






from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       AutoTokenizer,
       TFAutoModelForCausalLM,
       TFAutoModelForSeqaSeqLM,
       TFAutoModelForSpeechSeqaSeq,
       TFAutoModelForVisionaSeq,
       TFBartForConditionalGeneration,
       TFLogitsProcessorList,
       TFMinLengthLogitsProcessor,
       tf_top_k_top_p_filtering,
   )

if is_tensorflow_text_available():
   import tensorflow_text as text


@require_tf
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tf.convert_to_tensor(
           [
               [
                   8.2_220_991,  # 3rd highest value; idx. 0
                   -0.5_620_044,
                   5.23_229_752,
                   4.0_386_393,
                   -6.8_798_378,
                   -0.54_785_802,
                   -3.2_012_153,
                   2.92_777_176,
                   1.88_171_953,
                   7.35_341_276,  # 5th highest value; idx. 9
                   8.43_207_833,  # 2nd highest value; idx. 10
                   -9.85_711_836,
                   -5.96_209_236,
                   -1.13_039_161,
                   -7.1_115_294,
                   -0.8_369_633,
                   -5.3_186_408,
                   7.06_427_407,
                   0.81_369_344,
                   -0.82_023_817,
                   -5.9_179_796,
                   0.58_813_443,
                   -6.99_778_438,
                   4.71_551_189,
                   -0.18_771_637,
                   7.44_020_759,  # 4th highest value; idx. 25
                   9.38_450_987,  # 1st highest value; idx. 26
                   2.12_662_941,
                   -9.32_562_038,
                   2.35_652_522,
               ],  # cummulative prob of 5 highest values <= 0.6
               [
                   0.58_425_518,
                   4.53_139_238,
                   -5.57_510_464,
                   -6.28_030_699,
                   -7.19_529_503,
                   -4.02_122_551,
                   1.39_337_037,
                   -6.06_707_057,
                   1.59_480_517,
                   -9.643_119,
                   0.03_907_799,
                   0.67_231_762,
                   -8.88_206_726,
                   6.27_115_922,  # 4th highest value; idx. 13
                   2.28_520_723,
                   4.82_767_506,
                   4.30_421_368,
                   8.8_275_313,  # 2nd highest value; idx. 17
                   5.44_029_958,  # 5th highest value; idx. 18
                   -4.4_735_794,
                   7.38_579_536,  # 3rd highest value; idx. 20
                   -2.91_051_663,
                   2.61_946_077,
                   -2.5_674_762,
                   -9.48_959_302,
                   -4.02_922_645,
                   -1.35_416_918,
                   9.67_702_323,  # 1st highest value; idx. 27
                   -5.89_478_553,
                   1.85_370_467,
               ],  # cummulative prob of 5 highest values <= 0.6
           ]       ,			dtype=tf.floataa       ,			)

       SCREAMING_SNAKE_CASE							:   Tuple						    =     tf.convert_to_tensor(
           [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]       ,			dtype=tf.intaa       ,			)  # expected non filtered idx as noted above

       SCREAMING_SNAKE_CASE							:   List[str]						    =     tf.convert_to_tensor(
           [8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023]       ,			dtype=tf.floataa       ,			)  # expected non filtered values as noted above

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tf_top_k_top_p_filtering(UpperCAmelCase_       ,			top_k=10       ,			top_p=0.6       ,			min_tokens_to_keep=4   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     output[output != -float("inf"   )]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tf.cast(
           tf.where(tf.not_equal(UpperCAmelCase_       ,			tf.constant(-float("inf"   )       ,			dtype=tf.floataa   )   )   )       ,			dtype=tf.intaa       ,			)

       tf.debugging.assert_near(UpperCAmelCase_       ,			UpperCAmelCase_       ,			rtol=1E-12   )
       tf.debugging.assert_equal(UpperCAmelCase_       ,			UpperCAmelCase_   )





@require_tf
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase   ,		lowerCAmelCase       ):
     '''simple docstring'''


     if is_tf_available():
          UpperCamelCase_				:		List[str]								=						{
              '''AutoModelForCausalLM''': TFAutoModelForCausalLM,
              '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
              '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
              '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
              '''LogitsProcessorList''': TFLogitsProcessorList,
              '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
              '''create_tensor_fn''': tf.convert_to_tensor,
              '''floats_tensor''': floats_tensor,
              '''return_tensors''': '''tf''',
          }


     @slow
     def 					_A							(					self							:						Tuple   ):
       # TF-only test: tf.saved_model export
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     2
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     2


       class 			SCREAMING_SNAKE_CASE      (      tf.Module       ):
            '''simple docstring'''


            def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						Dict   ):
              super(UpperCAmelCase_       ,			self   ).__init__()
              SCREAMING_SNAKE_CASE							:   List[Any]						    =     model






            @tf.function(
                input_signature=(
                    tf.TensorSpec((None, input_length)       ,			tf.intaa       ,			name="input_ids"   ),
                    tf.TensorSpec((None, input_length)       ,			tf.intaa       ,			name="attention_mask"   ),
                )       ,			jit_compile=UpperCAmelCase_       ,			)
            def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]   ):
              SCREAMING_SNAKE_CASE							:   Dict						    =     self.model.generate(
                  input_ids=UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			max_new_tokens=UpperCAmelCase_       ,			return_dict_in_generate=UpperCAmelCase_       ,			)
              return {"sequences": outputs["sequences"]}





       SCREAMING_SNAKE_CASE							:   int						    =     [[2, 0], [102, 103]]
       SCREAMING_SNAKE_CASE							:   Tuple						    =     [[1, 0], [1, 1]]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     DummyModel(model=UpperCAmelCase_   )
       with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(UpperCAmelCase_       ,			UpperCAmelCase_       ,			signatures={"serving_default": dummy_model.serving}   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.saved_model.load(UpperCAmelCase_   ).signatures["serving_default"]
         for batch_size in range(1       ,			len(UpperCAmelCase_   ) + 1   ):
           SCREAMING_SNAKE_CASE							:   Tuple						    =     {
               "input_ids": tf.constant(dummy_input_ids[:batch_size]   ),
               "attention_mask": tf.constant(dummy_attention_masks[:batch_size]   ),
           }
           SCREAMING_SNAKE_CASE							:   List[Any]						    =     serving_func(**UpperCAmelCase_   )["sequences"]
           SCREAMING_SNAKE_CASE							:   Tuple						    =     test_model.generate(**UpperCAmelCase_       ,			max_new_tokens=UpperCAmelCase_   )
           tf.debugging.assert_equal(UpperCAmelCase_       ,			UpperCAmelCase_   )


     @slow
     def 					_A							(					self							:						Tuple   ):
       # TF-only test: tf.saved_model export
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     1
       SCREAMING_SNAKE_CASE							:   Dict						    =     2


       class 			SCREAMING_SNAKE_CASE      (      tf.Module       ):
            '''simple docstring'''


            def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						int   ):
              super(UpperCAmelCase_       ,			self   ).__init__()
              SCREAMING_SNAKE_CASE							:   List[Any]						    =     model






            @tf.function(
                input_signature=(
                    tf.TensorSpec((batch_size, None)       ,			tf.intaa       ,			name="input_ids"   ),
                    tf.TensorSpec((batch_size, None)       ,			tf.intaa       ,			name="attention_mask"   ),
                )       ,			jit_compile=UpperCAmelCase_       ,			)
            def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						List[Any]   ):
              SCREAMING_SNAKE_CASE							:   str						    =     self.model.generate(
                  input_ids=UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			max_new_tokens=UpperCAmelCase_       ,			return_dict_in_generate=UpperCAmelCase_       ,			)
              return {"sequences": outputs["sequences"]}





       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     [[2], [102, 103]]
       SCREAMING_SNAKE_CASE							:   Dict						    =     [[1], [1, 1]]
       SCREAMING_SNAKE_CASE							:   str						    =     DummyModel(model=UpperCAmelCase_   )
       with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(UpperCAmelCase_       ,			UpperCAmelCase_       ,			signatures={"serving_default": dummy_model.serving}   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.saved_model.load(UpperCAmelCase_   ).signatures["serving_default"]
         for input_row in range(len(UpperCAmelCase_   )   ):
           SCREAMING_SNAKE_CASE							:   Any						    =     {
               "input_ids": tf.constant([dummy_input_ids[input_row]]   ),
               "attention_mask": tf.constant([dummy_attention_masks[input_row]]   ),
           }
           SCREAMING_SNAKE_CASE							:   List[str]						    =     serving_func(**UpperCAmelCase_   )["sequences"]
           SCREAMING_SNAKE_CASE							:   List[str]						    =     test_model.generate(**UpperCAmelCase_       ,			max_new_tokens=UpperCAmelCase_   )
           tf.debugging.assert_equal(UpperCAmelCase_       ,			UpperCAmelCase_   )


     @slow
     @require_tensorflow_text
     def 					_A							(					self							:						Any   ):
       # TF-only test: tf.saved_model export
       with tempfile.TemporaryDirectory() as tmp_dir:
         # file needed to load the TF tokenizer
         hf_hub_download(repo_id="google/flan-t5-small"       ,			filename="spiece.model"       ,			local_dir=UpperCAmelCase_   )


         class 			SCREAMING_SNAKE_CASE      (      tf.keras.layers.Layer       ):
              '''simple docstring'''


              def __init__(					self							:						int   ):
                super().__init__()
                SCREAMING_SNAKE_CASE							:   List[str]						    =     text.SentencepieceTokenizer(
                    model=tf.io.gfile.GFile(os.path.join(UpperCAmelCase_       ,			"spiece.model"   )       ,			"rb"   ).read()   )
                SCREAMING_SNAKE_CASE							:   List[Any]						    =     TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5"   )






              def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Tuple       ,			*UpperCAmelCase_							:						Union[str, Any]       ,			**UpperCAmelCase_							:						int   ):
                SCREAMING_SNAKE_CASE							:   Dict						    =     self.tokenizer.tokenize(UpperCAmelCase_   )
                SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     text.pad_model_inputs(
                    UpperCAmelCase_       ,			max_seq_length=64       ,			pad_value=self.model.config.pad_token_id   )
                SCREAMING_SNAKE_CASE							:   str						    =     self.model.generate(input_ids=UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_   )
                return self.tokenizer.detokenize(UpperCAmelCase_   )





         SCREAMING_SNAKE_CASE							:   List[Any]						    =     CompleteSentenceTransformer()
         SCREAMING_SNAKE_CASE							:   List[str]						    =     tf.keras.layers.Input(shape=(1,)       ,			dtype=tf.string       ,			name="inputs"   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     complete_model(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     tf.keras.Model(UpperCAmelCase_       ,			UpperCAmelCase_   )
         keras_model.save(UpperCAmelCase_   )


     def 					_A							(					self							:						Any   ):
       # Has PT equivalent: this test relies on random sampling
       SCREAMING_SNAKE_CASE							:   List[str]						    =     {
           "do_sample": True,
           "num_beams": 1,
           "top_p": 0.7,
           "top_k": 10,
           "temperature": 0.7,
       }
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     14

       SCREAMING_SNAKE_CASE							:   str						    =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
       SCREAMING_SNAKE_CASE							:   str						    =     "Hello, my dog is cute and"
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tokenizer(UpperCAmelCase_       ,			return_tensors="tf"   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )

       SCREAMING_SNAKE_CASE							:   Any						    =     638
       # forces the generation to happen on CPU, to avoid GPU-related quirks
       with tf.device(":/CPU:0"   ):
         tf.random.set_seed(0   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model.generate(**UpperCAmelCase_       ,			eos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )
       self.assertTrue(expectation == len(generated_tokens[0]   )   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     [638, 198]
       with tf.device(":/CPU:0"   ):
         tf.random.set_seed(0   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     model.generate(**UpperCAmelCase_       ,			eos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )
       self.assertTrue(expectation == len(generated_tokens[0]   )   )






     def 					_A							(					self							:						Tuple   ):
       # Has PT equivalent: ample use of framework-specific code
       SCREAMING_SNAKE_CASE							:   Dict						    =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart"   )
       SCREAMING_SNAKE_CASE							:   str						    =     "Hugging Face is a technology company based in New York and Paris."
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     bart_tokenizer(UpperCAmelCase_       ,			return_tensors="tf"   ).input_ids
       SCREAMING_SNAKE_CASE							:   Tuple						    =     TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     bart_model.generate(UpperCAmelCase_   ).numpy()


       class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
            '''simple docstring'''


            def 					_A							(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						List[str]=None       ,			**UpperCAmelCase_							:						Optional[Any]   ):
              return super().call(UpperCAmelCase_       ,			**UpperCAmelCase_   )





       SCREAMING_SNAKE_CASE							:   int						    =     FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     bart_model.generate(UpperCAmelCase_       ,			foo="bar"   ).numpy()
       self.assertTrue(np.array_equal(UpperCAmelCase_       ,			UpperCAmelCase_   )   )


       class 			SCREAMING_SNAKE_CASE      (      bart_model.model.encoder.__class__       ):
            '''simple docstring'''


            def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Dict       ,			**UpperCAmelCase_							:						Union[str, Any]   ):
              return super().call(UpperCAmelCase_       ,			**UpperCAmelCase_   )





       SCREAMING_SNAKE_CASE							:   List[str]						    =     FakeEncoder(bart_model.config       ,			bart_model.model.shared   )
       SCREAMING_SNAKE_CASE							:   Any						    =     fake_encoder

       # Normal generation still works (the output will be different because the encoder weights are different)
       SCREAMING_SNAKE_CASE							:   Any						    =     bart_model.generate(UpperCAmelCase_   ).numpy()
       with self.assertRaises(UpperCAmelCase_   ):
         # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
         bart_model.generate(UpperCAmelCase_       ,			foo="bar"   )









"""simple docstring"""





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

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


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,remove_space=lowercase_  ,keep_accents=lowercase_  ,bos_token=lowercase_  ,eos_token=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		List[Any]    							=      do_lower_case
						lowerCAmelCase__      :		str    							=      remove_space
						lowerCAmelCase__      :		Optional[int]    							=      keep_accents
						lowerCAmelCase__      :		Dict    							=      vocab_file
						lowerCAmelCase__      :		Optional[int]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [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						) * [0] + len(token_ids_a + sep						) * [1]







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = 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(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

						return (out_vocab_file,)




