



"""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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

				def 					_UpperCAmelCase	(				self			:				Dict):







						"""simple docstring"""







						lowercase_								= """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
						lowercase_								= dict(zip(UpperCAmelCase_ ,  range(len(UpperCAmelCase_))))

						lowercase_								= {
						    """unk_token""": """<unk>""",
						    """bos_token""": """<s>""",
						    """eos_token""": """</s>""",
						}
						lowercase_								= {
						    """feature_size""": 1,
						    """padding_value""": 0.0,
						    """sampling_rate""": 1_6_0_0_0,
						    """return_attention_mask""": False,
						    """do_normalize""": True,
						}

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

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

						# load decoder from hub
						lowercase_								= """hf-internal-testing/ngram-beam-search-decoder"""

				def 					_UpperCAmelCase	(				self			:				Any ,  **lowerCAmelCase_			:				List[Any]):







						"""simple docstring"""







						lowercase_								= self.add_kwargs_tokens_map.copy()
						kwargs.update(UpperCAmelCase_)
						return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,  **UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				List[Any] ,  **lowerCAmelCase_			:				Optional[int]):







						"""simple docstring"""







						return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,  **UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				str ,  **lowerCAmelCase_			:				int):







						"""simple docstring"""







						return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,  **UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				str):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname)

				def 					_UpperCAmelCase	(				self			:				Dict):







						"""simple docstring"""







						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

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

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

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

						# 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 ,  UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				Dict):







						"""simple docstring"""







						lowercase_								= 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
						lowercase_								= 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 					_UpperCAmelCase	(				self			:				Tuple):







						"""simple docstring"""







						lowercase_								= self.get_tokenizer()
						# add token to trigger raise
						tokenizer.add_tokens(["""xx"""])
						with self.assertRaisesRegex(UpperCAmelCase_ ,  """include"""):
								WavaVecaProcessorWithLM(
								    tokenizer=UpperCAmelCase_ ,  feature_extractor=self.get_feature_extractor() ,  decoder=self.get_decoder())

				def 					_UpperCAmelCase	(				self			:				List[Any]):







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= floats_list((3, 1_0_0_0))

						lowercase_								= feature_extractor(UpperCAmelCase_ ,  return_tensors="""np""")
						lowercase_								= processor(UpperCAmelCase_ ,  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 					_UpperCAmelCase	(				self			:				Optional[int]):







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= """This is a test string"""

						lowercase_								= processor(text=UpperCAmelCase_)

						lowercase_								= tokenizer(UpperCAmelCase_)

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

				def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				int=(2, 1_0, 1_6) ,  lowerCAmelCase_			:				Any=7_7):







						"""simple docstring"""







						np.random.seed(UpperCAmelCase_)
						return np.random.rand(*UpperCAmelCase_)

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







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= self._get_dummy_logits(shape=(1_0, 1_6) ,  seed=1_3)

						lowercase_								= processor.decode(UpperCAmelCase_)

						lowercase_								= decoder.decode_beams(UpperCAmelCase_)[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 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				List[str]):







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= 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:
								lowercase_								= processor.batch_decode(UpperCAmelCase_)
						else:
								with get_context(UpperCAmelCase_).Pool() as pool:
										lowercase_								= processor.batch_decode(UpperCAmelCase_ ,  UpperCAmelCase_)

						lowercase_								= list(UpperCAmelCase_)

						with get_context("""fork""").Pool() as p:
								lowercase_								= decoder.decode_beams_batch(UpperCAmelCase_ ,  UpperCAmelCase_)

						lowercase_					,    lowercase_					,    lowercase_								= [], [], []
						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(UpperCAmelCase_ ,  decoded_processor.text)
						self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,  decoded_processor.text)
						self.assertListEqual(UpperCAmelCase_ ,  decoded_processor.logit_score)
						self.assertListEqual(UpperCAmelCase_ ,  decoded_processor.lm_score)

				def 					_UpperCAmelCase	(				self			:				Optional[int]):







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= self._get_dummy_logits()

						lowercase_								= 1_5
						lowercase_								= -20.0
						lowercase_								= -4.0

						lowercase_								= processor.batch_decode(
						    UpperCAmelCase_ ,  beam_width=UpperCAmelCase_ ,  beam_prune_logp=UpperCAmelCase_ ,  token_min_logp=UpperCAmelCase_ ,  )
						lowercase_								= decoded_processor_out.text

						lowercase_								= list(UpperCAmelCase_)

						with get_context("""fork""").Pool() as pool:
								lowercase_								= decoder.decode_beams_batch(
								    UpperCAmelCase_ ,  UpperCAmelCase_ ,  beam_width=UpperCAmelCase_ ,  beam_prune_logp=UpperCAmelCase_ ,  token_min_logp=UpperCAmelCase_ ,  )

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

						self.assertListEqual(UpperCAmelCase_ ,  UpperCAmelCase_)
						self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,  UpperCAmelCase_)

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

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

				def 					_UpperCAmelCase	(				self			:				Optional[Any]):







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

						lowercase_								= self._get_dummy_logits()

						lowercase_								= 2.0
						lowercase_								= 5.0
						lowercase_								= -20.0
						lowercase_								= True

						lowercase_								= processor.batch_decode(
						    UpperCAmelCase_ ,  alpha=UpperCAmelCase_ ,  beta=UpperCAmelCase_ ,  unk_score_offset=UpperCAmelCase_ ,  lm_score_boundary=UpperCAmelCase_ ,  )
						lowercase_								= decoded_processor_out.text

						lowercase_								= list(UpperCAmelCase_)
						decoder.reset_params(
						    alpha=UpperCAmelCase_ ,  beta=UpperCAmelCase_ ,  unk_score_offset=UpperCAmelCase_ ,  lm_score_boundary=UpperCAmelCase_ ,  )

						with get_context("""fork""").Pool() as pool:
								lowercase_								= decoder.decode_beams_batch(
								    UpperCAmelCase_ ,  UpperCAmelCase_ ,  )

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

						self.assertListEqual(UpperCAmelCase_ ,  UpperCAmelCase_)
						self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,  UpperCAmelCase_)
						lowercase_								= 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 ,  UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				Optional[int]):







						"""simple docstring"""







						lowercase_								= WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""")

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

						lowercase_								= os.listdir(UpperCAmelCase_)
						lowercase_								= ["""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(UpperCAmelCase_ ,  UpperCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				Tuple):







						"""simple docstring"""







						lowercase_								= snapshot_download("""hf-internal-testing/processor_with_lm""")

						lowercase_								= WavaVecaProcessorWithLM.from_pretrained(UpperCAmelCase_)

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

						lowercase_								= os.listdir(UpperCAmelCase_)
						lowercase_								= os.listdir(UpperCAmelCase_)

						local_decoder_files.sort()
						expected_decoder_files.sort()

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

				def 					_UpperCAmelCase	(				self			:				List[Any]):







						"""simple docstring"""







						lowercase_								= WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""")
						lowercase_								= AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""")

						lowercase_								= floats_list((3, 1_0_0_0))

						lowercase_								= processor_wavaveca(UpperCAmelCase_ ,  return_tensors="""np""")
						lowercase_								= processor_auto(UpperCAmelCase_ ,  return_tensors="""np""")

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

						lowercase_								= self._get_dummy_logits()

						lowercase_								= processor_wavaveca.batch_decode(UpperCAmelCase_)
						lowercase_								= processor_auto.batch_decode(UpperCAmelCase_)

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

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







						"""simple docstring"""







						lowercase_								= self.get_feature_extractor()
						lowercase_								= self.get_tokenizer()
						lowercase_								= self.get_decoder()

						lowercase_								= WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_ ,  feature_extractor=UpperCAmelCase_ ,  decoder=UpperCAmelCase_)

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

				@staticmethod
				def 					_UpperCAmelCase	(				lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Dict):







						"""simple docstring"""







						lowercase_								= [d[key] for d in offsets]
						return retrieved_list

				def 					_UpperCAmelCase	(				self			:				Tuple):







						"""simple docstring"""







						lowercase_								= WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""")
						lowercase_								= self._get_dummy_logits()[0]

						lowercase_								= processor.decode(UpperCAmelCase_ ,  output_word_offsets=UpperCAmelCase_)
						# 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(UpperCAmelCase_ ,  UpperCAmelCase_))

						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 					_UpperCAmelCase	(				self			:				Any):







						"""simple docstring"""







						lowercase_								= WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""")
						lowercase_								= self._get_dummy_logits()

						lowercase_								= processor.batch_decode(UpperCAmelCase_ ,  output_word_offsets=UpperCAmelCase_)

						# 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(UpperCAmelCase_ ,  UpperCAmelCase_))

						self.assertListEqual(
						    [""" """.join(self.get_from_offsets(UpperCAmelCase_ ,  """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 					_UpperCAmelCase	(				self			:				List[Any]):







						"""simple docstring"""







						import torch

						lowercase_								= load_dataset("""common_voice""" ,  """en""" ,  split="""train""" ,  streaming=UpperCAmelCase_)
						lowercase_								= ds.cast_column("""audio""" ,  datasets.Audio(sampling_rate=1_6_0_0_0))
						lowercase_								= iter(UpperCAmelCase_)
						lowercase_								= next(UpperCAmelCase_)

						lowercase_								= AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""")
						lowercase_								= 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
						lowercase_								= processor(sample["""audio"""]["""array"""] ,  return_tensors="""pt""").input_values

						with torch.no_grad():
								lowercase_								= model(UpperCAmelCase_).logits.cpu().numpy()

						lowercase_								= processor.decode(logits[0] ,  output_word_offsets=UpperCAmelCase_)

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

						lowercase_								= """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(UpperCAmelCase_ ,  """word""")) ,  UpperCAmelCase_)
						self.assertEqual(""" """.join(self.get_from_offsets(UpperCAmelCase_ ,  """word""")) ,  output.text)

						# output times
						lowercase_								= torch.tensor(self.get_from_offsets(UpperCAmelCase_ ,  """start_time"""))
						lowercase_								= torch.tensor(self.get_from_offsets(UpperCAmelCase_ ,  """end_time"""))

						# fmt: off
						lowercase_								= 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])
						lowercase_								= 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(UpperCAmelCase_ ,  UpperCAmelCase_ ,  atol=0.01))
						self.assertTrue(torch.allclose(UpperCAmelCase_ ,  UpperCAmelCase_ ,  atol=0.01))






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase						:		List[Any]			= {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
       from .tokenization_bertweet import BertweetTokenizer

else:
       import sys

       UpperCAmelCase						:		Tuple			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

