






'''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    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Any      =						[R'''h\.\d+\.attn\.bias''', R'''h\.\d+\.attn\.masked_bias''']
 @register_to_config
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = 5_02_57			,     __lowerCAmelCase = 10_24			,     __lowerCAmelCase = 7_68			,     __lowerCAmelCase = 12			,     __lowerCAmelCase = 12			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "gelu_new"			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 1E-5			,     __lowerCAmelCase = 0.02			,     __lowerCAmelCase = True			,     __lowerCAmelCase = True			,     __lowerCAmelCase = False			,     __lowerCAmelCase = False			,     ):
       super().__init__()

       UpperCamelCase_				:   Optional[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_				:   Tuple					    =							prefix_inner_dim
       UpperCamelCase_				:   Optional[Any]					    =							prefix_hidden_dim

       UpperCamelCase_				:   Optional[Any]					    =							(
           nn.Linear(self.prefix_inner_dim			,     self.prefix_hidden_dim )
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       UpperCamelCase_				:   Tuple					    =							(
           nn.Linear(self.prefix_hidden_dim			,     __lowerCAmelCase ) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       UpperCamelCase_				:   Dict					    =							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_				:   Optional[Any]					    =							GPTaLMHeadModel(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     ):
       UpperCamelCase_				:   List[str]					    =							self.transformer.transformer.wte(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							self.encode_prefix(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							self.decode_prefix(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							torch.cat((prefix_embeds, embedding_text)			,     dim=1 )

       if labels is not None:
             UpperCamelCase_				:   Any					    =							self.get_dummy_token(input_ids.shape[0]			,     input_ids.device )
             UpperCamelCase_				:   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_				:   Tuple					    =							torch.split(__lowerCAmelCase			,     1			,     dim=0 )
       UpperCamelCase_				:   Union[str, Any]					    =							[]
       UpperCamelCase_				:   str					    =							[]
       for feature in features:
             UpperCamelCase_				:   int					    =							self.decode_prefix(feature.to(__lowerCAmelCase ) )  # back to the clip feature
             # Only support beam search for now
             UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							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_				:   int					    =							torch.stack(__lowerCAmelCase )
       UpperCamelCase_				:   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_				:   List[str]					    =							None
       UpperCamelCase_				:   List[Any]					    =							None
       UpperCamelCase_				:   Optional[int]					    =							torch.ones(__lowerCAmelCase			,     device=__lowerCAmelCase			,     dtype=torch.int )
       UpperCamelCase_				:   List[Any]					    =							torch.zeros(__lowerCAmelCase			,     device=__lowerCAmelCase			,     dtype=torch.bool )

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

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

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

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

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














'''simple docstring'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






