






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 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase		):
     __lowerCamelCase      				=  [R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']




     @register_to_config
     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 50257	,						_lowerCAmelCase = 1024	,						_lowerCAmelCase = 768	,						_lowerCAmelCase = 12	,						_lowerCAmelCase = 12	,						_lowerCAmelCase = None	,						_lowerCAmelCase = "gelu_new"	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 0.1	,						_lowerCAmelCase = 1e-5	,						_lowerCAmelCase = 0.0_2	,						_lowerCAmelCase = True	,						_lowerCAmelCase = True	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						):
          super().__init__()

          UpperCAmelCase__      :  int				      =			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__      :  Optional[Any]				      =			prefix_inner_dim
          UpperCAmelCase__      :  Union[str, Any]				      =			prefix_hidden_dim

          UpperCAmelCase__      :  List[Any]				      =			(
              nn.Linear(self.prefix_inner_dim	,						self.prefix_hidden_dim		)
              if self.prefix_hidden_dim is not None
              else nn.Identity()
          )
          UpperCAmelCase__      :  str				      =			(
              nn.Linear(self.prefix_hidden_dim	,						_lowerCAmelCase		) if self.prefix_hidden_dim is not None else nn.Identity()
          )

          UpperCAmelCase__      :  Optional[Any]				      =			GPTaConfig(
              vocab_size=_lowerCAmelCase	,						n_positions=_lowerCAmelCase	,						n_embd=_lowerCAmelCase	,						n_layer=_lowerCAmelCase	,						n_head=_lowerCAmelCase	,						n_inner=_lowerCAmelCase	,						activation_function=_lowerCAmelCase	,						resid_pdrop=_lowerCAmelCase	,						embd_pdrop=_lowerCAmelCase	,						attn_pdrop=_lowerCAmelCase	,						layer_norm_epsilon=_lowerCAmelCase	,						initializer_range=_lowerCAmelCase	,						scale_attn_weights=_lowerCAmelCase	,						use_cache=_lowerCAmelCase	,						scale_attn_by_inverse_layer_idx=_lowerCAmelCase	,						reorder_and_upcast_attn=_lowerCAmelCase	,						)
          UpperCAmelCase__      :  str				      =			GPTaLMHeadModel(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  Optional[Any]				      =			self.transformer.transformer.wte(_lowerCAmelCase		)
          UpperCAmelCase__      :  str				      =			self.encode_prefix(_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			self.decode_prefix(_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			torch.cat((prefix_embeds, embedding_text)	,						dim=1		)

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




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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return self.encode_prefix(_lowerCAmelCase		)




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




     @torch.no_grad()
     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase = 5	,						_lowerCAmelCase = 67	,						_lowerCAmelCase = 1.0	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			eos_token_id
          UpperCAmelCase__      :  Any				      =			None
          UpperCAmelCase__      :  List[str]				      =			None
          UpperCAmelCase__      :  Dict				      =			torch.ones(_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=torch.int		)
          UpperCAmelCase__      :  str				      =			torch.zeros(_lowerCAmelCase	,						device=_lowerCAmelCase	,						dtype=torch.bool		)

          if input_embeds is not None:
               UpperCAmelCase__      :  Union[str, Any]				      =			input_embeds
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			self.transformer.transformer.wte(_lowerCAmelCase		)

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

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

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

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










"""simple docstring"""




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format




class 			__snake_case   :
							__a					 =       None




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )
										__lowerCamelCase          =      json.loads(feat_extract.to_json_string()   )
										for key, value in self.feat_extract_dict.items():
													self.assertEqual(obj[key]     ,							A_   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCamelCase          =      os.path.join(A_     ,							"""feat_extract.json"""   )
													feat_extract_first.to_json_file(A_   )
													__lowerCamelCase          =      self.feature_extraction_class.from_json_file(A_   )

										self.assertEqual(feat_extract_second.to_dict()     ,							feat_extract_first.to_dict()   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCamelCase          =      feat_extract_first.save_pretrained(A_   )[0]
													check_json_file_has_correct_format(A_   )
													__lowerCamelCase          =      self.feature_extraction_class.from_pretrained(A_   )

										self.assertEqual(feat_extract_second.to_dict()     ,							feat_extract_first.to_dict()   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.feature_extraction_class()
										self.assertIsNotNone(A_   )




