


"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						"""▁"""

UpperCAmelCase				=						{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase				=						{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase				=						{
    """facebook/xglm-564M""": 2048,
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					['''input_ids''', '''attention_mask''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE="<s>"      ,   SCREAMING_SNAKE_CASE="</s>"      ,   SCREAMING_SNAKE_CASE="</s>"      ,   SCREAMING_SNAKE_CASE="<s>"      ,   SCREAMING_SNAKE_CASE="<unk>"      ,   SCREAMING_SNAKE_CASE="<pad>"      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  None:
      _lowerCamelCase	:					Tuple   =		{} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      _lowerCamelCase	:					List[str]   =		7
      _lowerCamelCase	:					List[str]   =		[F'<madeupword{i}>' for i in range(self.num_madeup_words)]

      _lowerCamelCase	:					Union[str, Any]   =		kwargs.get("""additional_special_tokens"""      ,   [])
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          bos_token=SCREAMING_SNAKE_CASE      ,   eos_token=SCREAMING_SNAKE_CASE      ,   unk_token=SCREAMING_SNAKE_CASE      ,   sep_token=SCREAMING_SNAKE_CASE      ,   cls_token=SCREAMING_SNAKE_CASE      ,   pad_token=SCREAMING_SNAKE_CASE      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **SCREAMING_SNAKE_CASE      ,   )

      _lowerCamelCase	:					Tuple   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Union[str, Any]   =		vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _lowerCamelCase	:					List[Any]   =		1

      # Mimic fairseq token-to-id alignment for the first 4 token
      _lowerCamelCase	:					List[Any]   =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

      _lowerCamelCase	:					str   =		len(self.sp_model)
      _lowerCamelCase	:					List[Any]   =		{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
      self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}





   def __getstate__(							self)  ->  Optional[Any]:
      _lowerCamelCase	:					str   =		self.__dict__.copy()
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					List[str]   =		self.sp_model.serialized_model_proto()
      return state





   def __setstate__(							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					int   =		d

      # for backward compatibility
      if not hasattr(self      ,   """sp_model_kwargs"""):
         _lowerCamelCase	:					Optional[int]   =		{}

      _lowerCamelCase	:					List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:

      if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
      _lowerCamelCase	:					List[Any]   =		[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False)  ->  List[int]:

      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=SCREAMING_SNAKE_CASE      ,   token_ids_a=SCREAMING_SNAKE_CASE      ,   already_has_special_tokens=SCREAMING_SNAKE_CASE)

      if token_ids_a is None:
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE))
      return [1] + ([0] * len(SCREAMING_SNAKE_CASE)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					Optional[int]   =		[self.sep_token_id]

      if token_ids_a is None:
         return len(sep + token_ids_a) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]





   @property
   def 		UpperCamelCase_  (							self)  ->  List[str]:
      return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Dict   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.sp_model.encode(SCREAMING_SNAKE_CASE      ,   out_type=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _lowerCamelCase	:					List[Any]   =		self.sp_model.PieceToId(SCREAMING_SNAKE_CASE)

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		"""""".join(SCREAMING_SNAKE_CASE).replace(SCREAMING_SNAKE_CASE      ,   """ """).strip()
      return out_string





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
      _lowerCamelCase	:					Tuple   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

      if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
         copyfile(self.vocab_file      ,   SCREAMING_SNAKE_CASE)
      elif not os.path.isfile(self.vocab_file):
         with open(SCREAMING_SNAKE_CASE      ,   """wb""") as fi:
            _lowerCamelCase	:					List[Any]   =		self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE)

      return (out_vocab_file,)







"""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 lowerCAmelCase__      (	lowercase,      lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]
			@register_to_config
			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = 50257    , lowercase = 1024    , lowercase = 768    , lowercase = 12    , lowercase = 12    , lowercase = None    , lowercase = "gelu_new"    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 0.1    , lowercase = 1E-5    , lowercase = 0.02    , lowercase = True    , lowercase = True    , lowercase = False    , lowercase = False    , ):
							super().__init__()

							_lowerCamelCase	:     Tuple       =       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.'''    )

							_lowerCamelCase	:     Optional[Any]       =       prefix_inner_dim
							_lowerCamelCase	:     Tuple       =       prefix_hidden_dim

							_lowerCamelCase	:     Dict       =       (
							    nn.Linear(self.prefix_inner_dim    , self.prefix_hidden_dim    )
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							_lowerCamelCase	:     Tuple       =       (
							    nn.Linear(self.prefix_hidden_dim    , lowercase    ) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							_lowerCamelCase	:     Any       =       GPTaConfig(
							    vocab_size=lowercase    , n_positions=lowercase    , n_embd=lowercase    , n_layer=lowercase    , n_head=lowercase    , n_inner=lowercase    , activation_function=lowercase    , resid_pdrop=lowercase    , embd_pdrop=lowercase    , attn_pdrop=lowercase    , layer_norm_epsilon=lowercase    , initializer_range=lowercase    , scale_attn_weights=lowercase    , use_cache=lowercase    , scale_attn_by_inverse_layer_idx=lowercase    , reorder_and_upcast_attn=lowercase    , )
							_lowerCamelCase	:     Dict       =       GPTaLMHeadModel(lowercase    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    , ):
							_lowerCamelCase	:     List[Any]       =       self.transformer.transformer.wte(lowercase    )
							_lowerCamelCase	:     str       =       self.encode_prefix(lowercase    )
							_lowerCamelCase	:     int       =       self.decode_prefix(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.cat((prefix_embeds, embedding_text)    , dim=1    )

							if labels is not None:
											_lowerCamelCase	:     Any       =       self.get_dummy_token(input_ids.shape[0]    , input_ids.device    )
											_lowerCamelCase	:     int       =       torch.cat((dummy_token, input_ids)    , dim=1    )
							_lowerCamelCase	:     List[Any]       =       self.transformer(inputs_embeds=lowercase    , labels=lowercase    , attention_mask=lowercase    )
							if self.prefix_hidden_dim is not None:
											return out, hidden
							else:
											return out
			def    A_       (    self    , lowercase    , lowercase    ):
							return torch.zeros(lowercase    , self.prefix_length    , dtype=torch.intaa    , device=lowercase    )
			def    A_       (    self    , lowercase    ):
							return self.encode_prefix(lowercase    )
			@torch.no_grad()
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       torch.split(lowercase    , 1    , dim=0    )
							_lowerCamelCase	:     Any       =       []
							_lowerCamelCase	:     str       =       []
							for feature in features:
											_lowerCamelCase	:     Any       =       self.decode_prefix(feature.to(lowercase    )    )  # back to the clip feature
											# Only support beam search for now
											_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self.generate_beam(
											    input_embeds=lowercase    , device=lowercase    , eos_token_id=lowercase    )
											generated_tokens.append(output_tokens[0]    )
											generated_seq_lengths.append(seq_lengths[0]    )
							_lowerCamelCase	:     List[Any]       =       torch.stack(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       torch.stack(lowercase    )
							return generated_tokens, generated_seq_lengths






			@torch.no_grad()
			def    A_       (    self    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase = 5    , lowercase = 67    , lowercase = 1.0    , lowercase = None    , ):
							_lowerCamelCase	:     Any       =       eos_token_id
							_lowerCamelCase	:     List[str]       =       None
							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     List[str]       =       torch.ones(lowercase    , device=lowercase    , dtype=torch.int    )
							_lowerCamelCase	:     Optional[int]       =       torch.zeros(lowercase    , device=lowercase    , dtype=torch.bool    )

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

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

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

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

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