



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__					(  _lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase       ):
				A_   :     Union[str, Any]								=     [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']


				@register_to_config
				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  int = 50_257						, __UpperCamelCase      :  int = 1_024						, __UpperCamelCase      :  int = 768						, __UpperCamelCase      :  int = 12						, __UpperCamelCase      :  int = 12						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  str = "gelu_new"						, __UpperCamelCase      :  float = 0.1						, __UpperCamelCase      :  float = 0.1						, __UpperCamelCase      :  float = 0.1						, __UpperCamelCase      :  float = 1e-5						, __UpperCamelCase      :  float = 0.0_2						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, )		->     int:
											super().__init__()

											A      						=    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      						=    prefix_inner_dim
											A      						=    prefix_hidden_dim

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

											A      						=    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						, )
											A      						=    GPTaLMHeadModel(__UpperCamelCase       )


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

											if labels is not None:
																		A      						=    self.get_dummy_token(input_ids.shape[0]						, input_ids.device       )
																		A      						=    torch.cat((dummy_token, input_ids)						, dim=1       )
											A      						=    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      :  Dict						, __UpperCamelCase      :  int						, __UpperCamelCase      :  torch.device       )		->     torch.Tensor:
											return torch.zeros(__UpperCamelCase						, self.prefix_length						, dtype=torch.intaa						, device=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  Tuple       )		->     Optional[int]:
											return self.encode_prefix(__UpperCamelCase       )


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


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

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

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

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

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

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



'''simple docstring'''
class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      None:
            _A				=							size
            _A				=							[0] * size
            _A				=							[0] * size






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return index | (index + 1)






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return (index & (index + 1)) - 1






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      None:
            _A				=							value
            while index < self.size:
                 _A				=							self.get_prev(_UpperCamelCase   ) + 1
                 if current_left_border == index:
                      _A				=							value
                 else:
                      _A				=							max(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							self.get_next(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      int:
            right -= 1  # Because of right is exclusive
            _A				=							0
            while left <= right:
                 _A				=							self.get_prev(_UpperCamelCase   )
                 if left <= current_left:
                      _A				=							max(_UpperCamelCase			,     self.tree[right]   )
                      _A				=							current_left
                 else:
                      _A				=							max(_UpperCamelCase			,     self.arr[right]   )
                      right -= 1
            return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()





