_tokens)			,  dim=1   )
        __UpperCamelCase       						= generated[next_tokens_source]
        __UpperCamelCase       						= scores_sum_average * seq_lengths
        __UpperCamelCase       						= is_stopped[next_tokens_source]

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

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





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



