




'''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    __lowercase						(  __magic_name__       ,							__magic_name__       ,							__magic_name__ ):
   _a							=      [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]







   @register_to_config
   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = 5_0257      ,							UpperCamelCase = 1024      ,							UpperCamelCase = 768      ,							UpperCamelCase = 12      ,							UpperCamelCase = 12      ,							UpperCamelCase = None      ,							UpperCamelCase = "gelu_new"      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 1e-5      ,							UpperCamelCase = 0.02      ,							UpperCamelCase = True      ,							UpperCamelCase = True      ,							UpperCamelCase = False      ,							UpperCamelCase = 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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							)				->    List[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      ,							UpperCamelCase      ,							UpperCamelCase      )				->    torch.Tensor:
     return torch.zeros(UpperCamelCase      ,							self.prefix_length      ,							dtype=torch.intaa      ,							device=UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     return self.encode_prefix(UpperCamelCase      )







   @torch.no_grad()
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Union[str, Any]:
     __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      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = 5      ,							UpperCamelCase = 67      ,							UpperCamelCase = 1.0      ,							UpperCamelCase = 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'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase_ 		=      {"mobilebert-uncased": 5_12}


UpperCAmelCase_ 		=      {}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      MobileBertTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    List[Any]:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         normalizer_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or normalizer_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'      ,							UpperCamelCase      ) != tokenize_chinese_chars
     ):
       __a			=					getattr(UpperCamelCase      ,							normalizer_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					tokenize_chinese_chars
       __a			=					normalizer_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )



