






"""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      a						(					__snake_case  ,      __snake_case  ,      __snake_case		):
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]






    @register_to_config
    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 50257       ,						__SCREAMING_SNAKE_CASE     :							int = 1024       ,						__SCREAMING_SNAKE_CASE     :							int = 768       ,						__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 = 0.02       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						__SCREAMING_SNAKE_CASE     :							bool = False       ,						)     ->		Union[str, Any]:
      super().__init__()

      lowerCamelCase_         =						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.'''					)

      lowerCamelCase_         =						prefix_inner_dim
      lowerCamelCase_         =						prefix_hidden_dim

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

      lowerCamelCase_         =						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       ,						)
      lowerCamelCase_         =						GPTaLMHeadModel(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							torch.Tensor       ,						__SCREAMING_SNAKE_CASE     :							torch.Tensor       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Tensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Tensor] = None       ,						)     ->		List[str]:
      lowerCamelCase_         =						self.transformer.transformer.wte(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.encode_prefix(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.decode_prefix(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						torch.cat((prefix_embeds, embedding_text)       ,						dim=1					)

      if labels is not None:
        lowerCamelCase_         =						self.get_dummy_token(input_ids.shape[0]       ,						input_ids.device					)
        lowerCamelCase_         =						torch.cat((dummy_token, input_ids)       ,						dim=1					)
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Tuple       ,						__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    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Optional[int]:
      return self.encode_prefix(__SCREAMING_SNAKE_CASE					)






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






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=None       ,						__SCREAMING_SNAKE_CASE     :							int=None       ,						__SCREAMING_SNAKE_CASE     :							int=None       ,						__SCREAMING_SNAKE_CASE     :							int = 5       ,						__SCREAMING_SNAKE_CASE     :							int = 67       ,						__SCREAMING_SNAKE_CASE     :							float = 1.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						)     ->		Union[str, Any]:
      lowerCamelCase_         =						eos_token_id
      lowerCamelCase_         =						None
      lowerCamelCase_         =						None
      lowerCamelCase_         =						torch.ones(__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE       ,						dtype=torch.int					)
      lowerCamelCase_         =						torch.zeros(__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE       ,						dtype=torch.bool					)

      if input_embeds is not None:
        lowerCamelCase_         =						input_embeds
      else:
        lowerCamelCase_         =						self.transformer.transformer.wte(__SCREAMING_SNAKE_CASE					)

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

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

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

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












"""simple docstring"""





import argparse
import json
import subprocess







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		str )      ->				List[Any]:
  lowerCamelCase_         =						[]

  lowerCamelCase_         =						(
      F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowerCamelCase_         =						subprocess.run(_lowerCamelCase		,				shell=_lowerCamelCase		,				stdout=subprocess.PIPE )
  lowerCamelCase_         =						output.stdout.decode('utf-8' )
  lowerCamelCase_         =						json.loads(_lowerCamelCase )

  lowerCamelCase_         =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase )

    # save the result so we can report them on Slack
  with open('offline_runners.txt'		,				'w' ) as fp:
    fp.write(json.dumps(_lowerCamelCase ) )

  if len(_lowerCamelCase ) > 0:
    lowerCamelCase_         =						'\n'.join([x['name'] for x in offline_runners] )
    raise ValueError(F'''The following runners are offline:\n{failed}''' )


if __name__ == "__main__":







  def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				Tuple:
    return values.split(',' )

  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--target_runners''',
      default=None,
      type=list_str,
      required=True,
      help='''Comma-separated list of runners to check status.''',
  )

  parser.add_argument(
      '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
  )
  _SCREAMING_SNAKE_CASE					:				Any					    =						parser.parse_args()






  get_runner_status(args.target_runners, args.token)




