






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__ (							__snake_case     ,				__snake_case     ,				__snake_case							):
		__snake_case      :       str     =   [R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







		@register_to_config
		def __init__(					self       ,A__       ,A__       ,A__ = None       ,A__ = 50257       ,A__ = 1024       ,A__ = 768       ,A__ = 12       ,A__ = 12       ,A__ = None       ,A__ = "gelu_new"       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 1E-5       ,A__ = 0.02       ,A__ = True       ,A__ = True       ,A__ = False       ,A__ = False       ,):
						super().__init__()

						_A	:				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."""		)

						_A	:				Any   =      prefix_inner_dim
						_A	:				Tuple   =      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	:				List[Any]   =      (
						    nn.Linear(self.prefix_hidden_dim       ,A__		) if self.prefix_hidden_dim is not None else nn.Identity()
						)

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







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,A__ = None       ,):
						_A	:				Any   =      self.transformer.transformer.wte(A__		)
						_A	:				List[Any]   =      self.encode_prefix(A__		)
						_A	:				str   =      self.decode_prefix(A__		)
						_A	:				Any   =      torch.cat((prefix_embeds, embedding_text)       ,dim=1		)

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







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







		def  A__      (					self       ,A__		):
						return self.encode_prefix(A__		)







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







		@torch.no_grad()
		def  A__      (					self       ,A__=None       ,A__=None       ,A__=None       ,A__ = 5       ,A__ = 67       ,A__ = 1.0       ,A__ = None       ,):
						_A	:				Tuple   =      eos_token_id
						_A	:				int   =      None
						_A	:				Any   =      None
						_A	:				List[Any]   =      torch.ones(A__       ,device=A__       ,dtype=torch.int		)
						_A	:				Dict   =      torch.zeros(A__       ,device=A__       ,dtype=torch.bool		)

						if input_embeds is not None:
										_A	:				Optional[Any]   =      input_embeds
						else:
										_A	:				List[str]   =      self.transformer.transformer.wte(A__		)

						for i in range(A__		):
										_A	:				List[str]   =      self.transformer(inputs_embeds=A__		)
										_A	:				Tuple   =      outputs.logits
										_A	:				Dict   =      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										_A	:				str   =      logits.softmax(-1		).log()

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

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

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











from __future__ import annotations



def  a__ (__lowercase							:list					,  __lowercase							:int | None = None					,  __lowercase							:int | None = None   )		-> None:
				if start is None:
								_A	:				Optional[int]   =      0

				if end is None:
								_A	:				Union[str, Any]   =      len(__lowercase   ) - 1

				if start >= end:
								return

				_A	:				Union[str, Any]   =      (start + end) // 2

				slowsort(__lowercase					,  __lowercase					,  __lowercase   )
				slowsort(__lowercase					,  mid + 1					,  __lowercase   )

				if sequence[end] < sequence[mid]:
								_A  ,				_A	:				Optional[int]   =      sequence[mid], sequence[end]

				slowsort(__lowercase					,  __lowercase					,  end - 1   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()



