"""simple docstring"""



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[Any]							=					None
    UpperCamelCase_	:					Optional[Any]							=					None
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         return self.feat_extract_tester.prepare_feat_extract_dict()
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         self.assertTrue(hasattr(A_	,      '''feature_size'''    )    )
         self.assertTrue(hasattr(A_	,      '''sampling_rate'''    )    )
         self.assertTrue(hasattr(A_	,      '''padding_value'''    )    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         self.assertTrue(all(len(A_    ) == len(A_    ) for x, y in zip(A_	,      processed_features[input_name]    )    )    )

         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
         __snake_case				=							BatchFeature({input_name: speech_inputs}	,      tensor_type='''np'''    )

         __snake_case				=							processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              __snake_case				=							batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
    @require_torch
    def  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}	,      tensor_type='''pt'''    )

         __snake_case				=							processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              __snake_case				=							batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
    @require_tf
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}	,      tensor_type='''tf'''    )

         __snake_case				=							processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              __snake_case				=							batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
    def  lowercase					(		self							:     int	,      A_							:     Union[str, Any]=False    )    ->       Optional[Any]:
         def _inputs_have_equal_length(A_							:     int    ):
              __snake_case				=							len(input[0]    )
              for input_slice in input[1:]:
                   if len(A_    ) != length:
                        return False
              return True

         def _inputs_are_equal(A_							:     int	,      A_							:     Optional[int]    ):
              if len(A_    ) != len(A_    ):
                   return False

              for input_slice_a, input_slice_a in zip(A_	,      A_    ):
                   if not np.allclose(np.asarray(A_    )	,      np.asarray(A_    )	,      atol=1E-3    ):
                        return False
              return True

         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(numpify=A_    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         __snake_case				=							self.feat_extract_tester.seq_length_diff
         __snake_case				=							self.feat_extract_tester.max_seq_length + pad_diff
         __snake_case				=							self.feat_extract_tester.min_seq_length
         __snake_case				=							self.feat_extract_tester.batch_size
         __snake_case				=							self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         __snake_case				=							feat_extract.pad(A_	,      padding=A_    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[-1]    )    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )
         __snake_case				=							input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(A_    ):
              feat_extract.pad(A_	,      padding='''max_length'''    )[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=A_	,      return_tensors='''np'''    )
         __snake_case				=							input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(A_    )    )
         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertTrue(_inputs_are_equal(A_	,      A_    )    )
         self.assertTrue(len(input_a[0]    ) == pad_min_length    )
         self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

         if feature_size > 1:
              self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         __snake_case				=							feat_extract.pad(A_	,      pad_to_multiple_of=10    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      pad_to_multiple_of=10    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      pad_to_multiple_of=10	,      max_length=A_    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      pad_to_multiple_of=10	,      max_length=A_	,      return_tensors='''np'''	,      )
         __snake_case				=							input_a[input_name]

         self.assertTrue(all(len(A_    ) % 10 == 0 for x in input_a    )    )
         self.assertTrue(_inputs_are_equal(A_	,      A_    )    )

         __snake_case				=							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(A_    ) == expected_mult_pad_length for x in input_a    )    )
         self.assertEqual(input_a.shape[:2]	,      (batch_size, expected_mult_pad_length)    )

         if feature_size > 1:
              self.assertTrue(input_a.shape[2] == feature_size    )

         # Check padding value is correct
         __snake_case				=							(np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
             < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
             < 1E-3    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     Optional[int]=False    )    ->       Dict:
         def _inputs_have_equal_length(A_							:     Any    ):
              __snake_case				=							len(input[0]    )
              for input_slice in input[1:]:
                   if len(A_    ) != length:
                        return False
              return True

         def _inputs_are_equal(A_							:     str	,      A_							:     str    ):
              if len(A_    ) != len(A_    ):
                   return False

              for input_slice_a, input_slice_a in zip(A_	,      A_    ):
                   if not np.allclose(np.asarray(A_    )	,      np.asarray(A_    )	,      atol=1E-3    ):
                        return False
              return True

         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(numpify=A_    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         # truncate to smallest
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      truncation=A_    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )    )
         __snake_case				=							input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertFalse(_inputs_have_equal_length(A_    )    )

         # truncate to smallest with np
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      return_tensors='''np'''	,      truncation=A_	,      )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      return_tensors='''np'''    )
         __snake_case				=							input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(A_    )    )

         # truncate to middle
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[1]    )	,      truncation=A_	,      return_tensors='''np'''	,      )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[1]    )	,      truncation=A_    )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[1]    )	,      return_tensors='''np'''    )
         __snake_case				=							input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertTrue(_inputs_are_equal(A_	,      A_    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(A_    )    )
         self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(A_    ):
              feat_extract.pad(A_	,      truncation=A_    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(A_    ):
              feat_extract.pad(A_	,      padding='''longest'''	,      truncation=A_    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(A_    ):
              feat_extract.pad(A_	,      padding='''longest'''	,      truncation=A_    )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(A_    ):
              feat_extract.pad(A_	,      padding='''max_length'''	,      truncation=A_    )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         __snake_case				=							12
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      pad_to_multiple_of=A_	,      truncation=A_	,      )
         __snake_case				=							input_a[input_name]

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      pad_to_multiple_of=A_	,      )
         __snake_case				=							input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         __snake_case				=							len(speech_inputs[0]    )
         if expected_length % pad_to_multiple_of != 0:
              __snake_case				=							((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]    ) == expected_length    )
         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertFalse(_inputs_have_equal_length(A_    )    )
    def  lowercase					(		self							:     int    )    ->       str:
         self._check_padding(numpify=A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         self._check_padding(numpify=A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         self._check_truncation(numpify=A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         self._check_truncation(numpify=A_    )
    @require_torch
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )[input_name]
         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''pt'''    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )
    @require_tf
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )[input_name]
         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''tf'''    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )
    def  lowercase					(		self							:     Dict    )    ->       List[Any]:
         __snake_case				=							self.feat_extract_dict
         __snake_case				=							True
         __snake_case				=							self.feature_extraction_class(**A_    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							[len(A_    ) for x in speech_inputs]
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )
         self.assertIn('''attention_mask'''	,      A_    )
         self.assertListEqual(list(processed.attention_mask.shape    )	,      list(processed[input_name].shape[:2]    )    )
         self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()	,      A_    )







    def  lowercase					(		self							:     Union[str, Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.feat_extract_dict
         __snake_case				=							True
         __snake_case				=							self.feature_extraction_class(**A_    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							[len(A_    ) for x in speech_inputs]
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )
         __snake_case				=							min(A_    )

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=A_	,      truncation=A_	,      return_tensors='''np'''    )
         self.assertIn('''attention_mask'''	,      A_    )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape    )	,      [processed_pad[input_name].shape[0], max_length]    )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()	,      [max_length for x in speech_inputs]    )
"""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


__lowercase		:		List[str]														=							logging.get_logger(__name__)

__lowercase		:		Union[str, Any]														=							{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__lowercase		:		Optional[int]														=							{
    "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",
    },
}

__lowercase		:		Dict														=							{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}





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

     __snake_case				=							set(snake_case)
     return pairs




class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




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

         __snake_case				=							vocab_file
         __snake_case				=							merges_file

         __snake_case				=							{}
         __snake_case				=							0
         __snake_case				=							1
         __snake_case				=							2
         __snake_case				=							3

         self.add_from_file(A_    )

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

         with open(A_	,      encoding='''utf-8'''    ) as merges_handle:
              __snake_case				=							merges_handle.read().split('''\n'''    )[:-1]
         __snake_case				=							[tuple(merge.split()[:-1]    ) for merge in merges]
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							{}
    def  lowercase					(		self							:     str	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:

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

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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							:     List[Any]    )    ->       str:
         return len(self.encoder    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         return dict(self.encoder	,      **self.added_tokens_encoder    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]    )    ->       Optional[int]:
         if token in self.cache:
              return self.cache[token]
         __snake_case				=							tuple(A_    )
         __snake_case				=							tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
         __snake_case				=							get_pairs(A_    )

         if not pairs:
              return token

         while True:
              __snake_case				=							min(A_	,      key=lambda A_    : self.bpe_ranks.get(A_	,      float('''inf'''    )    )    )
              if bigram not in self.bpe_ranks:
                   break
              __snake_case		,	__snake_case				=							bigram
              __snake_case				=							[]
              __snake_case				=							0
              while i < len(A_    ):
                   try:
                        __snake_case				=							word.index(A_	,      A_    )
                   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(A_    ) - 1 and word[i + 1] == second:
                        new_word.append(first + second    )
                        i += 2
                   else:
                        new_word.append(word[i]    )
                        i += 1
              __snake_case				=							tuple(A_    )
              __snake_case				=							new_word
              if len(A_    ) == 1:
                   break
              else:
                   __snake_case				=							get_pairs(A_    )
         __snake_case				=							'''@@ '''.join(A_    )
         __snake_case				=							word[:-4]
         __snake_case				=							word
         return word
    def  lowercase					(		self							:     int	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							[]

         __snake_case				=							re.findall(R'''\S+\n?'''	,      A_    )

         for token in words:
              split_tokens.extend(list(self.bpe(A_    ).split(''' '''    )    )    )
         return split_tokens
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Tuple    )    ->       int:
         return self.encoder.get(A_	,      self.encoder.get(self.unk_token    )    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       Optional[Any]:
         return self.decoder.get(A_	,      self.unk_token    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     int    )    ->       Tuple:
         __snake_case				=							''' '''.join(A_    ).replace('''@@ '''	,      ''''''    ).strip()
         return out_string
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

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

         return out_vocab_file, out_merge_file







    def  lowercase					(		self							:     Optional[int]	,      A_							:     Union[str, Any]    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              try:
                   with open(A_	,      '''r'''	,      encoding='''utf-8'''    ) as fd:
                        self.add_from_file(A_    )
              except FileNotFoundError as fnfe:
                   raise fnfe
              except UnicodeError:
                   raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"    )
              return

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