




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

class     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=10  ,_SCREAMING_SNAKE_CASE=[8, 16, 32, 64]  ,_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE="relu"  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"]  ,_SCREAMING_SNAKE_CASE=[2, 3, 4]  ,_SCREAMING_SNAKE_CASE=1  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				Optional[Any]  =     parent
         UpperCAmelCase_     :				Tuple  =     batch_size
         UpperCAmelCase_     :				Optional[Any]  =     image_size
         UpperCAmelCase_     :				List[Any]  =     num_channels
         UpperCAmelCase_     :				Union[str, Any]  =     embeddings_size
         UpperCAmelCase_     :				Dict  =     hidden_sizes
         UpperCAmelCase_     :				str  =     depths
         UpperCAmelCase_     :				Union[str, Any]  =     is_training
         UpperCAmelCase_     :				Any  =     use_labels
         UpperCAmelCase_     :				Optional[int]  =     hidden_act
         UpperCAmelCase_     :				List[str]  =     num_labels
         UpperCAmelCase_     :				Union[str, Any]  =     scope
         UpperCAmelCase_     :				Tuple  =     len(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     out_features
         UpperCAmelCase_     :				List[Any]  =     out_indices
         UpperCAmelCase_     :				Optional[Any]  =     num_groups





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Union[str, Any]  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         UpperCAmelCase_     :				str  =     None
         if self.use_labels:
              UpperCAmelCase_     :				int  =     ids_tensor([self.batch_size]  ,self.num_labels    )

         UpperCAmelCase_     :				int  =     self.get_config()

         return config, pixel_values, labels





    def 				a__ ( self    )					->						List[Any]:
         return BitConfig(
             num_channels=self.num_channels  ,embeddings_size=self.embeddings_size  ,hidden_sizes=self.hidden_sizes  ,depths=self.depths  ,hidden_act=self.hidden_act  ,num_labels=self.num_labels  ,out_features=self.out_features  ,out_indices=self.out_indices  ,num_groups=self.num_groups  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     BitModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(
             result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						int:
         UpperCAmelCase_     :				Any  =     self.num_labels
         UpperCAmelCase_     :				Optional[int]  =     BitForImageClassification(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[Any]  =     BitBackbone(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Union[str, Any]  =     model(_SCREAMING_SNAKE_CASE    )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps    )  ,len(config.out_features    )    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )  ,[self.batch_size, self.hidden_sizes[1], 4, 4]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )  ,len(config.out_features    )    )
         self.parent.assertListEqual(model.channels  ,config.hidden_sizes[1:]    )

         # verify backbone works with out_features=None
         UpperCAmelCase_     :				List[Any]  =     None
         UpperCAmelCase_     :				Union[str, Any]  =     BitBackbone(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE    )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps    )  ,1    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )  ,[self.batch_size, self.hidden_sizes[-1], 1, 1]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )  ,1    )
         self.parent.assertListEqual(model.channels  ,[config.hidden_sizes[-1]]    )





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


@require_torch
class     __a(	_lowerCAmelCase							,		_lowerCAmelCase							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowerCAmelCase         = (
        {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				int  =     BitModelTester(self    )
         UpperCAmelCase_     :				List[Any]  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,has_text_modality=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





    def 				a__ ( self    )					->						Optional[int]:
         return





    @unittest.skip(reason='''Bit does not output attentions'''    )
    def 				a__ ( self    )					->						Any:
         pass





    @unittest.skip(reason='''Bit does not use inputs_embeds'''    )
    def 				a__ ( self    )					->						str:
         pass





    @unittest.skip(reason='''Bit does not support input and output embeddings'''    )
    def 				a__ ( self    )					->						int:
         pass





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_     :				List[str]  =     ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_,       UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				List[str]  =     model_class(config=_SCREAMING_SNAKE_CASE    )
              for name, module in model.named_modules():
                   if isinstance(_SCREAMING_SNAKE_CASE  ,(nn.BatchNormad, nn.GroupNorm)    ):
                        self.assertTrue(
                            torch.all(module.weight == 1    )  ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,)
                        self.assertTrue(
                            torch.all(module.bias == 0    )  ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,)





    def 				a__ ( self    )					->						Optional[Any]:
         def check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				Tuple  =     model_class(_SCREAMING_SNAKE_CASE    )
              model.to(_SCREAMING_SNAKE_CASE    )
              model.eval()

              with torch.no_grad():
                   UpperCAmelCase_     :				int  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )

              UpperCAmelCase_     :				List[str]  =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              UpperCAmelCase_     :				List[Any]  =     self.model_tester.num_stages
              self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,expected_num_stages + 1    )

              # Bit's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]    )  ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,)

         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_     :				List[Any]  =     ['''preactivation''', '''bottleneck''']
         for model_class in self.all_model_classes:
              for layer_type in layers_type:
                   UpperCAmelCase_     :				List[str]  =     layer_type
                   UpperCAmelCase_     :				Tuple  =     True
                   check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   UpperCAmelCase_     :				Optional[Any]  =     True

                   check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    @unittest.skip(reason='''Bit does not use feedforward chunking'''    )
    def 				a__ ( self    )					->						Dict:
         pass





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE    )





    @slow
    def 				a__ ( self    )					->						List[Any]:
         for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_     :				Optional[int]  =     BitModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @cached_property
    def 				a__ ( self    )					->						List[Any]:
         return (
             BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
         )





    @slow
    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     self.default_image_processor
         UpperCAmelCase_     :				Optional[int]  =     prepare_img()
         UpperCAmelCase_     :				Union[str, Any]  =     image_processor(images=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Any  =     model(**_SCREAMING_SNAKE_CASE    )

         # verify the logits
         UpperCAmelCase_     :				Dict  =     torch.Size((1, 1_000)    )
         self.assertEqual(outputs.logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Any  =     torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]    ).to(_SCREAMING_SNAKE_CASE    )

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


@require_torch
class     __a(	_lowerCAmelCase							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (BitBackbone,) if is_torch_available() else ()
    lowerCAmelCase         = BitConfig

    lowerCAmelCase         = False





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				List[Any]  =     BitModelTester(self    )







'''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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							List[str]         =   {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

__UpperCamelCase   :							List[str]         =   {
    """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""",
    },
}

__UpperCamelCase   :							Union[str, Any]         =   {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      List[str]:

   """simple docstring"""
   __a					  =     set()
   __a					  =     word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)     )
      __a					  =     char

   __a					  =     set(SCREAMING_SNAKE_CASE__     )
   return pairs



class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =VOCAB_FILES_NAMES
      __a	       =PRETRAINED_VOCAB_FILES_MAP
      __a	       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase="<s>"			, lowerCamelCase="</s>"			, lowerCamelCase="</s>"			, lowerCamelCase="<s>"			, lowerCamelCase="<unk>"			, lowerCamelCase="<pad>"			, lowerCamelCase="<mask>"			, **lowerCamelCase			, )				->str:



         '''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			, )

         __a					  =     vocab_file
         __a					  =     merges_file

         __a					  =     {}
         __a					  =     0
         __a					  =     1
         __a					  =     2
         __a					  =     3

         self.add_from_file(lowerCamelCase   )

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

         with open(lowerCamelCase			, encoding='utf-8'   ) as merges_handle:
            __a					  =     merges_handle.read().split('\n'   )[:-1]
         __a					  =     [tuple(merge.split()[:-1]   ) for merge in merges]
         __a					  =     dict(zip(lowerCamelCase			, range(len(lowerCamelCase   )   )   )   )
         __a					  =     {}




      def 			__UpperCamelCase				(	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]
         __a					  =     [self.cls_token_id]
         __a					  =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def 			__UpperCamelCase				(	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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->List[int]:



         '''simple docstring'''
         __a					  =     [self.sep_token_id]
         __a					  =     [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 			__UpperCamelCase				(	self   )				->Union[str, Any]:



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




      def 			__UpperCamelCase				(	self   )				->Optional[int]:



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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         if token in self.cache:
            return self.cache[token]
         __a					  =     tuple(lowerCamelCase   )
         __a					  =     tuple(list(word[:-1]   ) + [word[-1] + '</w>']   )
         __a					  =     get_pairs(lowerCamelCase   )

         if not pairs:
            return token

         while True:
            __a					  =     min(lowerCamelCase			, key=lambda lowerCamelCase   : self.bpe_ranks.get(lowerCamelCase			, float('inf'   )   )   )
            if bigram not in self.bpe_ranks:
               break
            __a  ,      __a					  =     bigram
            __a					  =     []
            __a					  =     0
            while i < len(lowerCamelCase   ):
               try:
                  __a					  =     word.index(lowerCamelCase			, lowerCamelCase   )
               except ValueError:
                  new_word.extend(word[i:]   )
                  break
               else:
                  new_word.extend(word[i:j]   )
                  __a					  =     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
            __a					  =     tuple(lowerCamelCase   )
            __a					  =     new_word
            if len(lowerCamelCase   ) == 1:
               break
            else:
               __a					  =     get_pairs(lowerCamelCase   )
         __a					  =     '@@ '.join(lowerCamelCase   )
         __a					  =     word[:-4]
         __a					  =     word
         return word




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     []

         __a					  =     re.findall(r'\S+\n?'			, lowerCamelCase   )

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[Any]:



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




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return self.decoder.get(lowerCamelCase			, self.unk_token   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     ' '.join(lowerCamelCase   ).replace('@@ '			, ''   ).strip()
         return out_string




      def 			__UpperCamelCase				(	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
         __a					  =     os.path.join(
             lowerCamelCase			, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
         __a					  =     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 			__UpperCamelCase				(	self			, lowerCamelCase   )				->str:



         '''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

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