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

import regex as re

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


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

_A  					= logging.get_logger(__name__)

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

_A  					= {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

_A  					= {
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}



@lru_cache()
def 						lowercase_			(   )							->							Union[str, Any]:
    """simple docstring"""

    snake_case      =					(
        list(range(ord("!"		) ,		ord("~"		) + 1		)		) + list(range(ord("¡"		) ,		ord("¬"		) + 1		)		) + list(range(ord("®"		) ,		ord("ÿ"		) + 1		)		)
    )
    snake_case      =					bs[:]
    snake_case      =					0
    for b in range(2**8		):
        if b not in bs:
            bs.append(A__		)
            cs.append(2**8 + n		)
            n += 1
    snake_case      =					[chr(A__		) for n in cs]
    return dict(zip(A__ ,		A__		)		)



def 						lowercase_			(   A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case      =					set()
    snake_case      =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        snake_case      =					char
    return pairs







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[str]           =		VOCAB_FILES_NAMES
     UpperCAmelCase__				:							List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__				:							Tuple           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__				:							Union[str, Any]           =		['''input_ids''', '''attention_mask''']





     def __init__(self      :    List[str]				,    _A      :    Any				,    _A      :    Tuple				,    _A      :    List[Any]="replace"				,    _A      :    int="<|endoftext|>"				,    _A      :    int="<|endoftext|>"				,    _A      :    Union[str, Any]="<|endoftext|>"				,    _A      :    int=None				,    _A      :    str=False				,    _A      :    int=False				,    **_A      :    Optional[int]				,    )						->						Dict:
         snake_case      =					AddedToken(UpperCAmelCase_				,    lstrip=UpperCAmelCase_				,    rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,    UpperCAmelCase_   ) else bos_token
         snake_case      =					AddedToken(UpperCAmelCase_				,    lstrip=UpperCAmelCase_				,    rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,    UpperCAmelCase_   ) else eos_token
         snake_case      =					AddedToken(UpperCAmelCase_				,    lstrip=UpperCAmelCase_				,    rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,    UpperCAmelCase_   ) else unk_token
         snake_case      =					AddedToken(UpperCAmelCase_				,    lstrip=UpperCAmelCase_				,    rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,    UpperCAmelCase_   ) else pad_token
         super().__init__(
             errors=UpperCAmelCase_				,    unk_token=UpperCAmelCase_				,    bos_token=UpperCAmelCase_				,    eos_token=UpperCAmelCase_				,    pad_token=UpperCAmelCase_				,    add_prefix_space=UpperCAmelCase_				,    add_bos_token=UpperCAmelCase_				,    **UpperCAmelCase_				,    )
         snake_case      =					add_bos_token

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

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case      =					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      :    str   )						->						Any:
         return len(self.encoder   )





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						Optional[Any]:
         return dict(self.encoder				,    **self.added_tokens_encoder   )





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    Any   )						->						Tuple:
         if token in self.cache:
             return self.cache[token]
         snake_case      =					tuple(UpperCAmelCase_   )
         snake_case      =					get_pairs(UpperCAmelCase_   )

         if not pairs:
             return token

         while True:
             snake_case      =					min(UpperCAmelCase_				,    key=lambda _A   : self.bpe_ranks.get(UpperCAmelCase_				,    float("inf"   )   )   )
             if bigram not in self.bpe_ranks:
                 break
             snake_case      =					bigram
             snake_case      =					[]
             snake_case      =					0
             while i < len(UpperCAmelCase_   ):
                 try:
                     snake_case      =					word.index(UpperCAmelCase_				,    UpperCAmelCase_   )
                 except ValueError:
                     new_word.extend(word[i:]   )
                     break
                 else:
                     new_word.extend(word[i:j]   )
                     snake_case      =					j

                 if word[i] == first and i < len(UpperCAmelCase_   ) - 1 and word[i + 1] == second:
                     new_word.append(first + second   )
                     i += 2
                 else:
                     new_word.append(word[i]   )
                     i += 1
             snake_case      =					tuple(UpperCAmelCase_   )
             snake_case      =					new_word
             if len(UpperCAmelCase_   ) == 1:
                 break
             else:
                 snake_case      =					get_pairs(UpperCAmelCase_   )
         snake_case      =					" ".join(UpperCAmelCase_   )
         snake_case      =					word
         return word





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    Optional[int]				,    _A      :    Tuple=None   )						->						List[str]:
         if self.add_bos_token:
             snake_case      =					[self.bos_token_id]
         else:
             snake_case      =					[]

         snake_case      =					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      :    Dict				,    _A      :    List[int]				,    _A      :    Optional[List[int]] = None				,    _A      :    bool = False   )						->						int:
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=UpperCAmelCase_				,    token_ids_a=UpperCAmelCase_				,    already_has_special_tokens=UpperCAmelCase_   )

         if not self.add_bos_token:
             return super().get_special_tokens_mask(
                 token_ids_a=UpperCAmelCase_				,    token_ids_a=UpperCAmelCase_				,    already_has_special_tokens=UpperCAmelCase_   )

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





     def 		UpperCAmelCase(self      :    List[str]				,    _A      :    Dict   )						->						str:
         snake_case      =					[]
         for token in re.findall(self.pat				,    UpperCAmelCase_   ):
             snake_case      =					"".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(UpperCAmelCase_   ).split(" "   )   )
         return bpe_tokens





     def 		UpperCAmelCase(self      :    int				,    _A      :    Optional[int]   )						->						Optional[Any]:
         return self.encoder.get(UpperCAmelCase_				,    self.encoder.get(self.unk_token   )   )





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    List[Any]   )						->						Dict:
         return self.decoder.get(UpperCAmelCase_   )





     def 		UpperCAmelCase(self      :    List[str]				,    _A      :    Union[str, Any]   )						->						Optional[Any]:
         snake_case      =					"".join(UpperCAmelCase_   )
         snake_case      =					bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"				,    errors=self.errors   )
         return text





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    str				,    _A      :    Optional[str] = None   )						->						Tuple:
         if not os.path.isdir(UpperCAmelCase_   ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
             return
         snake_case      =					os.path.join(
             UpperCAmelCase_				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
         snake_case      =					os.path.join(
             UpperCAmelCase_				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

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

         snake_case      =					0
         with open(UpperCAmelCase_				,    "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!"   )
                     snake_case      =					token_index
                 writer.write(" ".join(UpperCAmelCase_   ) + "\n"   )
                 index += 1

         return vocab_file, merge_file





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    Optional[Any]				,    _A      :    Dict=False				,    **_A      :    Dict   )						->						Tuple:
         snake_case      =					kwargs.pop("add_prefix_space"				,    self.add_prefix_space   )
         if is_split_into_words or add_prefix_space:
             snake_case      =					" " + text
         return (text, kwargs)





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    "Conversation"   )						->						int:
         snake_case      =					[]
         for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(UpperCAmelCase_				,    add_special_tokens=UpperCAmelCase_   ) + [self.eos_token_id]   )
         if len(UpperCAmelCase_   ) > self.model_max_length:
             snake_case      =					input_ids[-self.model_max_length :]
         return input_ids









import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


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


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






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


def      lowerCamelCase__	(   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[str]		  =			argparse.ArgumentParser()
  parser.add_argument(
      "--model_name_or_path"  ,   default=lowercase  ,   type=lowercase  ,   required=lowercase  ,   )
  parser.add_argument("--prompt"  ,   type=lowercase  ,   default="" )
  parser.add_argument("--length"  ,   type=lowercase  ,   default=20 )
  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("--penalty_alpha"  ,   type=lowercase  ,   default=0.0 )
  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=42  ,   help="random seed for initialization" )
  parser.add_argument("--no_cuda"  ,   action="store_true"  ,   help="Avoid using CUDA when available" )
  parser.add_argument(
      "--fp16"  ,   action="store_true"  ,   help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[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
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device )

  if args.fpaa:
    model.half()

  logger.info(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(
      **lowercase  ,   max_length=args.length + len(inputs["input_ids"][0] )  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[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(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






