

'''simple docstring'''





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


A_	= random.Random()







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase=1.0    ,__UpperCamelCase=None    ,__UpperCamelCase=None	)       ->			Optional[Any]:


					if rng is None:
										lowerCamelCase_  				=      global_rng

					lowerCamelCase_  				=      []
					for batch_idx in range(shape[0]	):
										values.append([]	)
										for _ in range(shape[1]	):
															values[-1].append(rng.random() * scale	)

					return values


@require_torch
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=7   ,			SCREAMING_SNAKE_CASE_=400   ,			SCREAMING_SNAKE_CASE_=2000   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=16000   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=80   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=64   ,			SCREAMING_SNAKE_CASE_="hann_window"   ,			SCREAMING_SNAKE_CASE_=80   ,			SCREAMING_SNAKE_CASE_=7600   ,			SCREAMING_SNAKE_CASE_=1E-10   ,			SCREAMING_SNAKE_CASE_=True   ,			)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      min_seq_length
							lowerCamelCase_  				=      max_seq_length
							lowerCamelCase_  				=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCamelCase_  				=      feature_size
							lowerCamelCase_  				=      padding_value
							lowerCamelCase_  				=      sampling_rate
							lowerCamelCase_  				=      do_normalize
							lowerCamelCase_  				=      num_mel_bins
							lowerCamelCase_  				=      hop_length
							lowerCamelCase_  				=      win_length
							lowerCamelCase_  				=      win_function
							lowerCamelCase_  				=      fmin
							lowerCamelCase_  				=      fmax
							lowerCamelCase_  				=      mel_floor
							lowerCamelCase_  				=      return_attention_mask
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "do_normalize": self.do_normalize,
							    "num_mel_bins": self.num_mel_bins,
							    "hop_length": self.hop_length,
							    "win_length": self.win_length,
							    "win_function": self.win_function,
							    "fmin": self.fmin,
							    "fmax": self.fmax,
							    "mel_floor": self.mel_floor,
							    "return_attention_mask": self.return_attention_mask,
							}
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False		)  ->     List[str]:






							'''simple docstring'''



							def _flatten(SCREAMING_SNAKE_CASE_		):
												return list(itertools.chain(*lowercase__		)		)

							if equal_length:
												lowerCamelCase_  				=      floats_list((self.batch_size, self.max_seq_length)		)
							else:
												# make sure that inputs increase in size
												lowerCamelCase_  				=      [
												    _flatten(floats_list((x, self.feature_size)		)		)
												    for x in range(self.min_seq_length   ,			self.max_seq_length   ,			self.seq_length_diff		)
												]

							if numpify:
												lowerCamelCase_  				=      [np.asarray(lowercase__		) for x in speech_inputs]

							return speech_inputs






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False		)  ->     Tuple:






							'''simple docstring'''



							if equal_length:
												lowerCamelCase_  				=      [floats_list((self.max_seq_length, self.num_mel_bins)		) for _ in range(self.batch_size		)]
							else:
												# make sure that inputs increase in size
												lowerCamelCase_  				=      [
												    floats_list((x, self.num_mel_bins)		)
												    for x in range(self.min_seq_length   ,			self.max_seq_length   ,			self.seq_length_diff		)
												]

							if numpify:
												lowerCamelCase_  				=      [np.asarray(lowercase__		) for x in speech_inputs]

							return speech_inputs


@require_torch
class  UpperCAmelCase (							snake_case_  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					SpeechTaFeatureExtractor
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      SpeechTaFeatureExtractionTester(self		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							self.assertTrue(np.all(np.mean(lowercase__   ,			axis=0		) < 1E-3		)		)
							self.assertTrue(np.all(np.abs(np.var(lowercase__   ,			axis=0		) - 1		) < 1E-3		)		)
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							# create three inputs of length 800, 1000, and 1200
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]
							lowerCamelCase_  				=      [np.asarray(lowercase__		) for speech_input in speech_inputs]

							# Test not batched input
							lowerCamelCase_  				=      feat_extract(speech_inputs[0]   ,			return_tensors='np'		).input_values
							lowerCamelCase_  				=      feat_extract(np_speech_inputs[0]   ,			return_tensors='np'		).input_values
							self.assertTrue(np.allclose(lowercase__   ,			lowercase__   ,			atol=1E-3		)		)

							# Test batched
							lowerCamelCase_  				=      feat_extract(lowercase__   ,			return_tensors='np'		).input_values
							lowerCamelCase_  				=      feat_extract(lowercase__   ,			return_tensors='np'		).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__   ,			lowercase__		):
												self.assertTrue(np.allclose(lowercase__   ,			lowercase__   ,			atol=1E-3		)		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]

							lowerCamelCase_  				=      ["""longest""", """max_length""", """do_not_pad"""]
							lowerCamelCase_  				=      [None, 1600, None]
							for max_length, padding in zip(lowercase__   ,			lowercase__		):
												lowerCamelCase_  				=      feat_extract(lowercase__   ,			padding=lowercase__   ,			max_length=lowercase__   ,			return_tensors='np'		)
												lowerCamelCase_  				=      processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]		)
												self.assertTrue(input_values[0][800:].sum() < 1E-6		)
												self._check_zero_mean_unit_variance(input_values[1][:1000]		)
												self.assertTrue(input_values[0][1000:].sum() < 1E-6		)
												self._check_zero_mean_unit_variance(input_values[2][:1200]		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							lowerCamelCase_  				=      range(800   ,			1400   ,			200		)
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in lengths]

							lowerCamelCase_  				=      ["""longest""", """max_length""", """do_not_pad"""]
							lowerCamelCase_  				=      [None, 1600, None]

							for max_length, padding in zip(lowercase__   ,			lowercase__		):
												lowerCamelCase_  				=      feat_extract(lowercase__   ,			max_length=lowercase__   ,			padding=lowercase__		)
												lowerCamelCase_  				=      processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]		)
												self._check_zero_mean_unit_variance(input_values[1][:1000]		)
												self._check_zero_mean_unit_variance(input_values[2][:1200]		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]
							lowerCamelCase_  				=      feat_extract(
							    lowercase__   ,			truncation=lowercase__   ,			max_length=1000   ,			padding='max_length'   ,			return_tensors='np'		)
							lowerCamelCase_  				=      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]		)
							self._check_zero_mean_unit_variance(input_values[1]		)
							self._check_zero_mean_unit_variance(input_values[2]		)
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]
							lowerCamelCase_  				=      feat_extract(
							    lowercase__   ,			truncation=lowercase__   ,			max_length=1000   ,			padding='longest'   ,			return_tensors='np'		)
							lowerCamelCase_  				=      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]		)
							self._check_zero_mean_unit_variance(input_values[1, :1000]		)
							self._check_zero_mean_unit_variance(input_values[2]		)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1000)		)

							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]
							lowerCamelCase_  				=      feat_extract(
							    lowercase__   ,			truncation=lowercase__   ,			max_length=2000   ,			padding='longest'   ,			return_tensors='np'		)
							lowerCamelCase_  				=      processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]		)
							self._check_zero_mean_unit_variance(input_values[1, :1000]		)
							self._check_zero_mean_unit_variance(input_values[2]		)

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1200)		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							lowerCamelCase_  				=      np.random.rand(100		).astype(np.floataa		)
							lowerCamelCase_  				=      np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
												lowerCamelCase_  				=      feature_extractor.pad([{'input_values': inputs}]   ,			return_tensors='np'		)
												self.assertTrue(np_processed.input_values.dtype == np.floataa		)
												lowerCamelCase_  				=      feature_extractor.pad([{'input_values': inputs}]   ,			return_tensors='pt'		)
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							# create three inputs of length 800, 1000, and 1200
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in range(800   ,			1400   ,			200		)]
							lowerCamelCase_  				=      [np.asarray(lowercase__		) for speech_input in speech_inputs]

							# Test feature size
							lowerCamelCase_  				=      feature_extractor(audio_target=lowercase__   ,			padding=lowercase__   ,			return_tensors='np'		).input_values
							self.assertTrue(input_values.ndim == 3		)
							self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins		)

							# Test not batched input
							lowerCamelCase_  				=      feature_extractor(speech_inputs[0]   ,			return_tensors='np'		).input_values
							lowerCamelCase_  				=      feature_extractor(np_speech_inputs[0]   ,			return_tensors='np'		).input_values
							self.assertTrue(np.allclose(lowercase__   ,			lowercase__   ,			atol=1E-3		)		)

							# Test batched
							lowerCamelCase_  				=      feature_extractor(lowercase__   ,			return_tensors='np'		).input_values
							lowerCamelCase_  				=      feature_extractor(lowercase__   ,			return_tensors='np'		).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__   ,			lowercase__		):
												self.assertTrue(np.allclose(lowercase__   ,			lowercase__   ,			atol=1E-3		)		)

							# Test 2-D numpy arrays are batched.
							lowerCamelCase_  				=      [floats_list((1, x)		)[0] for x in (800, 800, 800)]
							lowerCamelCase_  				=      np.asarray(lowercase__		)
							lowerCamelCase_  				=      feature_extractor(lowercase__   ,			return_tensors='np'		).input_values
							lowerCamelCase_  				=      feature_extractor(lowercase__   ,			return_tensors='np'		).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__   ,			lowercase__		):
												self.assertTrue(np.allclose(lowercase__   ,			lowercase__   ,			atol=1E-3		)		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target()
							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_dict		)
							lowerCamelCase_  				=      feat_extract.model_input_names[0]

							lowerCamelCase_  				=      BatchFeature({input_name: speech_inputs}		)

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

							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__		)
							lowerCamelCase_  				=      BatchFeature({input_name: speech_inputs}   ,			tensor_type='np'		)

							lowerCamelCase_  				=      processed_features[input_name]

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

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__		)
							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_dict		)
							lowerCamelCase_  				=      feat_extract.model_input_names[0]

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

							lowerCamelCase_  				=      processed_features[input_name]

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

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.num_mel_bins)		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feature_extraction_class(**self.feat_extract_dict		)
							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target()
							lowerCamelCase_  				=      feat_extract.model_input_names[0]

							lowerCamelCase_  				=      BatchFeature({input_name: speech_inputs}		)

							lowerCamelCase_  				=      feat_extract.num_mel_bins  # hack!

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

							self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feat_extract_dict
							lowerCamelCase_  				=      True
							lowerCamelCase_  				=      self.feature_extraction_class(**lowercase__		)
							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target()
							lowerCamelCase_  				=      [len(lowercase__		) for x in speech_inputs]
							lowerCamelCase_  				=      feat_extract.model_input_names[0]

							lowerCamelCase_  				=      BatchFeature({input_name: speech_inputs}		)

							lowerCamelCase_  				=      feat_extract.num_mel_bins  # hack!

							lowerCamelCase_  				=      feat_extract.pad(lowercase__   ,			padding='longest'   ,			return_tensors='np'		)
							self.assertIn('attention_mask'   ,			lowercase__		)
							self.assertListEqual(list(processed.attention_mask.shape		)   ,			list(processed[input_name].shape[:2]		)		)
							self.assertListEqual(processed.attention_mask.sum(-1		).tolist()   ,			lowercase__		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.feat_extract_dict
							lowerCamelCase_  				=      True
							lowerCamelCase_  				=      self.feature_extraction_class(**lowercase__		)
							lowerCamelCase_  				=      self.feat_extract_tester.prepare_inputs_for_target()
							lowerCamelCase_  				=      [len(lowercase__		) for x in speech_inputs]
							lowerCamelCase_  				=      feat_extract.model_input_names[0]

							lowerCamelCase_  				=      BatchFeature({input_name: speech_inputs}		)
							lowerCamelCase_  				=      min(lowercase__		)

							lowerCamelCase_  				=      feat_extract.num_mel_bins  # hack!

							lowerCamelCase_  				=      feat_extract.pad(
							    lowercase__   ,			padding='max_length'   ,			max_length=lowercase__   ,			truncation=lowercase__   ,			return_tensors='np'		)
							self.assertIn('attention_mask'   ,			lowercase__		)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape		)   ,			[processed_pad[input_name].shape[0], max_length]		)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()   ,			[max_length for x in speech_inputs]		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							from datasets import load_dataset

							lowerCamelCase_  				=      load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,			'clean'   ,			split='validation'		)
							# automatic decoding with librispeech
							lowerCamelCase_  				=      ds.sort('id'		).select(range(lowercase__		)		)[:num_samples]["""audio"""]

							return [x["array"] for x in speech_samples]
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]		)
							# fmt: on

							lowerCamelCase_  				=      self._load_datasamples(1		)
							lowerCamelCase_  				=      SpeechTaFeatureExtractor()
							lowerCamelCase_  				=      feature_extractor(lowercase__   ,			return_tensors='pt'		).input_values
							self.assertEquals(input_values.shape   ,			(1, 93680)		)
							self.assertTrue(torch.allclose(input_values[0, :30]   ,			lowercase__   ,			atol=1E-6		)		)






		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      torch.tensor(
							    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
							     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
							     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
							     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]		)
							# fmt: on

							lowerCamelCase_  				=      self._load_datasamples(1		)
							lowerCamelCase_  				=      SpeechTaFeatureExtractor()
							lowerCamelCase_  				=      feature_extractor(audio_target=lowercase__   ,			return_tensors='pt'		).input_values
							self.assertEquals(input_values.shape   ,			(1, 366, 80)		)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,			lowercase__   ,			atol=1E-4		)		)









'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
