

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_UpperCamelCase			      =		logging.getLogger(__name__)

_UpperCamelCase			      =		int(1_0000)  # Hardcoded max length to avoid infinite loop

_UpperCamelCase			      =		{
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_UpperCamelCase			      =		"In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"







def 		_lowercase			(					lowercase__						):
      np.random.seed(args.seed						)
      torch.manual_seed(args.seed						)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed						)







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      if args.temperature > 0.7:
            logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''						)

      __lowerCAmelCase :						Union[str, Any] = tokenizer.encode(lowercase__       ,     add_special_tokens=lowercase__						)
      if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()						):
            logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''						)
      return prompt_text







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      # kwargs = {"language": None, "mask_token_id": None}

      # Set the language
      __lowerCAmelCase :						List[Any] = hasattr(model.config       ,     '''use_lang_emb'''						) and model.config.use_lang_emb
      if hasattr(model.config       ,     '''lang2id'''						) and use_lang_emb:
            __lowerCAmelCase :						Optional[int] = model.config.langaid.keys()
            if args.xlm_language in available_languages:
                  __lowerCAmelCase :						str = args.xlm_language
            else:
                  __lowerCAmelCase :						Any = None
                  while language not in available_languages:
                        __lowerCAmelCase :						Dict = input('''Using XLM. Select language in ''' + str(list(lowercase__						)						) + ''' >>> '''						)

            __lowerCAmelCase :						Optional[int] = model.config.langaid[language]
            # kwargs["language"] = tokenizer.lang2id[language]

      # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
      # XLM masked-language modeling (MLM) models need masked token
      # is_xlm_mlm = "mlm" in args.model_name_or_path
      # if is_xlm_mlm:
      #     kwargs["mask_token_id"] = tokenizer.mask_token_id

      return prompt_text







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Optional[int] = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      __lowerCAmelCase :						str = prefix + prompt_text
      return prompt_text







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						str = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      __lowerCAmelCase :						int = prefix + prompt_text
      return prompt_text


_UpperCamelCase			      =		{
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      if length < 0 and max_sequence_length > 0:
            __lowerCAmelCase :						Union[str, Any] = max_sequence_length
      elif 0 < max_sequence_length < length:
            __lowerCAmelCase :						str = max_sequence_length  # No generation bigger than model size
      elif length < 0:
            __lowerCAmelCase :						List[Any] = MAX_LENGTH  # avoid infinite loop
      return length







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						str = None
      if hasattr(lowercase__       ,     '''hidden_size'''						):
            __lowerCAmelCase :						List[str] = model_config.hidden_size
      elif hasattr(lowercase__       ,     '''n_embed'''						):
            __lowerCAmelCase :						Tuple = model_config.n_embed
      elif hasattr(lowercase__       ,     '''n_embd'''						):
            __lowerCAmelCase :						List[str] = model_config.n_embd

      __lowerCAmelCase :						Dict = None
      if hasattr(lowercase__       ,     '''num_attention_heads'''						):
            __lowerCAmelCase :						Dict = model_config.num_attention_heads
      elif hasattr(lowercase__       ,     '''n_head'''						):
            __lowerCAmelCase :						int = model_config.n_head

      if embedding_size is None or num_head is None or num_head == 0:
            raise ValueError('''Check the model config'''						)

      __lowerCAmelCase :						List[Any] = int(embedding_size / num_head						)
      if hasattr(lowercase__       ,     '''n_layer'''						):
            __lowerCAmelCase :						List[str] = model_config.n_layer
      elif hasattr(lowercase__       ,     '''num_hidden_layers'''						):
            __lowerCAmelCase :						str = model_config.num_hidden_layers
      else:
            raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''						)

      return num_layer, num_head, num_embedding_size_per_head







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = sparse_model_config(model.config						)
      if model.config.model_type == "bloom":
            __lowerCAmelCase :						List[str] = tuple(
                (
                    torch.empty(int(num_attention_heads * batch_size						)       ,     lowercase__       ,     lowercase__						)
                    .to(model.dtype						)
                    .to(model.device						),
                    torch.empty(int(num_attention_heads * batch_size						)       ,     lowercase__       ,     lowercase__						)
                    .to(model.dtype						)
                    .to(model.device						),
                )
                for _ in range(lowercase__						)						)
      else:
            __lowerCAmelCase :						Any = tuple(
                (
                    torch.empty(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
                    .to(model.dtype						)
                    .to(model.device						),
                    torch.empty(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
                    .to(model.dtype						)
                    .to(model.device						),
                )
                for _ in range(lowercase__						)						)
      return past_key_values







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Dict = len(lowercase__						)
      __lowerCAmelCase :						List[Any] = tokenizer.batch_encode_plus(lowercase__       ,     return_tensors='''pt'''						)
      __lowerCAmelCase :						List[str] = dummy_input.to(model.device						)
      if model.config.use_cache:
            __lowerCAmelCase :						Any = generate_past_key_values(lowercase__       ,     lowercase__       ,     1						)
      __lowerCAmelCase :						List[str] = torch.cat(
          [
              torch.zeros(dummy_input['''attention_mask'''].shape[0]       ,     1						)
              .to(dummy_input['''attention_mask'''].dtype						)
              .to(model.device						),
              dummy_input['''attention_mask'''],
          ]       ,     -1       ,     )
      return dummy_input







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  ("""_optimized""", """_default""")
 def __init__(							self				,							A_				,							A_ ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						str = optimized
       __lowerCAmelCase :						Tuple = default
 def __call__(							self				,							*A_				,							**A_ ) ->Dict:



       '''simple docstring'''





       if kwargs["past_key_values"] is None and self._default.config.use_cache:
             __lowerCAmelCase :						Optional[Any] = generate_past_key_values(self._default				,							kwargs['''input_ids'''].shape[0]				,							0 )
       kwargs.pop('''position_ids'''				,							A_ )
       for k in list(kwargs.keys() ):
             if kwargs[k] is None or isinstance(kwargs[k]				,							A_ ):
                   kwargs.pop(A_ )
       __lowerCAmelCase :						int = self._optimized(**A_ )
       __lowerCAmelCase :						Optional[int] = outputs[0]
       __lowerCAmelCase :						int = outputs[1]
       __lowerCAmelCase :						Any = CausalLMOutputWithPast(
           loss=A_				,							logits=A_				,							past_key_values=A_				,							hidden_states=A_				,							attentions=A_				,							)
       return fixed_output
 def __getattr__(							self				,							A_ ) ->Tuple:



       '''simple docstring'''





       return getattr(self._default				,							A_ )
 def      UpperCamelCase__  (							self				,							A_				,							A_=None				,							A_=None				,							A_=None				,							**A_ ) ->Dict:



       '''simple docstring'''





       return self._default.prepare_inputs_for_generation(
           A_				,							past_key_values=A_				,							inputs_embeds=A_				,							use_cache=A_				,							**A_ )





 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Tuple[Tuple[torch.Tensor]]:



       '''simple docstring'''





       return self._default._reorder_cache(A_				,							A_ )







def 		_lowercase			(					):
      __lowerCAmelCase :						Any = argparse.ArgumentParser()
      parser.add_argument(
          '''--model_type'''       ,     default=lowercase__       ,     type=lowercase__       ,     required=lowercase__       ,     help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()						)       ,     )
      parser.add_argument(
          '''--model_name_or_path'''       ,     default=lowercase__       ,     type=lowercase__       ,     required=lowercase__       ,     help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()						)       ,     )

      parser.add_argument('''--prompt'''       ,     type=lowercase__       ,     default=''''''						)
      parser.add_argument('''--length'''       ,     type=lowercase__       ,     default=2_0						)
      parser.add_argument('''--stop_token'''       ,     type=lowercase__       ,     default=lowercase__       ,     help='''Token at which text generation is stopped'''						)

      parser.add_argument(
          '''--temperature'''       ,     type=lowercase__       ,     default=1.0       ,     help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''       ,     )
      parser.add_argument(
          '''--repetition_penalty'''       ,     type=lowercase__       ,     default=1.0       ,     help='''primarily useful for CTRL model; in that case, use 1.2'''						)
      parser.add_argument('''--k'''       ,     type=lowercase__       ,     default=0						)
      parser.add_argument('''--p'''       ,     type=lowercase__       ,     default=0.9						)

      parser.add_argument('''--prefix'''       ,     type=lowercase__       ,     default=''''''       ,     help='''Text added prior to input.'''						)
      parser.add_argument('''--padding_text'''       ,     type=lowercase__       ,     default=''''''       ,     help='''Deprecated, the use of `--prefix` is preferred.'''						)
      parser.add_argument('''--xlm_language'''       ,     type=lowercase__       ,     default=''''''       ,     help='''Optional language when used with the XLM model.'''						)

      parser.add_argument('''--seed'''       ,     type=lowercase__       ,     default=4_2       ,     help='''random seed for initialization'''						)
      parser.add_argument('''--no_cuda'''       ,     action='''store_true'''       ,     help='''Avoid using CUDA when available'''						)
      parser.add_argument('''--num_return_sequences'''       ,     type=lowercase__       ,     default=1       ,     help='''The number of samples to generate.'''						)
      parser.add_argument(
          '''--fp16'''       ,     action='''store_true'''       ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''       ,     )
      parser.add_argument('''--jit'''       ,     action='''store_true'''       ,     help='''Whether or not to use jit trace to accelerate inference'''						)
      __lowerCAmelCase :						List[Any] = parser.parse_args()

      __lowerCAmelCase :						int = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
      __lowerCAmelCase :						str = 0 if args.no_cuda else torch.cuda.device_count()

      logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""						)

      set_seed(lowercase__						)

      # Initialize the model and tokenizer
      try:
            __lowerCAmelCase :						int = args.model_type.lower()
            __lowerCAmelCase,							__lowerCAmelCase :						Any = MODEL_CLASSES[args.model_type]
      except KeyError:
            raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''						)

      __lowerCAmelCase :						List[str] = tokenizer_class.from_pretrained(args.model_name_or_path						)
      if tokenizer.pad_token is None:
            __lowerCAmelCase :						List[Any] = tokenizer.eos_token
      __lowerCAmelCase :						Union[str, Any] = model_class.from_pretrained(args.model_name_or_path						)
      model.to(args.device						)

      if args.fpaa:
            model.half()
      __lowerCAmelCase :						Dict = getattr(model.config       ,     '''max_position_embeddings'''       ,     0						)
      __lowerCAmelCase :						List[Any] = adjust_length_to_model(args.length       ,     max_sequence_length=lowercase__						)
      logger.info(lowercase__						)

      __lowerCAmelCase :						Optional[int] = args.prompt if args.prompt else input('''Model prompt >>> '''						)

      # Different models need different input formatting and/or extra arguments
      __lowerCAmelCase :						Dict = args.model_type in PREPROCESSING_FUNCTIONS.keys()
      if requires_preprocessing:
            __lowerCAmelCase :						Dict = PREPROCESSING_FUNCTIONS.get(args.model_type						)
            __lowerCAmelCase :						Dict = prepare_input(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)

            if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                  __lowerCAmelCase :						Optional[int] = {'''add_space_before_punct_symbol''': True}
            else:
                  __lowerCAmelCase :						Dict = {}

            __lowerCAmelCase :						Tuple = tokenizer.encode(
                lowercase__       ,     add_special_tokens=lowercase__       ,     return_tensors='''pt'''       ,     **lowercase__						)
      else:
            __lowerCAmelCase :						str = args.prefix if args.prefix else args.padding_text
            __lowerCAmelCase :						Optional[Any] = tokenizer.encode(prefix + prompt_text       ,     add_special_tokens=lowercase__       ,     return_tensors='''pt'''						)
      __lowerCAmelCase :						str = encoded_prompt.to(args.device						)

      if encoded_prompt.size()[-1] == 0:
            __lowerCAmelCase :						List[str] = None
      else:
            __lowerCAmelCase :						str = encoded_prompt

      if args.jit:
            __lowerCAmelCase :						Dict = ['''enable jit''']
            __lowerCAmelCase :						Dict = prepare_jit_inputs(lowercase__       ,     lowercase__       ,     lowercase__						)
            torch._C._jit_set_texpr_fuser_enabled(lowercase__						)
            __lowerCAmelCase :						str = False
            if hasattr(lowercase__       ,     '''forward'''						):
                  __lowerCAmelCase :						str = inspect.signature(model.forward						)
            else:
                  __lowerCAmelCase :						List[str] = inspect.signature(model.__call__						)
            __lowerCAmelCase :						str = tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowercase__       ,     lowercase__						) is not None						)
            __lowerCAmelCase :						Union[str, Any] = torch.jit.trace(lowercase__       ,     lowercase__       ,     strict=lowercase__						)
            __lowerCAmelCase :						Union[str, Any] = torch.jit.freeze(traced_model.eval()						)
            traced_model(*lowercase__						)
            traced_model(*lowercase__						)

            __lowerCAmelCase :						int = _ModelFallbackWrapper(lowercase__       ,     lowercase__						)

      __lowerCAmelCase :						Optional[Any] = model.generate(
          input_ids=lowercase__       ,     max_length=args.length + len(encoded_prompt[0]						)       ,     temperature=args.temperature       ,     top_k=args.k       ,     top_p=args.p       ,     repetition_penalty=args.repetition_penalty       ,     do_sample=lowercase__       ,     num_return_sequences=args.num_return_sequences       ,     )

      # Remove the batch dimension when returning multiple sequences
      if len(output_sequences.shape						) > 2:
            output_sequences.squeeze_()

      __lowerCAmelCase :						List[Any] = []

      for generated_sequence_idx, generated_sequence in enumerate(lowercase__						):
            print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
            __lowerCAmelCase :						int = generated_sequence.tolist()

            # Decode text
            __lowerCAmelCase :						List[str] = tokenizer.decode(lowercase__       ,     clean_up_tokenization_spaces=lowercase__						)

            # Remove all text after the stop token
            __lowerCAmelCase :						Tuple = text[: text.find(args.stop_token						) if args.stop_token else None]

            # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
            __lowerCAmelCase :						Optional[int] = (
                prompt_text + text[len(tokenizer.decode(encoded_prompt[0]       ,     clean_up_tokenization_spaces=lowercase__						)						) :]
            )

            generated_sequences.append(lowercase__						)
            print(lowercase__						)

      return generated_sequences


if __name__ == "__main__":
 main()





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


_UpperCamelCase			      =		logging.get_logger(__name__)

_UpperCamelCase			      =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_UpperCamelCase			      =		{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
}

_UpperCamelCase			      =		{
    "Salesforce/codegen-350M-mono": 2048,
}







@lru_cache()
def 		_lowercase			(					):
      __lowerCAmelCase :						Union[str, Any] = (
          list(range(ord('''!'''						)       ,     ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						)       ,     ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						)       ,     ord('''ÿ'''						) + 1						)						)
      )
      __lowerCAmelCase :						Optional[int] = bs[:]
      __lowerCAmelCase :						Union[str, Any] = 0
      for b in range(2**8						):
            if b not in bs:
                  bs.append(lowercase__						)
                  cs.append(2**8 + n						)
                  n += 1
      __lowerCAmelCase :						Optional[int] = [chr(lowercase__						) for n in cs]
      return dict(zip(lowercase__       ,     lowercase__						)						)







def 		_lowercase			(					lowercase__						):
      __lowerCAmelCase :						List[Any] = set()
      __lowerCAmelCase :						int = word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            __lowerCAmelCase :						int = char
      return pairs







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  VOCAB_FILES_NAMES
 _UpperCamelCase        =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase        =  ["""input_ids""", """attention_mask"""]
 def __init__(							self				,							A_				,							A_				,							A_="replace"				,							A_="<|endoftext|>"				,							A_="<|endoftext|>"				,							A_="<|endoftext|>"				,							A_=None				,							A_=False				,							A_=False				,							**A_				,							) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else bos_token
       __lowerCAmelCase :						Tuple = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else eos_token
       __lowerCAmelCase :						Any = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else unk_token
       __lowerCAmelCase :						List[Any] = AddedToken(A_				,							lstrip=A_				,							rstrip=A_ ) if isinstance(A_				,							A_ ) else pad_token
       super().__init__(
           errors=A_				,							unk_token=A_				,							bos_token=A_				,							eos_token=A_				,							pad_token=A_				,							add_prefix_space=A_				,							add_bos_token=A_				,							**A_				,							)
       __lowerCAmelCase :						List[str] = add_bos_token

       with open(A_				,							encoding='''utf-8''' ) as vocab_handle:
             __lowerCAmelCase :						Optional[int] = json.load(A_ )
       __lowerCAmelCase :						str = {v: k for k, v in self.encoder.items()}
       __lowerCAmelCase :						Optional[Any] = errors  # how to handle errors in decoding
       __lowerCAmelCase :						Optional[int] = bytes_to_unicode()
       __lowerCAmelCase :						Optional[Any] = {v: k for k, v in self.byte_encoder.items()}
       with open(A_				,							encoding='''utf-8''' ) as merges_handle:
             __lowerCAmelCase :						Optional[int] = merges_handle.read().split('''\n''' )[1:-1]
       __lowerCAmelCase :						List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
       __lowerCAmelCase :						List[Any] = dict(zip(A_				,							range(len(A_ ) ) ) )
       __lowerCAmelCase :						Any = {}
       __lowerCAmelCase :						Optional[int] = add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       __lowerCAmelCase :						Optional[Any] = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
 @property
 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       return len(self.encoder )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       return dict(self.encoder				,							**self.added_tokens_encoder )
 def      UpperCamelCase__  (							self				,							A_ ) ->Tuple:



       '''simple docstring'''





       if token in self.cache:
             return self.cache[token]
       __lowerCAmelCase :						Tuple = tuple(A_ )
       __lowerCAmelCase :						str = get_pairs(A_ )

       if not pairs:
             return token

       while True:
             __lowerCAmelCase :						int = min(A_				,							key=lambda A_ : self.bpe_ranks.get(A_				,							float('''inf''' ) ) )
             if bigram not in self.bpe_ranks:
                   break
             __lowerCAmelCase,							__lowerCAmelCase :						Dict = bigram
             __lowerCAmelCase :						Union[str, Any] = []
             __lowerCAmelCase :						Union[str, Any] = 0
             while i < len(A_ ):
                   try:
                         __lowerCAmelCase :						List[str] = word.index(A_				,							A_ )
                   except ValueError:
                         new_word.extend(word[i:] )
                         break
                   else:
                         new_word.extend(word[i:j] )
                         __lowerCAmelCase :						Optional[int] = j

                   if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
                         new_word.append(first + second )
                         i += 2
                   else:
                         new_word.append(word[i] )
                         i += 1
             __lowerCAmelCase :						Optional[int] = tuple(A_ )
             __lowerCAmelCase :						List[Any] = new_word
             if len(A_ ) == 1:
                   break
             else:
                   __lowerCAmelCase :						Optional[int] = get_pairs(A_ )
       __lowerCAmelCase :						List[str] = ''' '''.join(A_ )
       __lowerCAmelCase :						Any = word
       return word
 def      UpperCamelCase__  (							self				,							A_				,							A_=None ) ->Tuple:



       '''simple docstring'''





       if self.add_bos_token:
             __lowerCAmelCase :						Any = [self.bos_token_id]
       else:
             __lowerCAmelCase :						Union[str, Any] = []

       __lowerCAmelCase :						Union[str, Any] = bos_token_ids + token_ids_a

       if token_ids_a is None:
             return output

       return output + bos_token_ids + token_ids_a
 def      UpperCamelCase__  (							self				,							A_ ) ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase :						str = []
       for token in re.findall(self.pat				,							A_ ):
             __lowerCAmelCase :						Any = ''''''.join(
                 self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_ ).split(''' ''' ) )
       return bpe_tokens
 def      UpperCamelCase__  (							self				,							A_ ) ->Dict:



       '''simple docstring'''





       return self.encoder.get(A_				,							self.encoder.get(self.unk_token ) )
 def      UpperCamelCase__  (							self				,							A_ ) ->Optional[int]:



       '''simple docstring'''





       return self.decoder.get(A_ )
 def      UpperCamelCase__  (							self				,							A_ ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = ''''''.join(A_ )
       __lowerCAmelCase :						Optional[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''				,							errors=self.errors )
       return text
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None ) ->Tuple[str]:



       '''simple docstring'''





       if not os.path.isdir(A_ ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
             return
       __lowerCAmelCase :						List[str] = os.path.join(
           A_				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       __lowerCAmelCase :						Tuple = os.path.join(
           A_				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

       with open(A_				,							'''w'''				,							encoding='''utf-8''' ) as f:
             f.write(json.dumps(self.encoder				,							indent=2				,							sort_keys=A_				,							ensure_ascii=A_ ) + '''\n''' )

       __lowerCAmelCase :						Optional[int] = 0
       with open(A_				,							'''w'''				,							encoding='''utf-8''' ) as writer:
             writer.write('''#version: 0.2\n''' )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,							key=lambda A_ : kv[1] ):
                   if index != token_index:
                         logger.warning(
                             f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                             ''' Please check that the tokenizer is not corrupted!''' )
                         __lowerCAmelCase :						List[Any] = token_index
                   writer.write(''' '''.join(A_ ) + '''\n''' )
                   index += 1

       return vocab_file, merge_file
 def      UpperCamelCase__  (							self				,							A_				,							A_=False				,							**A_ ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = kwargs.pop('''add_prefix_space'''				,							self.add_prefix_space )
       if is_split_into_words or add_prefix_space:
             __lowerCAmelCase :						List[str] = ''' ''' + text
       return (text, kwargs)
 def      UpperCamelCase__  (							self				,							A_				,							A_ = False				,							A_ = None				,							A_ = None				,							**A_				,							) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = super()._decode(
           token_ids=A_				,							skip_special_tokens=A_				,							clean_up_tokenization_spaces=A_				,							**A_				,							)

       if truncate_before_pattern is not None and len(A_ ) > 0:
             __lowerCAmelCase :						str = self.truncate(A_				,							A_ )

       return decoded_text





 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->int:



       '''simple docstring'''





       def find_re(A_				,							A_				,							A_ ):
             __lowerCAmelCase :						Dict = pattern.search(A_				,							A_ )
             return m.start() if m else -1

       __lowerCAmelCase :						int = [re.compile(A_				,							re.MULTILINE ) for pattern in truncate_before_pattern]

       __lowerCAmelCase :						Tuple = list(re.finditer('''^print'''				,							A_				,							re.MULTILINE ) )

       if len(A_ ) > 1:
             __lowerCAmelCase :						int = completion[: prints[1].start()]

       __lowerCAmelCase :						Optional[Any] = list(re.finditer('''^def'''				,							A_				,							re.MULTILINE ) )

       if len(A_ ) > 1:
             __lowerCAmelCase :						List[str] = completion[: defs[1].start()]

       __lowerCAmelCase :						Optional[Any] = 0

       __lowerCAmelCase :						Optional[int] = [
           pos for pos in [find_re(A_				,							A_				,							A_ ) for terminal in terminals] if pos != -1
       ]

       if len(A_ ) > 0:
             return completion[: min(A_ )]
       else:
             return completion


