



"""simple docstring"""
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_0_0_0_0)  # 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        __magic_name__			(							lowercase	):
   np.random.seed(args.seed	)
   torch.manual_seed(args.seed	)
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):






   if args.temperature > 0.7:
      logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""	)

   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tokenizer.encode(snake_case__							,     add_special_tokens=snake_case__	)
   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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =hasattr(model.config							,     """use_lang_emb"""	) and model.config.use_lang_emb
   if hasattr(model.config							,     """lang2id"""	) and use_lang_emb:
      SCREAMING_SNAKE_CASE_:	Tuple            =model.config.langaid.keys()
      if args.xlm_language in available_languages:
         SCREAMING_SNAKE_CASE_:	List[str]            =args.xlm_language
      else:
         SCREAMING_SNAKE_CASE_:	Dict            =None
         while language not in available_languages:
            SCREAMING_SNAKE_CASE_:	List[Any]            =input("""Using XLM. Select language in """ + str(list(snake_case__	)	) + """ >>> """	)

      SCREAMING_SNAKE_CASE_:	Dict            =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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	str            =args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
   SCREAMING_SNAKE_CASE_:	Dict            =prefix + prompt_text
   return prompt_text





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Tuple            =args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
   SCREAMING_SNAKE_CASE_:	Optional[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        __magic_name__			(							lowercase							,     lowercase	):






   if length < 0 and max_sequence_length > 0:
      SCREAMING_SNAKE_CASE_:	Any            =max_sequence_length
   elif 0 < max_sequence_length < length:
      SCREAMING_SNAKE_CASE_:	List[str]            =max_sequence_length  # No generation bigger than model size
   elif length < 0:
      SCREAMING_SNAKE_CASE_:	List[Any]            =MAX_LENGTH  # avoid infinite loop
   return length





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	str            =None
   if hasattr(snake_case__							,     """hidden_size"""	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_config.hidden_size
   elif hasattr(snake_case__							,     """n_embed"""	):
      SCREAMING_SNAKE_CASE_:	Optional[int]            =model_config.n_embed
   elif hasattr(snake_case__							,     """n_embd"""	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_config.n_embd

   SCREAMING_SNAKE_CASE_:	Any            =None
   if hasattr(snake_case__							,     """num_attention_heads"""	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_config.num_attention_heads
   elif hasattr(snake_case__							,     """n_head"""	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_config.n_head

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

   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =int(embedding_size / num_head	)
   if hasattr(snake_case__							,     """n_layer"""	):
      SCREAMING_SNAKE_CASE_:	Dict            =model_config.n_layer
   elif hasattr(snake_case__							,     """num_hidden_layers"""	):
      SCREAMING_SNAKE_CASE_:	Optional[int]            =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        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Any            =sparse_model_config(model.config	)
   if model.config.model_type == "bloom":
      SCREAMING_SNAKE_CASE_:	Optional[int]            =tuple(
          (
              torch.empty(int(num_attention_heads * batch_size	)							,     snake_case__							,     snake_case__	)
              .to(model.dtype	)
              .to(model.device	),
              torch.empty(int(num_attention_heads * batch_size	)							,     snake_case__							,     snake_case__	)
              .to(model.dtype	)
              .to(model.device	),
          )
          for _ in range(snake_case__	)	)
   else:
      SCREAMING_SNAKE_CASE_:	Tuple            =tuple(
          (
              torch.empty(snake_case__							,     snake_case__							,     snake_case__							,     snake_case__	)
              .to(model.dtype	)
              .to(model.device	),
              torch.empty(snake_case__							,     snake_case__							,     snake_case__							,     snake_case__	)
              .to(model.dtype	)
              .to(model.device	),
          )
          for _ in range(snake_case__	)	)
   return past_key_values





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(snake_case__	)
   SCREAMING_SNAKE_CASE_:	str            =tokenizer.batch_encode_plus(snake_case__							,     return_tensors="""pt"""	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =dummy_input.to(model.device	)
   if model.config.use_cache:
      SCREAMING_SNAKE_CASE_:	Any            =generate_past_key_values(snake_case__							,     snake_case__							,     1	)
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =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    a		(					_a    ):
  UpperCamelCase		:			Union[str, Any]         =   ('_optimized', '_default')



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   List[str]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =optimized
     SCREAMING_SNAKE_CASE_:	Dict            =default



  def __call__(  self	:   List[Any]    ,						*lowerCAmelCase	:   Optional[Any]    ,						**lowerCAmelCase	:   str						)       ->   Dict:


     '''simple docstring'''




     if kwargs["past_key_values"] is None and self._default.config.use_cache:
        SCREAMING_SNAKE_CASE_:	Any            =generate_past_key_values(self._default    ,						kwargs["""input_ids"""].shape[0]    ,						0						)
     kwargs.pop("""position_ids"""    ,						lowerCAmelCase						)
     for k in list(kwargs.keys()						):
        if kwargs[k] is None or isinstance(kwargs[k]    ,						lowerCAmelCase						):
           kwargs.pop(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =self._optimized(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =outputs[0]
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =outputs[1]
     SCREAMING_SNAKE_CASE_:	str            =CausalLMOutputWithPast(
         loss=lowerCAmelCase    ,						logits=lowerCAmelCase    ,						past_key_values=lowerCAmelCase    ,						hidden_states=lowerCAmelCase    ,						attentions=lowerCAmelCase    ,						)
     return fixed_output



  def __getattr__(  self	:   Optional[int]    ,						lowerCAmelCase	:   Tuple						)       ->   List[Any]:


     '''simple docstring'''




     return getattr(self._default    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   Any    ,						lowerCAmelCase	:   List[str]=None    ,						lowerCAmelCase	:   Dict=None    ,						lowerCAmelCase	:   Optional[int]=None    ,						**lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




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





  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Tuple[Tuple[torch.Tensor]]    ,						lowerCAmelCase	:   torch.Tensor						)       ->   Tuple:


     '''simple docstring'''




     return self._default._reorder_cache(lowerCAmelCase    ,						lowerCAmelCase						)







def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	List[Any]            =argparse.ArgumentParser()
   parser.add_argument(
       """--model_type"""							,     default=snake_case__							,     type=snake_case__							,     required=snake_case__							,     help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()	)							,     )
   parser.add_argument(
       """--model_name_or_path"""							,     default=snake_case__							,     type=snake_case__							,     required=snake_case__							,     help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()	)							,     )

   parser.add_argument("""--prompt"""							,     type=snake_case__							,     default=""""""	)
   parser.add_argument("""--length"""							,     type=snake_case__							,     default=20	)
   parser.add_argument("""--stop_token"""							,     type=snake_case__							,     default=snake_case__							,     help="""Token at which text generation is stopped"""	)

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

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

   parser.add_argument("""--seed"""							,     type=snake_case__							,     default=42							,     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=snake_case__							,     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"""	)
   SCREAMING_SNAKE_CASE_:	Dict            =parser.parse_args()

   SCREAMING_SNAKE_CASE_:	Tuple            =torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
   SCREAMING_SNAKE_CASE_:	Tuple            =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(snake_case__	)

   # Initialize the model and tokenizer
   try:
      SCREAMING_SNAKE_CASE_:	str            =args.model_type.lower()
      SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =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 :)"""	)

   SCREAMING_SNAKE_CASE_:	Any            =tokenizer_class.from_pretrained(args.model_name_or_path	)
   if tokenizer.pad_token is None:
      SCREAMING_SNAKE_CASE_:	Optional[int]            =tokenizer.eos_token
   SCREAMING_SNAKE_CASE_:	Any            =model_class.from_pretrained(args.model_name_or_path	)
   model.to(args.device	)

   if args.fpaa:
      model.half()
   SCREAMING_SNAKE_CASE_:	int            =getattr(model.config							,     """max_position_embeddings"""							,     0	)
   SCREAMING_SNAKE_CASE_:	List[str]            =adjust_length_to_model(args.length							,     max_sequence_length=snake_case__	)
   logger.info(snake_case__	)

   SCREAMING_SNAKE_CASE_:	str            =args.prompt if args.prompt else input("""Model prompt >>> """	)

   # Different models need different input formatting and/or extra arguments
   SCREAMING_SNAKE_CASE_:	Optional[int]            =args.model_type in PREPROCESSING_FUNCTIONS.keys()
   if requires_preprocessing:
      SCREAMING_SNAKE_CASE_:	List[Any]            =PREPROCESSING_FUNCTIONS.get(args.model_type	)
      SCREAMING_SNAKE_CASE_:	List[str]            =prepare_input(snake_case__							,     snake_case__							,     snake_case__							,     snake_case__	)

      if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
         SCREAMING_SNAKE_CASE_:	Tuple            ={"""add_space_before_punct_symbol""": True}
      else:
         SCREAMING_SNAKE_CASE_:	Dict            ={}

      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tokenizer.encode(
          snake_case__							,     add_special_tokens=snake_case__							,     return_tensors="""pt"""							,     **snake_case__	)
   else:
      SCREAMING_SNAKE_CASE_:	List[Any]            =args.prefix if args.prefix else args.padding_text
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tokenizer.encode(prefix + prompt_text							,     add_special_tokens=snake_case__							,     return_tensors="""pt"""	)
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =encoded_prompt.to(args.device	)

   if encoded_prompt.size()[-1] == 0:
      SCREAMING_SNAKE_CASE_:	Optional[int]            =None
   else:
      SCREAMING_SNAKE_CASE_:	List[Any]            =encoded_prompt

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

      SCREAMING_SNAKE_CASE_:	str            =_ModelFallbackWrapper(snake_case__							,     snake_case__	)

   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model.generate(
       input_ids=snake_case__							,     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=snake_case__							,     num_return_sequences=args.num_return_sequences							,     )

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

   SCREAMING_SNAKE_CASE_:	Optional[int]            =[]

   for generated_sequence_idx, generated_sequence in enumerate(snake_case__	):
      print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''	)
      SCREAMING_SNAKE_CASE_:	str            =generated_sequence.tolist()

      # Decode text
      SCREAMING_SNAKE_CASE_:	Any            =tokenizer.decode(snake_case__							,     clean_up_tokenization_spaces=snake_case__	)

      # Remove all text after the stop token
      SCREAMING_SNAKE_CASE_:	str            =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
      SCREAMING_SNAKE_CASE_:	str            =(
          prompt_text + text[len(tokenizer.decode(encoded_prompt[0]							,     clean_up_tokenization_spaces=snake_case__	)	) :]
      )

      generated_sequences.append(snake_case__	)
      print(snake_case__	)

   return generated_sequences


if __name__ == "__main__":
   main()








"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCAmelCase       =     {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

_UpperCAmelCase       =     {
    """RUCAIBox/mvp""": 1_0_2_4,
}





@lru_cache()
def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
       list(range(ord("""!"""	)							,     ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)							,     ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)							,     ord("""ÿ"""	) + 1	)	)
   )
   SCREAMING_SNAKE_CASE_:	Any            =bs[:]
   SCREAMING_SNAKE_CASE_:	str            =0
   for b in range(2**8	):
      if b not in bs:
         bs.append(lowercase	)
         cs.append(2**8 + n	)
         n += 1
   SCREAMING_SNAKE_CASE_:	List[Any]            =[chr(lowercase	) for n in cs]
   return dict(zip(lowercase							,     lowercase	)	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =set()
   SCREAMING_SNAKE_CASE_:	Tuple            =word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      SCREAMING_SNAKE_CASE_:	Dict            =char
   return pairs
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			str         =   VOCAB_FILES_NAMES
  UpperCamelCase		:			List[Any]         =   PRETRAINED_VOCAB_FILES_MAP
  UpperCamelCase		:			Optional[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCamelCase		:			Optional[int]         =   ['input_ids', 'attention_mask']



  def __init__(  self	:   Tuple    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   List[str]="replace"    ,						lowerCAmelCase	:   List[Any]="<s>"    ,						lowerCAmelCase	:   str="</s>"    ,						lowerCAmelCase	:   Dict="</s>"    ,						lowerCAmelCase	:   Optional[int]="<s>"    ,						lowerCAmelCase	:   int="<unk>"    ,						lowerCAmelCase	:   Optional[int]="<pad>"    ,						lowerCAmelCase	:   str="<mask>"    ,						lowerCAmelCase	:   Dict=False    ,						**lowerCAmelCase	:   Any    ,						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else bos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else eos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else sep_token
     SCREAMING_SNAKE_CASE_:	Dict            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else cls_token
     SCREAMING_SNAKE_CASE_:	str            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else unk_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE_:	Optional[int]            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else mask_token

     super().__init__(
         errors=lowerCAmelCase    ,						bos_token=lowerCAmelCase    ,						eos_token=lowerCAmelCase    ,						unk_token=lowerCAmelCase    ,						sep_token=lowerCAmelCase    ,						cls_token=lowerCAmelCase    ,						pad_token=lowerCAmelCase    ,						mask_token=lowerCAmelCase    ,						add_prefix_space=lowerCAmelCase    ,						**lowerCAmelCase    ,						)
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as vocab_handle:
        SCREAMING_SNAKE_CASE_:	Any            =json.load(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            ={v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =errors  # how to handle errors in decoding
     SCREAMING_SNAKE_CASE_:	Any            =bytes_to_unicode()
     SCREAMING_SNAKE_CASE_:	str            ={v: k for k, v in self.byte_encoder.items()}
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as merges_handle:
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =merges_handle.read().split("""\n"""						)[1:-1]
     SCREAMING_SNAKE_CASE_:	Dict            =[tuple(merge.split()						) for merge in bpe_merges]
     SCREAMING_SNAKE_CASE_:	Any            =dict(zip(lowerCAmelCase    ,						range(len(lowerCAmelCase						)						)						)						)
     SCREAMING_SNAKE_CASE_:	int            ={}
     SCREAMING_SNAKE_CASE_:	Optional[int]            =add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     SCREAMING_SNAKE_CASE_:	Tuple            =re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)



  @property
  def 		lowerCamelCase__    (  self	:   int						)       ->   List[Any]:


     '''simple docstring'''




     return len(self.encoder						)



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Optional[Any]:


     '''simple docstring'''




     return dict(self.encoder    ,						**self.added_tokens_encoder						)



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   Union[str, Any]						)       ->   Any:


     '''simple docstring'''




     if token in self.cache:
        return self.cache[token]
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =tuple(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =get_pairs(lowerCAmelCase						)

     if not pairs:
        return token

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

           if word[i] == first and i < len(lowerCAmelCase						) - 1 and word[i + 1] == second:
              new_word.append(first + second						)
              i += 2
           else:
              new_word.append(word[i]						)
              i += 1
        SCREAMING_SNAKE_CASE_:	Any            =tuple(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	str            =new_word
        if len(lowerCAmelCase						) == 1:
           break
        else:
           SCREAMING_SNAKE_CASE_:	List[str]            =get_pairs(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =""" """.join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =word
     return word



  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =[]
     for token in re.findall(self.pat    ,						lowerCAmelCase						):
        SCREAMING_SNAKE_CASE_:	Union[str, 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(lowerCAmelCase						).split(""" """						)						)
     return bpe_tokens



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   str						)       ->   str:


     '''simple docstring'''




     return self.encoder.get(lowerCAmelCase    ,						self.encoder.get(self.unk_token						)						)



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   Optional[Any]						)       ->   Optional[int]:


     '''simple docstring'''




     return self.decoder.get(lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   Optional[Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            ="""""".join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""    ,						errors=self.errors						)
     return text



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Optional[str] = None						)       ->   Tuple[str]:


     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0
     with open(lowerCAmelCase    ,						"""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 lowerCAmelCase						: 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!"""						)
              SCREAMING_SNAKE_CASE_:	Union[str, Any]            =token_index
           writer.write(""" """.join(lowerCAmelCase						) + """\n"""						)
           index += 1

     return vocab_file, merge_file



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.cls_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None    ,						lowerCAmelCase	:   bool = False						)       ->   List[int]:


     '''simple docstring'''




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

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



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =[self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	str            =[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 + sep + token_ids_a + sep						) * [0]





  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Union[str, Any]=False    ,						**lowerCAmelCase	:   str						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =kwargs.pop("""add_prefix_space"""    ,						self.add_prefix_space						)
     if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase						) > 0 and not text[0].isspace()):
        SCREAMING_SNAKE_CASE_:	List[Any]            =""" """ + text
     return (text, kwargs)



