



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase:      int		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







				def __init__(   self				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						int		,    __snake_case				:						float		,    **__snake_case				:						Optional[int]			):
						a		:      Optional[Any]       =  feature_size
						a		:      Tuple       =  sampling_rate
						a		:      str       =  padding_value

						a		:      Any       =  kwargs.pop('padding_side'		,    'right'			)
						a		:      Tuple       =  kwargs.pop('return_attention_mask'		,    __snake_case			)

						super().__init__(**__snake_case			)







				def     lowercase_ (   self				:						Tuple		,    __snake_case				:						Union[
				        BatchFeature,
				        List[BatchFeature],
				        Dict[str, BatchFeature],
				        Dict[str, List[BatchFeature]],
				        List[Dict[str, BatchFeature]],
				    ]		,    __snake_case				:						Union[bool, str, PaddingStrategy] = True		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						bool = False		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[Union[str, TensorType]] = None		,    ):
						# If we have a list of dicts, let's convert it in a dict of lists
						# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
						if isinstance(__snake_case		,    (list, tuple)			) and isinstance(processed_features[0]		,    (dict, BatchFeature)			):
								a		:      Union[str, Any]       =  {
								    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
								}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
								raise ValueError(
								    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
								    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
								    F""" {list(processed_features.keys()			)}"""			)

						a		:      Tuple       =  processed_features[self.model_input_names[0]]
						a		:      str       =  (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(__snake_case			) == 0:
								if return_attention_mask:
										a		:      Dict       =  []
								return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						a		:      Optional[int]       =  required_input[0]
						if isinstance(__snake_case		,    (list, tuple)			):
								# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
								a		:      Dict       =  0
								while len(required_input[index]			) == 0:
										index += 1
								if index < len(__snake_case			):
										a		:      Tuple       =  required_input[index][0]

						if return_tensors is None:
								if is_tf_tensor(__snake_case			):
										a		:      Any       =  'tf'
								elif is_torch_tensor(__snake_case			):
										a		:      Optional[Any]       =  'pt'
								elif isinstance(__snake_case		,    (int, float, list, tuple, np.ndarray)			):
										a		:      List[Any]       =  'np'
								else:
										raise ValueError(
										    F"""type of {first_element} unknown: {type(__snake_case			)}. """
										    'Should be one of a python, numpy, pytorch or tensorflow object.'			)

						for key, value in processed_features.items():
								if isinstance(value[0]		,    (int, float)			):
										a		:      List[Any]       =  to_numpy(__snake_case			)
								else:
										a		:      Tuple       =  [to_numpy(__snake_case			) for v in value]

								# Convert padding_strategy in PaddingStrategy
						a		:      Any       =  self._get_padding_strategies(padding=__snake_case		,    max_length=__snake_case			)

						a		:      int       =  processed_features[self.model_input_names[0]]

						a		:      Dict       =  len(__snake_case			)
						if not all(len(__snake_case			) == batch_size for v in processed_features.values()			):
								raise ValueError('Some items in the output dictionary have a different batch size than others.'			)

						a		:      Optional[int]       =  []
						for i in range(__snake_case			):
								a		:      int       =  {k: v[i] for k, v in processed_features.items()}
								# truncation
								a		:      List[str]       =  self._truncate(
								    __snake_case		,    max_length=__snake_case		,    pad_to_multiple_of=__snake_case		,    truncation=__snake_case		,    )
								truncated_inputs.append(__snake_case			)

						if padding_strategy == PaddingStrategy.LONGEST:
								# make sure that `max_length` cannot be longer than the longest truncated length
								a		:      List[str]       =  max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
								a		:      Tuple       =  PaddingStrategy.MAX_LENGTH

						a		:      int       =  {}
						for i in range(__snake_case			):
								# padding
								a		:      int       =  self._pad(
								    truncated_inputs[i]		,    max_length=__snake_case		,    padding_strategy=__snake_case		,    pad_to_multiple_of=__snake_case		,    return_attention_mask=__snake_case		,    )

								for key, value in outputs.items():
										if key not in batch_outputs:
												a		:      int       =  []
										if value.dtype is np.dtype(np.floataa			):
												a		:      List[str]       =  value.astype(np.floataa			)
										batch_outputs[key].append(__snake_case			)

						return BatchFeature(__snake_case		,    tensor_type=__snake_case			)







				def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[Dict[str, np.ndarray], BatchFeature]		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    ):
						a		:      List[str]       =  processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
								a		:      Any       =  len(__snake_case			)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
								a		:      Dict       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						a		:      List[Any]       =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
								a		:      Union[str, Any]       =  np.ones(len(__snake_case			)		,    dtype=np.intaa			)

						if needs_to_be_padded:
								a		:      Tuple       =  max_length - len(__snake_case			)
								if self.padding_side == "right":
										if return_attention_mask:
												a		:      Any       =  np.pad(
												    processed_features['attention_mask']		,    (0, difference)			)
										a		:      Optional[Any]       =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
										a		:      Optional[int]       =  np.pad(
										    __snake_case		,    __snake_case		,    'constant'		,    constant_values=self.padding_value			)
								elif self.padding_side == "left":
										if return_attention_mask:
												a		:      Optional[int]       =  np.pad(
												    processed_features['attention_mask']		,    (difference, 0)			)
										a		:      Optional[Any]       =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
										a		:      List[Any]       =  np.pad(
										    __snake_case		,    __snake_case		,    'constant'		,    constant_values=self.padding_value			)
								else:
										raise ValueError('Invalid padding strategy:' + str(self.padding_side			)			)

						return processed_features







				def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[Dict[str, np.ndarray], BatchFeature]		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    ):
						if not truncation:
								return processed_features
						elif truncation and max_length is None:
								raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'			)

						a		:      Dict       =  processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
								a		:      Optional[Any]       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						a		:      str       =  len(__snake_case			) > max_length

						if needs_to_be_truncated:
								a		:      str       =  processed_features[self.model_input_names[0]][:max_length]
								if "attention_mask" in processed_features:
										a		:      List[str]       =  processed_features['attention_mask'][:max_length]

						return processed_features







				def     lowercase_ (   self				:						Dict		,    __snake_case				:						Optional[Any]=False		,    __snake_case				:						Optional[int]=None			):

						# Get padding strategy
						if padding is not False:
								if padding is True:
										a		:      Union[str, Any]       =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
								elif not isinstance(__snake_case		,    __snake_case			):
										a		:      List[Any]       =  PaddingStrategy(__snake_case			)
								elif isinstance(__snake_case		,    __snake_case			):
										a		:      List[str]       =  padding
						else:
								a		:      Dict       =  PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
								if padding_strategy == PaddingStrategy.MAX_LENGTH:
										raise ValueError(
										    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""			)

								# Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
								raise ValueError(
								    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
								    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'			)

						return padding_strategy




'''simple docstring'''






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)

lowerCAmelCase:      Dict		 =		{
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

lowerCAmelCase:      List[Any]		 =		{
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

lowerCAmelCase:      Optional[Any]		 =		{
    'vinai/phobert-base': 2_5_6,
    'vinai/phobert-large': 2_5_6,
}
def    lowerCamelCase__ (		_A       ):
  a		:      List[Any]       =  set()
  a		:      Union[str, Any]       =  word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    a		:      Tuple       =  char

  a		:      Any       =  set(_A       )
  return pairs

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Any="<s>"		,    __snake_case				:						List[Any]="</s>"		,    __snake_case				:						Union[str, Any]="</s>"		,    __snake_case				:						List[Any]="<s>"		,    __snake_case				:						Optional[Any]="<unk>"		,    __snake_case				:						Union[str, Any]="<pad>"		,    __snake_case				:						List[Any]="<mask>"		,    **__snake_case				:						int		,    ):
      super().__init__(
          bos_token=__snake_case		,    eos_token=__snake_case		,    unk_token=__snake_case		,    sep_token=__snake_case		,    cls_token=__snake_case		,    pad_token=__snake_case		,    mask_token=__snake_case		,    **__snake_case		,    )

      a		:      Optional[int]       =  vocab_file
      a		:      Optional[Any]       =  merges_file

      a		:      int       =  {}
      a		:      List[str]       =  0
      a		:      Union[str, Any]       =  1
      a		:      Optional[Any]       =  2
      a		:      List[Any]       =  3

      self.add_from_file(__snake_case			)

      a		:      Dict       =  {v: k for k, v in self.encoder.items()}

      with open(__snake_case		,    encoding='utf-8'			) as merges_handle:
        a		:      Optional[int]       =  merges_handle.read().split('\n'			)[:-1]
      a		:      Any       =  [tuple(merge.split()[:-1]			) for merge in merges]
      a		:      List[Any]       =  dict(zip(__snake_case		,    range(len(__snake_case			)			)			)			)
      a		:      List[str]       =  {}







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):

      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a		:      Union[str, Any]       =  [self.cls_token_id]
      a		:      Any       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







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

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







    def     lowercase_ (   self				:						int		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):
      a		:      Dict       =  [self.sep_token_id]
      a		:      Optional[int]       =  [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]







    @property
    def     lowercase_ (   self				:						Optional[int]			):
      return len(self.encoder			)







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







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						Optional[Any]			):
      if token in self.cache:
        return self.cache[token]
      a		:      int       =  tuple(__snake_case			)
      a		:      int       =  tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
      a		:      Dict       =  get_pairs(__snake_case			)

      if not pairs:
        return token

      while True:
        a		:      str       =  min(__snake_case		,    key=lambda __snake_case			: self.bpe_ranks.get(__snake_case		,    float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
          break
        a     ,			a		:      Tuple       =  bigram
        a		:      int       =  []
        a		:      Optional[int]       =  0
        while i < len(__snake_case			):
          try:
            a		:      List[str]       =  word.index(__snake_case		,    __snake_case			)
          except ValueError:
            new_word.extend(word[i:]			)
            break
          else:
            new_word.extend(word[i:j]			)
            a		:      Dict       =  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
        a		:      Optional[Any]       =  tuple(__snake_case			)
        a		:      Tuple       =  new_word
        if len(__snake_case			) == 1:
          break
        else:
          a		:      Union[str, Any]       =  get_pairs(__snake_case			)
      a		:      List[Any]       =  '@@ '.join(__snake_case			)
      a		:      int       =  word[:-4]
      a		:      Optional[int]       =  word
      return word







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]			):
      a		:      Optional[int]       =  []

      a		:      Optional[Any]       =  re.findall(r'\S+\n?'		,    __snake_case			)

      for token in words:
        split_tokens.extend(list(self.bpe(__snake_case			).split(' '			)			)			)
      return split_tokens







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Dict			):
      return self.encoder.get(__snake_case		,    self.encoder.get(self.unk_token			)			)







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Dict			):
      return self.decoder.get(__snake_case		,    self.unk_token			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Any			):
      a		:      Union[str, Any]       =  ' '.join(__snake_case			).replace('@@ '		,    ''			).strip()
      return out_string







    def     lowercase_ (   self				:						str		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      if not os.path.isdir(__snake_case			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
      a		:      Dict       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
      a		:      int       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(__snake_case			):
        copyfile(self.vocab_file		,    __snake_case			)

      if os.path.abspath(self.merges_file			) != os.path.abspath(__snake_case			):
        copyfile(self.merges_file		,    __snake_case			)

      return out_vocab_file, out_merge_file







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[str]			):
      if isinstance(__snake_case		,    __snake_case			):
        try:
          with open(__snake_case		,    'r'		,    encoding='utf-8'			) as fd:
            self.add_from_file(__snake_case			)
        except FileNotFoundError as fnfe:
          raise fnfe
        except UnicodeError:
          raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""			)
        return

      a		:      List[str]       =  f.readlines()
      for lineTmp in lines:
        a		:      Any       =  lineTmp.strip()
        a		:      Dict       =  line.rfind(' '			)
        if idx == -1:
          raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''			)
        a		:      str       =  line[:idx]
        a		:      Optional[int]       =  len(self.encoder			)