






"""simple docstring"""



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class 	a					(       TensorFormatter[Mapping, np.ndarray, Mapping]     ):







    def __init__(      self							,		_lowerCamelCase=None							,		**_lowerCamelCase	):
      super().__init__(features=_lowerCamelCase	)
      lowercase							   =	np_array_kwargs







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        if column and all(
            isinstance(_lowerCamelCase							,		np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
          return np.stack(_lowerCamelCase	)
        else:
          # don't use np.array(column, dtype=object)
          # since it fails in certain cases
          # see https://stackoverflow.com/q/51005699
          lowercase							   =	np.empty(len(_lowerCamelCase	)							,		dtype=_lowerCamelCase	)
          lowercase							   =	column
          return out
      return column







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if isinstance(_lowerCamelCase							,		(str, bytes, type(_lowerCamelCase	))	):
        return value
      elif isinstance(_lowerCamelCase							,		(np.character, np.ndarray)	) and np.issubdtype(value.dtype							,		np.character	):
        return value
      elif isinstance(_lowerCamelCase							,		np.number	):
        return value

      lowercase							   =	{}

      if isinstance(_lowerCamelCase							,		np.ndarray	) and np.issubdtype(value.dtype							,		np.integer	):
        lowercase							   =	{'dtype': np.intaa}
      elif isinstance(_lowerCamelCase							,		np.ndarray	) and np.issubdtype(value.dtype							,		np.floating	):
        lowercase							   =	{'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(_lowerCamelCase							,		PIL.Image.Image	):
          return np.asarray(_lowerCamelCase							,		**self.np_array_kwargs	)

      return np.asarray(_lowerCamelCase							,		**{**default_dtype, **self.np_array_kwargs}	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(_lowerCamelCase							,		torch.Tensor	):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
      if hasattr(_lowerCamelCase							,		'__array__'	) and not isinstance(_lowerCamelCase							,		(np.ndarray, np.character, np.number)	):
        lowercase							   =	data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_lowerCamelCase							,		np.ndarray	):
        if data_struct.dtype == object:
          return self._consolidate([self.recursive_tensorize(_lowerCamelCase	) for substruct in data_struct]	)
      if isinstance(_lowerCamelCase							,		(list, tuple)	):
        return self._consolidate([self.recursive_tensorize(_lowerCamelCase	) for substruct in data_struct]	)
      return self._tensorize(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return map_nested(self._recursive_tensorize							,		_lowerCamelCase							,		map_list=_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	self.numpy_arrow_extractor().extract_row(_lowerCamelCase	)
      lowercase							   =	self.python_features_decoder.decode_row(_lowerCamelCase	)
      return self.recursive_tensorize(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	self.numpy_arrow_extractor().extract_column(_lowerCamelCase	)
      lowercase							   =	self.python_features_decoder.decode_column(_lowerCamelCase							,		pa_table.column_names[0]	)
      lowercase							   =	self.recursive_tensorize(_lowerCamelCase	)
      lowercase							   =	self._consolidate(_lowerCamelCase	)
      return column







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	self.numpy_arrow_extractor().extract_batch(_lowerCamelCase	)
      lowercase							   =	self.python_features_decoder.decode_batch(_lowerCamelCase	)
      lowercase							   =	self.recursive_tensorize(_lowerCamelCase	)
      for column_name in batch:
        lowercase							   =	self._consolidate(batch[column_name]	)
      return batch










"""simple docstring"""



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		:  Union[str, Any]						=		logging.get_logger(__name__)

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

_UpperCamelCase		:  Any						=		{
    '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		:  str						=		{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}
@lru_cache()
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





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





  '''simple docstring'''





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



class 	a					(       a_     ):
    UpperCAmelCase_			:					int     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					List[str]     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Tuple     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["input_ids", "attention_mask"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="replace"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase="<|endoftext|>"							,		_lowerCamelCase=None							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else bos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else eos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else unk_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else pad_token
      super().__init__(
          errors=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		pad_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		add_bos_token=_lowerCamelCase							,		**_lowerCamelCase							,		)
      lowercase							   =	add_bos_token

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

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







    def       UpperCamelCase_				(      self	):
      return dict(self.encoder							,		**self.added_tokens_encoder	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if token in self.cache:
        return self.cache[token]
      lowercase							   =	tuple(_lowerCamelCase	)
      lowercase							   =	get_pairs(_lowerCamelCase	)

      if not pairs:
        return token

      while True:
        lowercase							   =	min(_lowerCamelCase							,		key=lambda _lowerCamelCase	: self.bpe_ranks.get(_lowerCamelCase							,		float('inf'	)	)	)
        if bigram not in self.bpe_ranks:
          break
        lowercase   ,					lowercase							   =	bigram
        lowercase							   =	[]
        lowercase							   =	0
        while i < len(_lowerCamelCase	):
          try:
            lowercase							   =	word.index(_lowerCamelCase							,		_lowerCamelCase	)
          except ValueError:
            new_word.extend(word[i:]	)
            break
          else:
            new_word.extend(word[i:j]	)
            lowercase							   =	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
        lowercase							   =	tuple(_lowerCamelCase	)
        lowercase							   =	new_word
        if len(_lowerCamelCase	) == 1:
          break
        else:
          lowercase							   =	get_pairs(_lowerCamelCase	)
      lowercase							   =	' '.join(_lowerCamelCase	)
      lowercase							   =	word
      return word







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=None	):
      if self.add_bos_token:
        lowercase							   =	[self.bos_token_id]
      else:
        lowercase							   =	[]

      lowercase							   =	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							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      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 not self.add_bos_token:
        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	))
      return [1] + ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	))







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for token in re.findall(self.pat							,		_lowerCamelCase	):
        lowercase							   =	''.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       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.decoder.get(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	''.join(_lowerCamelCase	)
      lowercase							   =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,		errors=self.errors	)
      return text







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

      lowercase							   =	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!'	)
            lowercase							   =	token_index
          writer.write(' '.join(_lowerCamelCase	) + '\n'	)
          index += 1

      return vocab_file, merge_file







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.pop('add_prefix_space'							,		self.add_prefix_space	)
      if is_split_into_words or add_prefix_space:
        lowercase							   =	' ' + text
      return (text, kwargs)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase	) + [self.eos_token_id]	)
      if len(_lowerCamelCase	) > self.model_max_length:
        lowercase							   =	input_ids[-self.model_max_length :]
      return input_ids


