


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

__UpperCamelCase					:	Dict          =				logging.get_logger(__name__)

__UpperCamelCase					:	List[str]          =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase					:	Optional[int]          =				{
    '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',
    },
}

__UpperCamelCase					:	Any          =				{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}
@lru_cache()
def   snake_case_    (			):
   UpperCAmelCase_				:   Union[str, Any]							= (
       list(range(ord('''!'''							)      ,							ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)      ,							ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)      ,							ord('''ÿ'''							) + 1							)							)
   )
   UpperCAmelCase_				:   Optional[int]							= bs[:]
   UpperCAmelCase_				:   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
   UpperCAmelCase_				:   Dict							= [chr(__lowercase							) for n in cs]
   return dict(zip(__lowercase      ,							__lowercase							)							)
def   snake_case_    (			__lowercase							):
   UpperCAmelCase_				:   List[str]							= set()
   UpperCAmelCase_				:   Optional[Any]							= word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)							)
      UpperCAmelCase_				:   Union[str, Any]							= char
   return pairs





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    A_	:  Optional[int]								=			VOCAB_FILES_NAMES
    A_	:  Any								=			PRETRAINED_VOCAB_FILES_MAP
    A_	:  Dict								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_	:  List[str]								=			['input_ids', 'attention_mask']





    def __init__(						self					: Optional[int]							, __snake_case					: Dict							, __snake_case					: List[Any]							, __snake_case					: List[Any]="replace"							, __snake_case					: Any="<|endoftext|>"							, __snake_case					: Union[str, Any]="<|endoftext|>"							, __snake_case					: List[str]="<|endoftext|>"							, __snake_case					: Any=None							, __snake_case					: List[str]=False							, __snake_case					: Optional[int]=False							, **__snake_case					: str							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else bos_token
       UpperCAmelCase_				:   List[Any]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else eos_token
       UpperCAmelCase_				:   Optional[int]							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else unk_token
       UpperCAmelCase_				:   Tuple							= AddedToken(__snake_case							, lstrip=__snake_case							, rstrip=__snake_case						) if isinstance(__snake_case							, __snake_case						) else pad_token
       super().__init__(
           errors=__snake_case							, unk_token=__snake_case							, bos_token=__snake_case							, eos_token=__snake_case							, pad_token=__snake_case							, add_prefix_space=__snake_case							, add_bos_token=__snake_case							, **__snake_case							, )
       UpperCAmelCase_				:   List[str]							= add_bos_token

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

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_				:   int							= 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						):





       '''simple docstring'''
       return len(self.encoder						)





    def        _lowerCamelCase			(						self					: str						):





       '''simple docstring'''
       return dict(self.encoder							, **self.added_tokens_encoder						)





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[Any]						):





       '''simple docstring'''
       if token in self.cache:
          return self.cache[token]
       UpperCAmelCase_				:   Any							= tuple(__snake_case						)
       UpperCAmelCase_				:   Any							= get_pairs(__snake_case						)

       if not pairs:
          return token

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

             if word[i] == first and i < len(__snake_case						) - 1 and word[i + 1] == second:
                new_word.append(first + second						)
                i += 2
             else:
                new_word.append(word[i]						)
                i += 1
          UpperCAmelCase_				:   Any							= tuple(__snake_case						)
          UpperCAmelCase_				:   Tuple							= new_word
          if len(__snake_case						) == 1:
             break
          else:
             UpperCAmelCase_				:   str							= get_pairs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= ''' '''.join(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= word
       return word





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: Any							, __snake_case					: List[str]=None						):





       '''simple docstring'''
       if self.add_bos_token:
          UpperCAmelCase_				:   List[Any]							= [self.bos_token_id]
       else:
          UpperCAmelCase_				:   Dict							= []

       UpperCAmelCase_				:   Dict							= bos_token_ids + token_ids_a

       if token_ids_a is None:
          return output

       return output + bos_token_ids + token_ids_a





    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: List[int]							, __snake_case					: Optional[List[int]] = None							, __snake_case					: bool = False						):





       '''simple docstring'''
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__snake_case							, token_ids_a=__snake_case							, already_has_special_tokens=__snake_case						)

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

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





    def        _lowerCamelCase			(						self					: str							, __snake_case					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= []
       for token in re.findall(self.pat							, __snake_case						):
          UpperCAmelCase_				:   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(__snake_case						).split(''' '''						)						)
       return bpe_tokens





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: Optional[int]						):





       '''simple docstring'''
       return self.encoder.get(__snake_case							, self.encoder.get(self.unk_token						)						)





    def        _lowerCamelCase			(						self					: int							, __snake_case					: str						):





       '''simple docstring'''
       return self.decoder.get(__snake_case						)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= ''''''.join(__snake_case						)
       UpperCAmelCase_				:   str							= bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8'''							, errors=self.errors						)
       return text





    def        _lowerCamelCase			(						self					: str							, __snake_case					: str							, __snake_case					: Optional[str] = None						):





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

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

       UpperCAmelCase_				:   Union[str, Any]							= 0
       with open(__snake_case							, '''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 __snake_case						: 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!'''						)
                UpperCAmelCase_				:   Dict							= token_index
             writer.write(''' '''.join(__snake_case						) + '''\n'''						)
             index += 1

       return vocab_file, merge_file





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: str							, __snake_case					: List[Any]=False							, **__snake_case					: Dict						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= kwargs.pop('''add_prefix_space'''							, self.add_prefix_space						)
       if is_split_into_words or add_prefix_space:
          UpperCAmelCase_				:   str							= ''' ''' + text
       return (text, kwargs)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: "Conversation"						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= []
       for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__snake_case							, add_special_tokens=__snake_case						) + [self.eos_token_id]						)
       if len(__snake_case						) > self.model_max_length:
          UpperCAmelCase_				:   str							= input_ids[-self.model_max_length :]
       return input_ids



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__UpperCamelCase					:	int          =				get_logger(__name__)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None      ,							):
   # Model states
   for i, state in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Model weights saved in {output_model_file}'''							)
   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= opt.state_dict()
      UpperCAmelCase_				:   Any							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Optimizer state saved in {output_optimizer_file}'''							)
   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= scheduler.state_dict()
      UpperCAmelCase_				:   Tuple							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   Tuple							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Scheduler state saved in {output_scheduler_file}'''							)
   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   Union[str, Any]							= scaler.state_dict()
      UpperCAmelCase_				:   Union[str, Any]							= os.path.join(__lowercase      ,							__lowercase							)
      torch.save(__lowercase      ,							__lowercase							)
      logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''							)
   # Random number generator states
   UpperCAmelCase_				:   Union[str, Any]							= {}
   UpperCAmelCase_				:   Tuple							= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
   UpperCAmelCase_				:   List[Any]							= random.getstate()
   UpperCAmelCase_				:   Any							= np.random.get_state()
   UpperCAmelCase_				:   List[Any]							= torch.get_rng_state()
   if is_xpu_available():
      UpperCAmelCase_				:   Optional[int]							= torch.xpu.get_rng_state_all()
   else:
      UpperCAmelCase_				:   Any							= torch.cuda.get_rng_state_all()
   if is_tpu_available():
      UpperCAmelCase_				:   List[str]							= xm.get_rng_state()
   UpperCAmelCase_				:   Any							= os.path.join(__lowercase      ,							__lowercase							)
   torch.save(__lowercase      ,							__lowercase							)
   logger.info(F'''Random states saved in {output_states_file}'''							)
   return output_dir
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase=None      ,							__lowercase=None      ,							**__lowercase      ,							):
   if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
   if map_location is None:
      UpperCAmelCase_				:   Optional[Any]							= '''cpu'''
   elif map_location == "on_device":
      UpperCAmelCase_				:   Optional[int]							= PartialState().device
   # Model states
   for i, model in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[Any]							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      models[i].load_state_dict(torch.load(__lowercase      ,							map_location=__lowercase							)      ,							**__lowercase							)
   logger.info('''All model weights loaded successfully'''							)

   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[str]							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      UpperCAmelCase_				:   Tuple							= torch.load(__lowercase      ,							map_location=__lowercase							)
      optimizers[i].load_state_dict(__lowercase							)
   logger.info('''All optimizer states loaded successfully'''							)

   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      scheduler.load_state_dict(torch.load(__lowercase							)							)
   logger.info('''All scheduler states loaded successfully'''							)

   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   List[str]							= os.path.join(__lowercase      ,							__lowercase							)
      scaler.load_state_dict(torch.load(__lowercase							)							)
      logger.info('''GradScaler state loaded successfully'''							)

   # Random states
   try:
      UpperCAmelCase_				:   Optional[Any]							= torch.load(os.path.join(__lowercase      ,							F'''{RNG_STATE_NAME}_{process_index}.pkl'''							)							)
      random.setstate(states['''random_state''']							)
      np.random.set_state(states['''numpy_random_seed''']							)
      torch.set_rng_state(states['''torch_manual_seed''']							)
      if is_xpu_available():
         torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
      else:
         torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
      if is_tpu_available():
         xm.set_rng_state(states['''xm_seed''']							)
      logger.info('''All random states loaded successfully'''							)
   except Exception:
      logger.info('''Could not load random states'''							)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Optional[int]							= Path(__lowercase							) / F'''custom_checkpoint_{index}.pkl'''
   logger.info(F'''Saving the state of {get_pretty_name(__lowercase							)} to {save_location}'''							)
   torch.save(obj.state_dict()      ,							__lowercase							)


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Dict							= F'''{path}/custom_checkpoint_{index}.pkl'''
   logger.info(F'''Loading the state of {get_pretty_name(__lowercase							)} from {load_location}'''							)
   obj.load_state_dict(torch.load(__lowercase      ,							map_location='''cpu'''							)							)