



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


SCREAMING_SNAKE_CASE__       =      random.Random()


def A						(       __UpperCamelCase ,	__UpperCamelCase=1.0 ,	__UpperCamelCase=None ,	__UpperCamelCase=None					)						->      Any:
   if rng is None:
      A__       =    global_rng

   A__       =    []
   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      __lowerCAmelCase     (		unittest.TestCase      ):
     """simple docstring"""






     def __init__(     self					:       Any				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Tuple=7				,					_snake_case					:       Tuple=4_00				,					_snake_case					:       Any=20_00				,					_snake_case					:       Dict=1				,					_snake_case					:       int=0.0				,					_snake_case					:       List[str]=1_60_00				,					_snake_case					:       Dict=True				,					_snake_case					:       Dict=80				,					_snake_case					:       List[str]=16				,					_snake_case					:       Union[str, Any]=64				,					_snake_case					:       Optional[Any]="hann_window"				,					_snake_case					:       Any=80				,					_snake_case					:       Tuple=76_00				,					_snake_case					:       Any=1E-10				,					_snake_case					:       str=True				,					):






        """simple docstring"""


        A__       =    parent
        A__       =    batch_size
        A__       =    min_seq_length
        A__       =    max_seq_length
        A__       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A__       =    feature_size
        A__       =    padding_value
        A__       =    sampling_rate
        A__       =    do_normalize
        A__       =    num_mel_bins
        A__       =    hop_length
        A__       =    win_length
        A__       =    win_function
        A__       =    fmin
        A__       =    fmax
        A__       =    mel_floor
        A__       =    return_attention_mask



     def     _a							(     self					:       Union[str, Any]					):






        """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     _a							(     self					:       int				,					_snake_case					:       Union[str, Any]=False				,					_snake_case					:       Union[str, Any]=False					):






        """simple docstring"""


        def _flatten(_snake_case					:       int					):
           return list(itertools.chain(*_snake_case					)					)

        if equal_length:
           A__       =    floats_list((self.batch_size, self.max_seq_length)					)
        else:
           # make sure that inputs increase in size
           A__       =    [
               _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:
           A__       =    [np.asarray(_snake_case					) for x in speech_inputs]

        return speech_inputs



     def     _a							(     self					:       Tuple				,					_snake_case					:       Any=False				,					_snake_case					:       str=False					):






        """simple docstring"""


        if equal_length:
           A__       =    [floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
        else:
           # make sure that inputs increase in size
           A__       =    [
               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:
           A__       =    [np.asarray(_snake_case					) for x in speech_inputs]

        return speech_inputs







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
     """simple docstring"""






     A__				:		Dict			       =    SpeechTaFeatureExtractor



     def     _a							(     self					:       Optional[Any]					):






        """simple docstring"""


        A__       =    SpeechTaFeatureExtractionTester(self					)



     def     _a							(     self					:       Dict				,					_snake_case					:       Any					):






        """simple docstring"""


        self.assertTrue(np.all(np.mean(_snake_case				,					axis=0					) < 1E-3					)					)
        self.assertTrue(np.all(np.abs(np.var(_snake_case				,					axis=0					) - 1					) < 1E-3					)					)



     def     _a							(     self					:       Optional[int]					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        # create three inputs of length 800, 1000, and 1200
        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
        A__       =    [np.asarray(_snake_case					) for speech_input in speech_inputs]

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

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



     def     _a							(     self					:       Optional[int]					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]

        A__       =    ['longest', 'max_length', 'do_not_pad']
        A__       =    [None, 16_00, None]
        for max_length, padding in zip(_snake_case				,					_snake_case					):
           A__       =    feat_extract(_snake_case				,					padding=_snake_case				,					max_length=_snake_case				,					return_tensors='np'					)
           A__       =    processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:8_00]					)
           self.assertTrue(input_values[0][8_00:].sum() < 1E-6					)
           self._check_zero_mean_unit_variance(input_values[1][:10_00]					)
           self.assertTrue(input_values[0][10_00:].sum() < 1E-6					)
           self._check_zero_mean_unit_variance(input_values[2][:12_00]					)



     def     _a							(     self					:       List[Any]					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        A__       =    range(8_00				,					14_00				,					2_00					)
        A__       =    [floats_list((1, x)					)[0] for x in lengths]

        A__       =    ['longest', 'max_length', 'do_not_pad']
        A__       =    [None, 16_00, None]

        for max_length, padding in zip(_snake_case				,					_snake_case					):
           A__       =    feat_extract(_snake_case				,					max_length=_snake_case				,					padding=_snake_case					)
           A__       =    processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:8_00]					)
           self._check_zero_mean_unit_variance(input_values[1][:10_00]					)
           self._check_zero_mean_unit_variance(input_values[2][:12_00]					)



     def     _a							(     self					:       Optional[int]					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
        A__       =    feat_extract(
            _snake_case				,					truncation=_snake_case				,					max_length=10_00				,					padding='max_length'				,					return_tensors='np'					)
        A__       =    processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00]					)
        self._check_zero_mean_unit_variance(input_values[1]					)
        self._check_zero_mean_unit_variance(input_values[2]					)



     def     _a							(     self					:       str					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
        A__       =    feat_extract(
            _snake_case				,					truncation=_snake_case				,					max_length=10_00				,					padding='longest'				,					return_tensors='np'					)
        A__       =    processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00]					)
        self._check_zero_mean_unit_variance(input_values[1, :10_00]					)
        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, 10_00)					)

        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
        A__       =    feat_extract(
            _snake_case				,					truncation=_snake_case				,					max_length=20_00				,					padding='longest'				,					return_tensors='np'					)
        A__       =    processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00]					)
        self._check_zero_mean_unit_variance(input_values[1, :10_00]					)
        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, 12_00)					)



     def     _a							(     self					:       Any					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        A__       =    np.random.rand(1_00					).astype(np.floataa					)
        A__       =    np_speech_inputs.tolist()

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



     def     _a							(     self					:       Dict					):






        """simple docstring"""


        A__       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        # create three inputs of length 800, 1000, and 1200
        A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
        A__       =    [np.asarray(_snake_case					) for speech_input in speech_inputs]

        # Test feature size
        A__       =    feature_extractor(audio_target=_snake_case				,					padding=_snake_case				,					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
        A__       =    feature_extractor(speech_inputs[0]				,					return_tensors='np'					).input_values
        A__       =    feature_extractor(np_speech_inputs[0]				,					return_tensors='np'					).input_values
        self.assertTrue(np.allclose(_snake_case				,					_snake_case				,					atol=1E-3					)					)

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

        # Test 2-D numpy arrays are batched.
        A__       =    [floats_list((1, x)					)[0] for x in (8_00, 8_00, 8_00)]
        A__       =    np.asarray(_snake_case					)
        A__       =    feature_extractor(_snake_case				,					return_tensors='np'					).input_values
        A__       =    feature_extractor(_snake_case				,					return_tensors='np'					).input_values
        for enc_seq_a, enc_seq_a in zip(_snake_case				,					_snake_case					):
           self.assertTrue(np.allclose(_snake_case				,					_snake_case				,					atol=1E-3					)					)



     def     _a							(     self					:       Optional[int]					):






        """simple docstring"""


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

        A__       =    BatchFeature({input_name: speech_inputs}					)

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

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

        A__       =    processed_features[input_name]

        if len(batch_features_input.shape					) < 3:
           A__       =    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     _a							(     self					:       Tuple					):






        """simple docstring"""


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

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

        A__       =    processed_features[input_name]

        if len(batch_features_input.shape					) < 3:
           A__       =    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     _a							(     self					:       Any					):






        """simple docstring"""


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

        A__       =    BatchFeature({input_name: speech_inputs}					)

        A__       =    feat_extract.num_mel_bins  # hack!

        A__       =    feat_extract.pad(_snake_case				,					padding='longest'				,					return_tensors='np'					)[input_name]
        A__       =    feat_extract.pad(_snake_case				,					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     _a							(     self					:       List[Any]					):






        """simple docstring"""


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

        A__       =    BatchFeature({input_name: speech_inputs}					)

        A__       =    feat_extract.num_mel_bins  # hack!

        A__       =    feat_extract.pad(_snake_case				,					padding='longest'				,					return_tensors='np'					)
        self.assertIn('attention_mask'				,					_snake_case					)
        self.assertListEqual(list(processed.attention_mask.shape					)				,					list(processed[input_name].shape[:2]					)					)
        self.assertListEqual(processed.attention_mask.sum(-1					).tolist()				,					_snake_case					)



     def     _a							(     self					:       Any					):






        """simple docstring"""


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

        A__       =    BatchFeature({input_name: speech_inputs}					)
        A__       =    min(_snake_case					)

        A__       =    feat_extract.num_mel_bins  # hack!

        A__       =    feat_extract.pad(
            _snake_case				,					padding='max_length'				,					max_length=_snake_case				,					truncation=_snake_case				,					return_tensors='np'					)
        self.assertIn('attention_mask'				,					_snake_case					)
        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     _a							(     self					:       List[str]				,					_snake_case					:       int					):






        """simple docstring"""


        from datasets import load_dataset

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

        return [x["array"] for x in speech_samples]



     def     _a							(     self					:       Dict					):






        """simple docstring"""


        A__       =    torch.tensor(
            [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
             3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
             2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
             4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
             7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
             4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]					)
        # fmt: on

        A__       =    self._load_datasamples(1					)
        A__       =    SpeechTaFeatureExtractor()
        A__       =    feature_extractor(_snake_case				,					return_tensors='pt'					).input_values
        self.assertEquals(input_values.shape				,					(1, 9_36_80)					)
        self.assertTrue(torch.allclose(input_values[0, :30]				,					_snake_case				,					atol=1E-6					)					)



     def     _a							(     self					:       str					):






        """simple docstring"""


        A__       =    torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]					)
        # fmt: on

        A__       =    self._load_datasamples(1					)
        A__       =    SpeechTaFeatureExtractor()
        A__       =    feature_extractor(audio_target=_snake_case				,					return_tensors='pt'					).input_values
        self.assertEquals(input_values.shape				,					(1, 3_66, 80)					)
        self.assertTrue(torch.allclose(input_values[0, 0, :30]				,					_snake_case				,					atol=1E-4					)					)









import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		int			       =    LongformerTokenizer
					A__				:		Optional[int]			       =    True
					A__				:		Any			       =    LongformerTokenizerFast
					A__				:		Dict			       =    True



					def     _a							(     self					:       int					):






								"""simple docstring"""


								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A__       =    [
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								A__       =    dict(zip(_snake_case				,					range(len(_snake_case					)					)					)					)
								A__       =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								A__       =    {'unk_token': '<unk>'}

								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['vocab_file']					)
								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['merges_file']					)
								with open(self.vocab_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write(json.dumps(_snake_case					) + '\n'					)
								with open(self.merges_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write('\n'.join(_snake_case					)					)



					def     _a							(     self					:       int				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Optional[int]				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								kwargs.update(self.special_tokens_map					)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Any				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    'lower newer'
								A__       =    'lower newer'
								return input_text, output_text



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.tokenizer_class(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map					)
								A__       =    'lower newer'
								A__       =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
								A__       =    tokenizer.tokenize(_snake_case					)  # , add_prefix_space=True)
								self.assertListEqual(_snake_case				,					_snake_case					)

								A__       =    tokens + [tokenizer.unk_token]
								A__       =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					)				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('Hello world!'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 2]					)
								self.assertListEqual(
								    tokenizer.encode('Hello world! cécé herlolip 418'				,					add_special_tokens=_snake_case					)				,					[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,					)



					@slow
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

								A__       =    tokenizer.encode('sequence builders'				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.encode('multi-sequence build'				,					add_special_tokens=_snake_case					)

								A__       =    tokenizer.encode(
								    'sequence builders'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.encode(
								    'sequence builders'				,					'multi-sequence build'				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)

								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case					)
								A__       =    tokenizer.build_inputs_with_special_tokens(_snake_case				,					_snake_case					)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()

								A__       =    'Encode this sequence.'
								A__       =    tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

								# Testing encoder arguments
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case				,					add_prefix_space=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								tokenizer.add_special_tokens({'bos_token': '<s>'}					)
								A__       =    tokenizer.encode(_snake_case				,					add_special_tokens=_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)

								# Testing spaces after special tokens
								A__       =    '<mask>'
								tokenizer.add_special_tokens(
								    {'mask_token': AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					)}					)  # mask token has a left space
								A__       =    tokenizer.convert_tokens_to_ids(_snake_case					)

								A__       =    'Encode <mask> sequence'
								A__       =    'Encode <mask>sequence'

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertEqual(_snake_case				,					_snake_case					)

								A__       =    tokenizer.encode(_snake_case					)
								A__       =    encoded.index(_snake_case					)
								A__       =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
								self.assertNotEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    self.rust_tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    self.tokenizer_class.from_pretrained(_snake_case				,					**_snake_case					)
														A__       =    'A, <mask> AllenNLP sentence.'
														A__       =    tokenizer_r.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)
														A__       =    tokenizer_p.encode_plus(_snake_case				,					add_special_tokens=_snake_case				,					return_token_type_ids=_snake_case					)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['token_type_ids']					)				,					sum(tokens_p['token_type_ids']					)					)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)				,					sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)				,					)

														A__       =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
														A__       =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
														self.assertSequenceEqual(tokens_r['input_ids']				,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
														self.assertSequenceEqual(
														    _snake_case				,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for trim_offsets, add_prefix_space in itertools.product([True, False]				,					repeat=2					):
											A__       =    self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)

											A__       =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											A__       =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['add_prefix_space']				,					_snake_case					)

											self.assertEqual(post_processor_state['add_prefix_space']				,					_snake_case					)
											self.assertEqual(post_processor_state['trim_offsets']				,					_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														A__       =    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
														A__       =    F'''{text_of_1_token} {text_of_1_token}'''

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    F''' {text}'''

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(1, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					) + 1, 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)

														A__       =    self.rust_tokenizer_class.from_pretrained(
														    _snake_case				,					use_fast=_snake_case				,					add_prefix_space=_snake_case				,					trim_offsets=_snake_case					)
														A__       =    tokenizer_r(_snake_case				,					return_offsets_mapping=_snake_case				,					add_special_tokens=_snake_case					)
														self.assertEqual(encoding.offset_mapping[0]				,					(0, 1 + len(_snake_case					))					)
														self.assertEqual(
														    encoding.offset_mapping[1]				,					(1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					))				,					)




