



import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        TimesformerForVideoClassification,
        TimesformerModel,
    )
    from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from transformers import VideoMAEImageProcessor



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=5,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=37,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_="divided_space_time",     SCREAMING_SNAKE_CASE_=None,     )	->						Tuple:
          UpperCamelCase       :    Optional[Any]		      =      parent
          UpperCamelCase       :    Tuple		      =      batch_size
          UpperCamelCase       :    Optional[Any]		      =      image_size
          UpperCamelCase       :    Optional[int]		      =      num_channels
          UpperCamelCase       :    Union[str, Any]		      =      patch_size
          UpperCamelCase       :    List[Any]		      =      num_frames
          UpperCamelCase       :    Any		      =      is_training
          UpperCamelCase       :    int		      =      use_labels
          UpperCamelCase       :    Union[str, Any]		      =      hidden_size
          UpperCamelCase       :    Any		      =      num_hidden_layers
          UpperCamelCase       :    Any		      =      num_attention_heads
          UpperCamelCase       :    Optional[Any]		      =      intermediate_size
          UpperCamelCase       :    List[str]		      =      hidden_act
          UpperCamelCase       :    int		      =      hidden_dropout_prob
          UpperCamelCase       :    Dict		      =      attention_probs_dropout_prob
          UpperCamelCase       :    List[str]		      =      attention_type
          UpperCamelCase       :    Tuple		      =      initializer_range
          UpperCamelCase       :    str		      =      scope
          UpperCamelCase       :    Tuple		      =      num_labels

          # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
          UpperCamelCase       :    int		      =      (image_size // patch_size) ** 2
          UpperCamelCase       :    Union[str, Any]		      =      (num_frames) * self.num_patches_per_frame + 1
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      floats_tensor(
              [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]			)

          UpperCamelCase       :    Union[str, Any]		      =      None
          if self.use_labels:
             UpperCamelCase       :    Union[str, Any]		      =      ids_tensor([self.batch_size],     self.num_labels			)

          UpperCamelCase       :    Tuple		      =      self.get_config()

          return config, pixel_values, labels
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[str]		      =      TimesformerConfig(
              image_size=self.image_size,     patch_size=self.patch_size,     num_channels=self.num_channels,     num_frames=self.num_frames,     hidden_size=self.hidden_size,     num_hidden_layers=self.num_hidden_layers,     num_attention_heads=self.num_attention_heads,     intermediate_size=self.intermediate_size,     hidden_act=self.hidden_act,     hidden_dropout_prob=self.hidden_dropout_prob,     attention_probs_dropout_prob=self.attention_probs_dropout_prob,     initializer_range=self.initializer_range,     attention_type=self.attention_type,     )
          UpperCamelCase       :    Union[str, Any]		      =      self.num_labels
          return config
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Tuple:
          UpperCamelCase       :    Union[str, Any]		      =      TimesformerModel(config=_lowerCamelCase			)
          model.to(_lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Any		      =      model(_lowerCamelCase			)
          self.parent.assertEqual(result.last_hidden_state.shape,     (self.batch_size, self.seq_length, self.hidden_size)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    int		      =      TimesformerForVideoClassification(_lowerCamelCase			)
          model.to(_lowerCamelCase			)
          model.eval()

          UpperCamelCase       :    List[Any]		      =      model(_lowerCamelCase			)

          # verify the logits shape
          UpperCamelCase       :    Optional[int]		      =      torch.Size((self.batch_size, self.num_labels)			)
          self.parent.assertEqual(result.logits.shape,     _lowerCamelCase			)






       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    List[Any]		      =      self.prepare_config_and_inputs()
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    int		      =      config_and_inputs
          UpperCamelCase       :    List[str]		      =      {'pixel_values': pixel_values}
          return config, inputs_dict






@require_torch
class 							lowerCAmelCase_						(    __a			,       __a			,       unittest.TestCase				):
       UpperCAmelCase__							:   List[str]  	=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
       UpperCAmelCase__							:   Union[str, Any]  	=    (
           {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
           if is_torch_available()
           else {}
       )

       UpperCAmelCase__							:   int  	=    False
       UpperCAmelCase__							:   Tuple  	=    False
       UpperCAmelCase__							:   Any  	=    False
       UpperCAmelCase__							:   List[Any]  	=    False
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Union[str, Any]		      =      TimesformerModelTester(self			)
          UpperCamelCase       :    Optional[Any]		      =      ConfigTester(
              self,     config_class=_lowerCamelCase,     has_text_modality=_lowerCamelCase,     hidden_size=37			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=False			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      copy.deepcopy(_lowerCamelCase			)

          if return_labels:
             if model_class in get_values(_lowerCamelCase			):
                UpperCamelCase       :    str		      =      torch.zeros(
                    self.model_tester.batch_size,     dtype=torch.long,     device=_lowerCamelCase			)

          return inputs_dict
       def    snake_case_			(     self			)	->						List[Any]:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='TimeSformer does not use inputs_embeds'			)
       def    snake_case_			(     self			)	->						Tuple:
          pass
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    List[str]		      =      model_class(_lowerCamelCase			)
             self.assertIsInstance(model.get_input_embeddings(),     (nn.Module)			)
             UpperCamelCase       :    Any		      =      model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowerCamelCase,     nn.Linear			)			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Any		      =      model_class(_lowerCamelCase			)
             UpperCamelCase       :    Optional[int]		      =      inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase       :    Tuple		      =      [*signature.parameters.keys()]

             UpperCamelCase       :    Union[str, Any]		      =      ['pixel_values']
             self.assertListEqual(arg_names[:1],     _lowerCamelCase			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_video_classification(*_lowerCamelCase			)
       @slow
       def    snake_case_			(     self			)	->						str:
          for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    Tuple		      =      TimesformerModel.from_pretrained(_lowerCamelCase			)
             self.assertIsNotNone(_lowerCamelCase			)
       def    snake_case_			(     self			)	->						Optional[int]:
          if not self.has_attentions:
             pass

          else:
             UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs_for_common()
             UpperCamelCase       :    List[Any]		      =      True

             for model_class in self.all_model_classes:
                UpperCamelCase       :    List[str]		      =      self.model_tester.seq_length
                UpperCamelCase       :    str		      =      self.model_tester.num_frames

                UpperCamelCase       :    Union[str, Any]		      =      True
                UpperCamelCase       :    Tuple		      =      False
                UpperCamelCase       :    str		      =      True
                UpperCamelCase       :    str		      =      model_class(_lowerCamelCase			)
                model.to(_lowerCamelCase			)
                model.eval()
                with torch.no_grad():
                   UpperCamelCase       :    Dict		      =      model(**self._prepare_for_class(_lowerCamelCase,     _lowerCamelCase			)			)
                UpperCamelCase       :    Optional[int]		      =      outputs.attentions
                self.assertEqual(len(_lowerCamelCase			),     self.model_tester.num_hidden_layers			)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                UpperCamelCase       :    str		      =      True
                UpperCamelCase       :    Any		      =      model_class(_lowerCamelCase			)
                model.to(_lowerCamelCase			)
                model.eval()
                with torch.no_grad():
                   UpperCamelCase       :    Dict		      =      model(**self._prepare_for_class(_lowerCamelCase,     _lowerCamelCase			)			)
                UpperCamelCase       :    Optional[int]		      =      outputs.attentions
                self.assertEqual(len(_lowerCamelCase			),     self.model_tester.num_hidden_layers			)

                # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                self.assertListEqual(
                    list(attentions[0].shape[-3:]			),     [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1],     )
                UpperCamelCase       :    List[Any]		      =      len(_lowerCamelCase			)

                # Check attention is always last and order is fine
                UpperCamelCase       :    str		      =      True
                UpperCamelCase       :    Tuple		      =      True
                UpperCamelCase       :    str		      =      model_class(_lowerCamelCase			)
                model.to(_lowerCamelCase			)
                model.eval()
                with torch.no_grad():
                   UpperCamelCase       :    Dict		      =      model(**self._prepare_for_class(_lowerCamelCase,     _lowerCamelCase			)			)

                self.assertEqual(out_len + 1,     len(_lowerCamelCase			)			)

                UpperCamelCase       :    int		      =      outputs.attentions

                self.assertEqual(len(_lowerCamelCase			),     self.model_tester.num_hidden_layers			)

                # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]			),     [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1],     )






       def    snake_case_			(     self			)	->						Optional[Any]:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Any		      =      model_class(_lowerCamelCase			)
             model.to(_lowerCamelCase			)
             model.eval()

             with torch.no_grad():
                UpperCamelCase       :    Any		      =      model(**self._prepare_for_class(_lowerCamelCase,     _lowerCamelCase			)			)

             UpperCamelCase       :    Optional[int]		      =      outputs.hidden_states
             UpperCamelCase       :    Dict		      =      self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(_lowerCamelCase			),     _lowerCamelCase			)

             UpperCamelCase       :    Optional[int]		      =      self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]			),     [seq_length, self.model_tester.hidden_size],     )

          UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    int		      =      True
             check_hidden_states_output(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase			)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase       :    Dict		      =      True

             check_hidden_states_output(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase			)






def 						UpperCamelCase      (  )      ->	Union[str, Any]:
   UpperCamelCase       :    Tuple		      =      hf_hub_download(
       repo_id='hf-internal-testing/spaghetti-video'			,	filename='eating_spaghetti.npy'			,	repo_type='dataset'      )
   UpperCamelCase       :    Dict		      =      np.load(snake_case__      )
   return list(snake_case__      )



@require_torch
@require_vision
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @cached_property
       def    snake_case_			(     self			)	->						Optional[Any]:
          return (
              VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],     image_std=[0.5, 0.5, 0.5]			)
              if is_vision_available()
              else None
          )






       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'			).to(
              _lowerCamelCase			)

          UpperCamelCase       :    Optional[Any]		      =      self.default_image_processor
          UpperCamelCase       :    List[str]		      =      prepare_video()
          UpperCamelCase       :    List[str]		      =      image_processor(video[:8],     return_tensors='pt'			).to(_lowerCamelCase			)

          # forward pass
          with torch.no_grad():
             UpperCamelCase       :    Dict		      =      model(**_lowerCamelCase			)

          # verify the logits
          UpperCamelCase       :    str		      =      torch.Size((1, 400)			)
          self.assertEqual(outputs.logits.shape,     _lowerCamelCase			)

          UpperCamelCase       :    List[str]		      =      torch.tensor([-0.30_16, -0.77_13, -0.42_05]			).to(_lowerCamelCase			)

          self.assertTrue(torch.allclose(outputs.logits[0, :3],     _lowerCamelCase,     atol=1e-4			)			)





"""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							=				logging.get_logger(__name__)

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

_lowercase							=				{
    '''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							=				{
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char

			__lowercase       =       set(__magic_name__      )
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			Optional[Any]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    **_lowerCamelCase  ,    )			->							Tuple:




										'''simple docstring'''
										super().__init__(
										    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										__lowercase       =       vocab_file
										__lowercase       =       merges_file

										__lowercase       =       {}
										__lowercase       =       0
										__lowercase       =       1
										__lowercase       =       2
										__lowercase       =       3

										self.add_from_file(_lowerCamelCase       )

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

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[:-1]
										__lowercase       =       [tuple(merge.split()[:-1]       ) for merge in merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]
										__lowercase       =       [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''

										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 token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
										__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[:-4]
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       []

										__lowercase       =       re.findall(R"\S+\n?"  ,    _lowerCamelCase       )

										for token in words:
													split_tokens.extend(list(self.bpe(_lowerCamelCase       ).split(" "       )       )       )
										return split_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase  ,    self.unk_token       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       " ".join(_lowerCamelCase       ).replace("@@ "  ,    ""       ).strip()
										return out_string
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										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"]       )

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

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

										return out_vocab_file, out_merge_file





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													try:
																with open(_lowerCamelCase  ,    "r"  ,    encoding="utf-8"       ) as fd:
																			self.add_from_file(_lowerCamelCase       )
													except FileNotFoundError as fnfe:
																raise fnfe
													except UnicodeError:
																raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''       )
													return

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





