

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFEfficientFormerForImageClassification,
     TFEfficientFormerForImageClassificationWithTeacher,
     TFEfficientFormerModel,
 )
 from transformers.models.efficientformer.modeling_tf_efficientformer import (
     TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )


if is_vision_available():
 from PIL import Image

 from transformers import EfficientFormerImageProcessor



class 		a_       :



      """simple docstring"""







      def __init__(      self     :							int			,snake_case     :							Optional[Any]			,snake_case     :							List[Any] = 13			,snake_case     :							Dict = 64			,snake_case     :							str = 2			,snake_case     :							Tuple = 3			,snake_case     :							Any = 3			,snake_case     :							str = True			,snake_case     :							Optional[Any] = True			,snake_case     :							str = 128			,snake_case     :							Optional[int]=[16, 32, 64, 128]			,snake_case     :							Optional[Any] = 7			,snake_case     :							Optional[Any] = 4			,snake_case     :							List[Any] = 37			,snake_case     :							List[str] = "gelu"			,snake_case     :							Optional[Any] = 0.1			,snake_case     :							Tuple = 0.1			,snake_case     :							str = 10			,snake_case     :							List[Any] = 0.02			,snake_case     :							int = 2			,snake_case     :							int = 1			,snake_case     :							List[str] = 128			,snake_case     :							int = [2, 2, 2, 2]			,snake_case     :							Optional[int] = 2			,snake_case     :							Tuple = 2			,):
           SCREAMING_SNAKE_CASE      						=parent
           SCREAMING_SNAKE_CASE      						=batch_size
           SCREAMING_SNAKE_CASE      						=image_size
           SCREAMING_SNAKE_CASE      						=patch_size
           SCREAMING_SNAKE_CASE      						=num_channels
           SCREAMING_SNAKE_CASE      						=is_training
           SCREAMING_SNAKE_CASE      						=use_labels
           SCREAMING_SNAKE_CASE      						=hidden_size
           SCREAMING_SNAKE_CASE      						=num_hidden_layers
           SCREAMING_SNAKE_CASE      						=num_attention_heads
           SCREAMING_SNAKE_CASE      						=intermediate_size
           SCREAMING_SNAKE_CASE      						=hidden_act
           SCREAMING_SNAKE_CASE      						=hidden_dropout_prob
           SCREAMING_SNAKE_CASE      						=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE      						=type_sequence_label_size
           SCREAMING_SNAKE_CASE      						=initializer_range
           SCREAMING_SNAKE_CASE      						=encoder_stride
           SCREAMING_SNAKE_CASE      						=num_attention_outputs
           SCREAMING_SNAKE_CASE      						=embed_dim
           SCREAMING_SNAKE_CASE      						=embed_dim + 1
           SCREAMING_SNAKE_CASE      						=resolution
           SCREAMING_SNAKE_CASE      						=depths
           SCREAMING_SNAKE_CASE      						=hidden_sizes
           SCREAMING_SNAKE_CASE      						=dim
           SCREAMING_SNAKE_CASE      						=mlp_expansion_ratio
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           SCREAMING_SNAKE_CASE      						=None
           if self.use_labels:
                SCREAMING_SNAKE_CASE      						=ids_tensor([self.batch_size]			,self.type_sequence_label_size				)

           SCREAMING_SNAKE_CASE      						=self.get_config()

           return config, pixel_values, labels
      def        _lowerCAmelCase     (      self     :							Any				):
           return EfficientFormerConfig(
               image_size=self.image_size			,patch_size=self.patch_size			,num_channels=self.num_channels			,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			,is_decoder=snake_case			,initializer_range=self.initializer_range			,encoder_stride=self.encoder_stride			,resolution=self.resolution			,depths=self.depths			,hidden_sizes=self.hidden_sizes			,dim=self.dim			,mlp_expansion_ratio=self.mlp_expansion_ratio			,)
      def        _lowerCAmelCase     (      self     :							Tuple			,snake_case     :							Tuple			,snake_case     :							Union[str, Any]			,snake_case     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerModel(config=snake_case				)
           SCREAMING_SNAKE_CASE      						=model(snake_case			,training=snake_case				)
           self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)				)
      def        _lowerCAmelCase     (      self     :							Any			,snake_case     :							Optional[Any]			,snake_case     :							str			,snake_case     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=self.type_sequence_label_size
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerForImageClassification(snake_case				)
           SCREAMING_SNAKE_CASE      						=model(snake_case			,labels=snake_case			,training=snake_case				)
           self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.type_sequence_label_size)				)

           # test greyscale images
           SCREAMING_SNAKE_CASE      						=1
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerForImageClassification(snake_case				)

           SCREAMING_SNAKE_CASE      						=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
           SCREAMING_SNAKE_CASE      						=model(snake_case			,labels=snake_case				)
           self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.type_sequence_label_size)				)


      def        _lowerCAmelCase     (      self     :							List[str]				):
           SCREAMING_SNAKE_CASE      						=self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE      						=config_and_inputs
           SCREAMING_SNAKE_CASE      						={'pixel_values': pixel_values}
           return config, inputs_dict







@require_tf
class 		a_       (							A_ ,			A_ ,			unittest.TestCase     ):



      """simple docstring"""







      __UpperCAmelCase					=      (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase					=      (
          {
              '''feature-extraction''': TFEfficientFormerModel,
              '''image-classification''': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      __UpperCAmelCase					=      False

      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      __UpperCAmelCase					=      False
      def        _lowerCAmelCase     (      self     :							Dict				):
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerModelTester(self				)
           SCREAMING_SNAKE_CASE      						=ConfigTester(
               self			,config_class=snake_case			,has_text_modality=snake_case			,hidden_size=37				)
      def        _lowerCAmelCase     (      self     :							List[str]				):
           self.config_tester.run_common_tests()
      @unittest.skip(reason='EfficientFormer does not use inputs_embeds'				)
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           pass
      @unittest.skip(reason='EfficientFormer does not support input and output embeddings'				)
      def        _lowerCAmelCase     (      self     :							Any				):
           pass
      def        _lowerCAmelCase     (      self     :							List[str]				):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE      						=model_class(snake_case				)
                SCREAMING_SNAKE_CASE      						=inspect.signature(model.call				)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                SCREAMING_SNAKE_CASE      						=[*signature.parameters.keys()]

                SCREAMING_SNAKE_CASE      						=['pixel_values']
                self.assertListEqual(arg_names[:1]			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Tuple				):
           def check_hidden_states_output(snake_case     :							Optional[int]			,snake_case     :							Union[str, Any]			,snake_case     :							Optional[int]				):
                SCREAMING_SNAKE_CASE      						=model_class(snake_case				)

                SCREAMING_SNAKE_CASE      						=model(**self._prepare_for_class(snake_case			,snake_case				)			,training=snake_case				)
                SCREAMING_SNAKE_CASE      						=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                SCREAMING_SNAKE_CASE      						=getattr(
                    self.model_tester			,'expected_num_hidden_layers'			,self.model_tester.num_hidden_layers + 1				)
                self.assertEqual(len(snake_case				)			,snake_case				)

                if hasattr(self.model_tester			,'encoder_seq_length'				):
                     SCREAMING_SNAKE_CASE      						=self.model_tester.encoder_seq_length
                     if hasattr(self.model_tester			,'chunk_length'				) and self.model_tester.chunk_length > 1:
                          SCREAMING_SNAKE_CASE      						=seq_length * self.model_tester.chunk_length
                else:
                     SCREAMING_SNAKE_CASE      						=self.model_tester.seq_length

                self.assertListEqual(
                    list(hidden_states[-1].shape[-2:]				)			,[seq_length, self.model_tester.hidden_size]			,)

                if config.is_encoder_decoder:
                     SCREAMING_SNAKE_CASE      						=outputs.decoder_hidden_states

                     self.asseretIsInstance(snake_case			,(list, tuple)				)
                     self.assertEqual(len(snake_case				)			,snake_case				)
                     SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'seq_length'			,snake_case				)
                     SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'decoder_seq_length'			,snake_case				)

                     self.assertListEqual(
                         list(hidden_states[-1].shape[-2:]				)			,[decoder_seq_length, self.model_tester.hidden_size]			,)

           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE      						=True
                check_hidden_states_output(snake_case			,snake_case			,snake_case				)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                SCREAMING_SNAKE_CASE      						=True

                check_hidden_states_output(snake_case			,snake_case			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Any			,snake_case     :							List[str]			,snake_case     :							Tuple			,snake_case     :							int=False				):
           SCREAMING_SNAKE_CASE      						=super()._prepare_for_class(snake_case			,snake_case			,return_labels=snake_case				)

           if return_labels:
                if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                     del inputs_dict["labels"]

           return inputs_dict
      def        _lowerCAmelCase     (      self     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
      @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'				)
      def        _lowerCAmelCase     (      self     :							Any				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*snake_case				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
      @slow
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE      						=TFEfficientFormerModel.from_pretrained(snake_case				)
                self.assertIsNotNone(snake_case				)
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE      						=True

           SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'seq_length'			,snake_case				)
           SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'encoder_seq_length'			,snake_case				)
           SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'key_length'			,snake_case				)
           SCREAMING_SNAKE_CASE      						=getattr(self.model_tester			,'chunk_length'			,snake_case				)

           if chunk_length is not None and hasattr(self.model_tester			,'num_hashes'				):
                SCREAMING_SNAKE_CASE      						=encoder_seq_length * self.model_tester.num_hashes

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE      						=True
                SCREAMING_SNAKE_CASE      						=False
                SCREAMING_SNAKE_CASE      						=True
                SCREAMING_SNAKE_CASE      						=model_class(snake_case				)

                SCREAMING_SNAKE_CASE      						=model(**self._prepare_for_class(snake_case			,snake_case				)			,training=snake_case				)
                SCREAMING_SNAKE_CASE      						=outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                self.assertEqual(len(snake_case				)			,self.model_tester.num_attention_outputs				)

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                SCREAMING_SNAKE_CASE      						=True
                SCREAMING_SNAKE_CASE      						=model_class(snake_case				)
                SCREAMING_SNAKE_CASE      						=model(**self._prepare_for_class(snake_case			,snake_case				)			,training=snake_case				)

                SCREAMING_SNAKE_CASE      						=outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                self.assertEqual(len(snake_case				)			,self.model_tester.num_attention_outputs				)

                if chunk_length is not None:
                     self.assertListEqual(
                         list(attentions[0].shape[-4:]				)			,[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,)
                else:
                     self.assertListEqual(
                         list(attentions[0].shape[-3:]				)			,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,)


      def        _lowerCAmelCase     (      self     :							Any				):
           # We use a simplified version of this test for EfficientFormer because it requires training=False
           # and Keras refuses to let us force that during functional construction
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE      						=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                # Prepare our model
                SCREAMING_SNAKE_CASE      						=model_class(snake_case				)
                # These are maximally general inputs for the model, with multiple None dimensions
                # Hopefully this will catch any conditionals that fail for flexible shapes
                SCREAMING_SNAKE_CASE      						={
                    key: tf.keras.Input(shape=val.shape[1:]			,dtype=val.dtype			,name=snake_case				)
                    for key, val in model.input_signature.items()
                    if key in model.dummy_inputs
                }
                SCREAMING_SNAKE_CASE      						=model(snake_case				)
                self.assertTrue(outputs_dict is not None				)







def 			snake_case__				(   ):





     """simple docstring"""







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



@require_tf
@require_vision
class 		a_       (							unittest.TestCase     ):



      """simple docstring"""







      @cached_property
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           return (
               EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'				)
               if is_vision_available()
               else None
           )
      @slow
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'				)
           SCREAMING_SNAKE_CASE      						=self.default_image_processor
           SCREAMING_SNAKE_CASE      						=prepare_img()
           SCREAMING_SNAKE_CASE      						=image_processor(images=snake_case			,return_tensors='tf'				)
           # forward pass
           SCREAMING_SNAKE_CASE      						=model(**snake_case			,training=snake_case				)
           # verify the logits
           SCREAMING_SNAKE_CASE      						=tf.TensorShape((1, 1000)				)
           self.assertEqual(outputs.logits.shape			,snake_case				)
           SCREAMING_SNAKE_CASE      						=tf.constant([-0.0_555, 0.4_825, -0.0_852]				)
           self.assertTrue(np.allclose(outputs.logits[0, :3]			,snake_case			,atol=1e-4				)				)


      @slow
      def        _lowerCAmelCase     (      self     :							Optional[int]				):
           SCREAMING_SNAKE_CASE      						=TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
               'snap-research/efficientformer-l1-300'				)
           SCREAMING_SNAKE_CASE      						=self.default_image_processor
           SCREAMING_SNAKE_CASE      						=prepare_img()
           SCREAMING_SNAKE_CASE      						=image_processor(images=snake_case			,return_tensors='tf'				)
           # forward pass
           SCREAMING_SNAKE_CASE      						=model(**snake_case			,training=snake_case				)
           # verify the logits
           SCREAMING_SNAKE_CASE      						=tf.TensorShape((1, 1000)				)
           self.assertEqual(outputs.logits.shape			,snake_case				)
           SCREAMING_SNAKE_CASE      						=tf.constant([-0.1_312, 0.4_353, -1.0_499]				)
           self.assertTrue(np.allclose(outputs.logits[0, :3]			,snake_case			,atol=1e-4				)				)





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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	lowerCAmelCase           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase           =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase           =		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class       A    (   A_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[f'<extra_id_{i}>' for i in range(lowerCAmelCase					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase=			len(set(filter(lambda lowerCAmelCase					: bool('extra_id_' in str(lowerCAmelCase					)					)      ,				lowerCAmelCase					)					)					)
											if extra_tokens != extra_ids:
													raise ValueError(
													    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
													    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
													    ' tokens'					)

									super().__init__(
									    lowerCAmelCase      ,				tokenizer_file=lowerCAmelCase      ,				eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				extra_ids=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				**lowerCAmelCase      ,				)

									__lowercase=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
											elif init_max_model_length is None:
													warnings.warn(
													    'This tokenizer was incorrectly instantiated with a model max length of'
													    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
													    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
													    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
													    f' {pretrained_model_name_or_path} automatically truncating your input to'
													    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
													    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
													    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
													    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				lowerCAmelCase      ,				)

									return max_model_length


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'					)

									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']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
											logger.info(f'Copy vocab file to {out_vocab_file}'					)

									return (out_vocab_file,)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[self.eos_token_id]

									if token_ids_a is None:
											return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


							def 			_A       (self					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






