

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
				from huggingface_hub import snapshot_download
				from pyctcdecode import BeamSearchDecoderCTC

				from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
				from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
				from transformers import WavaVecaForCTC
@require_pyctcdecode
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
		__UpperCAmelCase      		= dict(zip(_lowercase			,    range(len(_lowercase						)						)						)						)

		__UpperCAmelCase      		= {
		    '''unk_token''': '''<unk>''',
		    '''bos_token''': '''<s>''',
		    '''eos_token''': '''</s>''',
		}
		__UpperCAmelCase      		= {
		    '''feature_size''': 1,
		    '''padding_value''': 0.0,
		    '''sampling_rate''': 1_60_00,
		    '''return_attention_mask''': False,
		    '''do_normalize''': True,
		}

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

		with open(self.feature_extraction_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			fp.write(json.dumps(_lowercase						) + '''\n'''						)

		# load decoder from hub
		__UpperCAmelCase      		= '''hf-internal-testing/ngram-beam-search-decoder'''



	def        a  (  self     :       Any			,    **_lowercase     :       Optional[int]						):
		__UpperCAmelCase      		= self.add_kwargs_tokens_map.copy()
		kwargs.update(_lowercase						)
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]			,    **_lowercase     :       Any						):
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]			,    **_lowercase     :       Optional[Any]						):
		return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]						):
		shutil.rmtree(self.tmpdirname						)



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		processor.save_pretrained(self.tmpdirname						)
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname						)

		# tokenizer
		self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer.get_vocab()						)
		self.assertIsInstance(processor.tokenizer			,    _lowercase						)

		# feature extractor
		self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor.to_json_string()						)
		self.assertIsInstance(processor.feature_extractor			,    _lowercase						)

		# decoder
		self.assertEqual(processor.decoder._alphabet.labels			,    decoder._alphabet.labels						)
		self.assertEqual(
		    processor.decoder.model_container[decoder._model_key]._unigram_set			,    decoder.model_container[decoder._model_key]._unigram_set			,    )
		self.assertIsInstance(processor.decoder			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM(
		    tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor()			,    decoder=self.get_decoder()						)
		processor.save_pretrained(self.tmpdirname						)

		# make sure that error is thrown when decoder alphabet doesn't match
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(
		    self.tmpdirname			,    alpha=5.0			,    beta=3.0			,    score_boundary=-7.0			,    unk_score_offset=3						)

		# decoder
		self.assertEqual(processor.language_model.alpha			,    5.0						)
		self.assertEqual(processor.language_model.beta			,    3.0						)
		self.assertEqual(processor.language_model.score_boundary			,    -7.0						)
		self.assertEqual(processor.language_model.unk_score_offset			,    3						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= self.get_tokenizer()
		# add token to trigger raise
		tokenizer.add_tokens(['''xx''']						)
		with self.assertRaisesRegex(_lowercase			,    '''include'''						):
			WavaVecaProcessorWithLM(
			    tokenizer=_lowercase			,    feature_extractor=self.get_feature_extractor()			,    decoder=self.get_decoder()						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= floats_list((3, 10_00)						)

		__UpperCAmelCase      		= feature_extractor(_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= processor(_lowercase			,    return_tensors='''np'''						)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1E-2						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= '''This is a test string'''

		__UpperCAmelCase      		= processor(text=_lowercase						)

		__UpperCAmelCase      		= tokenizer(_lowercase						)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]						)



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Any=(2, 10, 16)			,    _lowercase     :       str=77						):
		np.random.seed(_lowercase						)
		return np.random.rand(*_lowercase						)



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits(shape=(10, 16)			,    seed=13						)

		__UpperCAmelCase      		= processor.decode(_lowercase						)

		__UpperCAmelCase      		= decoder.decode_beams(_lowercase						)[0]

		self.assertEqual(decoded_decoder[0]			,    decoded_processor.text						)
		self.assertEqual('''</s> <s> </s>'''			,    decoded_processor.text						)
		self.assertEqual(decoded_decoder[-2]			,    decoded_processor.logit_score						)
		self.assertEqual(decoded_decoder[-1]			,    decoded_processor.lm_score						)



	@parameterized.expand([[None], ['''fork'''], ['''spawn''']]						)
	def        a  (  self     :       int			,    _lowercase     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
		#       otherwise, the LM won't be available to the pool's sub-processes.
		# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
		if pool_context is None:
			__UpperCAmelCase      		= processor.batch_decode(_lowercase						)
		else:
			with get_context(_lowercase						).Pool() as pool:
				__UpperCAmelCase      		= processor.batch_decode(_lowercase			,    _lowercase						)

		__UpperCAmelCase      		= list(_lowercase						)

		with get_context('''fork'''						).Pool() as p:
			__UpperCAmelCase      		= decoder.decode_beams_batch(_lowercase			,    _lowercase						)

		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= [], [], []
		for beams in decoded_beams:
			texts_decoder.append(beams[0][0]						)
			logit_scores_decoder.append(beams[0][-2]						)
			lm_scores_decoder.append(beams[0][-1]						)

		self.assertListEqual(_lowercase			,    decoded_processor.text						)
		self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']			,    decoded_processor.text						)
		self.assertListEqual(_lowercase			,    decoded_processor.logit_score						)
		self.assertListEqual(_lowercase			,    decoded_processor.lm_score						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= 15
		__UpperCAmelCase      		= -20.0
		__UpperCAmelCase      		= -4.0

		__UpperCAmelCase      		= processor.batch_decode(
		    _lowercase			,    beam_width=_lowercase			,    beam_prune_logp=_lowercase			,    token_min_logp=_lowercase			,    )
		__UpperCAmelCase      		= decoded_processor_out.text

		__UpperCAmelCase      		= list(_lowercase						)

		with get_context('''fork'''						).Pool() as pool:
			__UpperCAmelCase      		= decoder.decode_beams_batch(
			    _lowercase			,    _lowercase			,    beam_width=_lowercase			,    beam_prune_logp=_lowercase			,    token_min_logp=_lowercase			,    )

		__UpperCAmelCase      		= [d[0][0] for d in decoded_decoder_out]
		__UpperCAmelCase      		= [d[0][2] for d in decoded_decoder_out]
		__UpperCAmelCase      		= [d[0][3] for d in decoded_decoder_out]

		self.assertListEqual(_lowercase			,    _lowercase						)
		self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']			,    _lowercase						)

		self.assertTrue(np.array_equal(_lowercase			,    decoded_processor_out.logit_score						)						)
		self.assertTrue(np.allclose([-20.054, -18.447]			,    _lowercase			,    atol=1E-3						)						)

		self.assertTrue(np.array_equal(_lowercase			,    decoded_processor_out.lm_score						)						)
		self.assertTrue(np.allclose([-15.554, -13.9_474]			,    _lowercase			,    atol=1E-3						)						)



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= 2.0
		__UpperCAmelCase      		= 5.0
		__UpperCAmelCase      		= -20.0
		__UpperCAmelCase      		= True

		__UpperCAmelCase      		= processor.batch_decode(
		    _lowercase			,    alpha=_lowercase			,    beta=_lowercase			,    unk_score_offset=_lowercase			,    lm_score_boundary=_lowercase			,    )
		__UpperCAmelCase      		= decoded_processor_out.text

		__UpperCAmelCase      		= list(_lowercase						)
		decoder.reset_params(
		    alpha=_lowercase			,    beta=_lowercase			,    unk_score_offset=_lowercase			,    lm_score_boundary=_lowercase			,    )

		with get_context('''fork'''						).Pool() as pool:
			__UpperCAmelCase      		= decoder.decode_beams_batch(
			    _lowercase			,    _lowercase			,    )

		__UpperCAmelCase      		= [d[0][0] for d in decoded_decoder_out]

		self.assertListEqual(_lowercase			,    _lowercase						)
		self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']			,    _lowercase						)
		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		self.assertEqual(lm_model.alpha			,    2.0						)
		self.assertEqual(lm_model.beta			,    5.0						)
		self.assertEqual(lm_model.unk_score_offset			,    -20.0						)
		self.assertEqual(lm_model.score_boundary			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		__UpperCAmelCase      		= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

		__UpperCAmelCase      		= os.listdir(_lowercase						)
		__UpperCAmelCase      		= ['''alphabet.json''', '''language_model''']

		downloaded_decoder_files.sort()
		expected_decoder_files.sort()

		# test that only decoder relevant files from
		# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
		# are downloaded and none of the rest (e.g. README.md, ...)
		self.assertListEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= snapshot_download('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(_lowercase						)

		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		__UpperCAmelCase      		= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

		__UpperCAmelCase      		= os.listdir(_lowercase						)
		__UpperCAmelCase      		= os.listdir(_lowercase						)

		local_decoder_files.sort()
		expected_decoder_files.sort()

		# test that both decoder form hub and local files in cache are the same
		self.assertListEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= floats_list((3, 10_00)						)

		__UpperCAmelCase      		= processor_wavaveca(_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= processor_auto(_lowercase			,    return_tensors='''np'''						)

		for key in input_wavaveca.keys():
			self.assertAlmostEqual(input_wavaveca[key].sum()			,    input_auto[key].sum()			,    delta=1E-2						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= processor_wavaveca.batch_decode(_lowercase						)
		__UpperCAmelCase      		= processor_auto.batch_decode(_lowercase						)

		self.assertListEqual(decoded_wavaveca.text			,    decoded_auto.text						)



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		self.assertListEqual(
		    processor.model_input_names			,    feature_extractor.model_input_names			,    msg='''`processor` and `feature_extractor` model input names do not match'''			,    )



	@staticmethod
	def        a  (  _lowercase     :       List[Any]			,    _lowercase     :       Optional[Any]						):
		__UpperCAmelCase      		= [d[key] for d in offsets]
		return retrieved_list



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= self._get_dummy_logits()[0]

		__UpperCAmelCase      		= processor.decode(_lowercase			,    output_word_offsets=_lowercase						)
		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()						)			,    4						)
		self.assertTrue('''text''' in outputs						)
		self.assertTrue('''word_offsets''' in outputs						)
		self.assertTrue(isinstance(_lowercase			,    _lowercase						)						)

		self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']			,    '''word'''						)						)			,    outputs.text						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''word'''						)			,    ['''<s>''', '''<s>''', '''</s>''']						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''start_offset'''						)			,    [0, 2, 4]						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''end_offset'''						)			,    [1, 3, 5]						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= processor.batch_decode(_lowercase			,    output_word_offsets=_lowercase						)

		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()						)			,    4						)
		self.assertTrue('''text''' in outputs						)
		self.assertTrue('''word_offsets''' in outputs						)
		self.assertTrue(isinstance(_lowercase			,    _lowercase						)						)

		self.assertListEqual(
		    [''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						) for o in outputs['''word_offsets''']]			,    outputs.text						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''word'''						)			,    ['''<s>''', '''<s>''', '''</s>''']						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''start_offset'''						)			,    [0, 2, 4]						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''end_offset'''						)			,    [1, 3, 5]						)



	@slow
	@require_torch
	@require_torchaudio
	def        a  (  self     :       Union[str, Any]						):
		import torch

		__UpperCAmelCase      		= load_dataset('''common_voice'''			,    '''en'''			,    split='''train'''			,    streaming=_lowercase						)
		__UpperCAmelCase      		= ds.cast_column('''audio'''			,    datasets.Audio(sampling_rate=1_60_00						)						)
		__UpperCAmelCase      		= iter(_lowercase						)
		__UpperCAmelCase      		= next(_lowercase						)

		__UpperCAmelCase      		= AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)
		__UpperCAmelCase      		= WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)

		# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
		__UpperCAmelCase      		= processor(sample['''audio''']['''array''']			,    return_tensors='''pt'''						).input_values

		with torch.no_grad():
			__UpperCAmelCase      		= model(_lowercase						).logits.cpu().numpy()

		__UpperCAmelCase      		= processor.decode(logits[0]			,    output_word_offsets=_lowercase						)

		__UpperCAmelCase      		= model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
		__UpperCAmelCase      		= [
		    {
		        '''start_time''': d['''start_offset'''] * time_offset,
		        '''end_time''': d['''end_offset'''] * time_offset,
		        '''word''': d['''word'''],
		    }
		    for d in output['''word_offsets''']
		]

		__UpperCAmelCase      		= '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

		# output words
		self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						)			,    _lowercase						)
		self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						)			,    output.text						)

		# output times
		__UpperCAmelCase      		= torch.tensor(self.get_from_offsets(_lowercase			,    '''start_time'''						)						)
		__UpperCAmelCase      		= torch.tensor(self.get_from_offsets(_lowercase			,    '''end_time'''						)						)

		# fmt: off
		__UpperCAmelCase      		= torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]						)
		__UpperCAmelCase      		= torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]						)
		# fmt: on

		self.assertTrue(torch.allclose(_lowercase			,    _lowercase			,    atol=0.01						)						)
		self.assertTrue(torch.allclose(_lowercase			,    _lowercase			,    atol=0.01						)						)




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			CanineTokenizer
 SCREAMING_SNAKE_CASE__ =			False




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()
       a		:List[str]									=			CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname       )




 @cached_property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return CanineTokenizer.from_pretrained('''google/canine-s'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       a		:Dict									=			self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )
       a		:List[Any]									=			1024
       return tokenizer




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:Tuple									=			['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
       # fmt: off
       a		:Any									=			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
       # fmt: on
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertIsInstance(_lowerCamelCase   ,    _lowerCamelCase       )

       a		:str									=			list(batch.input_ids.numpy()[0]       )

       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

       self.assertEqual((2, 39)   ,    batch.input_ids.shape       )
       self.assertEqual((2, 39)   ,    batch.attention_mask.shape       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.canine_tokenizer
       a		:Optional[int]									=			['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn('''input_ids'''   ,    _lowerCamelCase       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertIn('''token_type_ids'''   ,    _lowerCamelCase       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:str									=			[
           '''What\'s the weater?''',
           '''It\'s about 25 degrees.''',
       ]
       a		:List[str]									=			tokenizer(
           text_target=_lowerCamelCase   ,    max_length=32   ,    padding='''max_length'''   ,    truncation=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertEqual(32   ,    targets['''input_ids'''].shape[1]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # safety check on max_len default value so we are sure the test works
       a		:Tuple									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   self.assertNotEqual(tokenizer.model_max_length   ,    42       )

        # Now let's start the test
       a		:Tuple									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   a		:List[str]									=			tempfile.mkdtemp()

                   a		:Any									=			''' He is very happy, UNwant\u00E9d,running'''
                   a		:Dict									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:Optional[Any]									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:str									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

                   shutil.rmtree(_lowerCamelCase       )

       a		:str									=			self.get_tokenizers(model_max_length=42       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   a		:Any									=			tempfile.mkdtemp()

                   a		:Optional[Any]									=			''' He is very happy, UNwant\u00E9d,running'''

                   a		:Optional[int]									=			tokenizer.additional_special_tokens

                   # We can add a new special token for Canine as follows:
                   a		:str									=			chr(0Xe_0_0_7       )
                   additional_special_tokens.append(_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:int									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:Dict									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )
                   self.assertIn(_lowerCamelCase   ,    after_tokenizer.additional_special_tokens       )
                   self.assertEqual(after_tokenizer.model_max_length   ,    42       )

                   a		:str									=			tokenizer.__class__.from_pretrained(_lowerCamelCase   ,    model_max_length=43       )
                   self.assertEqual(tokenizer.model_max_length   ,    43       )

                   shutil.rmtree(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a				,							a		:Tuple									=			self.get_clean_sequence(_lowerCamelCase       )

                   # a special token for Canine can be defined as follows:
                   a		:Tuple									=			0Xe_0_0_5
                   a		:Optional[int]									=			chr(_lowerCamelCase       )

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   a		:Optional[int]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )

                   a		:List[Any]									=			tokenizer.decode(ids + encoded_special_token   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
                   a		:List[str]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )

                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(_lowerCamelCase   ,    input_encoded + special_token_id       )

                   a		:Any									=			tokenizer.decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )
                   self.assertTrue(special_token not in decoded       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:int									=			chr(0Xe_0_0_5       )
                   a		:str									=			chr(0Xe_0_0_6       )

                   # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                   tokenizer.add_tokens([SPECIAL_TOKEN_1]   ,    special_tokens=_lowerCamelCase       )
                   # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                   # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}       )

                   a		:Optional[int]									=			tokenizer.tokenize(_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.tokenize(_lowerCamelCase       )

                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # a special token for Canine can be defined as follows:
                   a		:Optional[int]									=			0Xe_0_0_6
                   a		:List[str]									=			chr(_lowerCamelCase       )

                   a		:Optional[int]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}       )

                   with tempfile.TemporaryDirectory() as tmp_dir_name:
                         tokenizer.save_pretrained(_lowerCamelCase       )
                         tokenizer.from_pretrained(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(_lowerCamelCase       )

                   with open(os.path.join(_lowerCamelCase   ,    '''special_tokens_map.json'''       )   ,    encoding='''utf-8'''       ) as json_file:
                         a		:Optional[Any]									=			json.load(_lowerCamelCase       )

                   with open(os.path.join(_lowerCamelCase   ,    '''tokenizer_config.json'''       )   ,    encoding='''utf-8'''       ) as json_file:
                         a		:Tuple									=			json.load(_lowerCamelCase       )

                   # a special token for Canine can be defined as follows:
                   a		:int									=			0Xe_0_0_6
                   a		:Optional[Any]									=			chr(_lowerCamelCase       )

                   a		:Union[str, Any]									=			[new_token_a]
                   a		:Optional[int]									=			[new_token_a]

                   with open(os.path.join(_lowerCamelCase   ,    '''special_tokens_map.json'''       )   ,    '''w'''   ,    encoding='''utf-8'''       ) as outfile:
                         json.dump(_lowerCamelCase   ,    _lowerCamelCase       )
                   with open(os.path.join(_lowerCamelCase   ,    '''tokenizer_config.json'''       )   ,    '''w'''   ,    encoding='''utf-8'''       ) as outfile:
                         json.dump(_lowerCamelCase   ,    _lowerCamelCase       )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   a		:str									=			tokenizer_class.from_pretrained(_lowerCamelCase   ,    extra_ids=0       )
                   self.assertIn(_lowerCamelCase   ,    tokenizer_without_change_in_init.additional_special_tokens       )
                   # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]       )       )   ,    )

                   a		:Optional[int]									=			0Xe_0_0_7
                   a		:Any									=			chr(_lowerCamelCase       )
                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   a		:Tuple									=			[AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )]
                   a		:Optional[Any]									=			tokenizer_class.from_pretrained(
                       _lowerCamelCase   ,    additional_special_tokens=_lowerCamelCase   ,    extra_ids=0       )

                   self.assertIn(_lowerCamelCase   ,    tokenizer.additional_special_tokens       )
                   # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]       )       )       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:List[str]									=			'''hello world'''
                   if self.space_between_special_tokens:
                         a		:Optional[Any]									=			'''[CLS] hello world [SEP]'''
                   else:
                         a		:Tuple									=			input
                   a		:Any									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:List[Any]									=			tokenizer.decode(_lowerCamelCase   ,    spaces_between_special_tokens=self.space_between_special_tokens       )
                   self.assertIn(_lowerCamelCase   ,    [output, output.lower()]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:Any									=			[
                       '''bos_token''',
                       '''eos_token''',
                       '''unk_token''',
                       '''sep_token''',
                       '''pad_token''',
                       '''cls_token''',
                       '''mask_token''',
                   ]

       a		:str									=			'''a'''
       a		:List[Any]									=			ord(_lowerCamelCase       )

       for attr in attributes_list:
             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    []       )

       a		:List[Any]									=			0Xe_0_0_6
       a		:str									=			chr(_lowerCamelCase       )
       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    [additional_special_token_id]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    [additional_special_token]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    [additional_special_token_id]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass





