
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    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
												lowerCamelCase_					=       dict(zip(__UpperCamelCase ,       range(len(__UpperCamelCase ) ) ) )

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

												lowerCamelCase_					=       tempfile.mkdtemp()
												lowerCamelCase_					=       os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""] )
												lowerCamelCase_					=       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
												lowerCamelCase_					=       """hf-internal-testing/ngram-beam-search-decoder"""

					def  lowercase__   (						self       :  List[str] ,       **__UpperCamelCase       :  Tuple ):
												lowerCamelCase_					=       self.add_kwargs_tokens_map.copy()
												kwargs.update(__UpperCamelCase )
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  int ):
												return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ,       **__UpperCamelCase       :  str ):
												return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Any ):
												shutil.rmtree(self.tmpdirname )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												processor.save_pretrained(self.tmpdirname )
												lowerCamelCase_					=       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  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       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
												lowerCamelCase_					=       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  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       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  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												lowerCamelCase_					=       floats_list((3, 1_0_0_0) )

												lowerCamelCase_					=       feature_extractor(__UpperCamelCase ,       return_tensors="""np""" )
												lowerCamelCase_					=       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  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												lowerCamelCase_					=       """This is a test string"""

												lowerCamelCase_					=       processor(text=__UpperCamelCase )

												lowerCamelCase_					=       tokenizer(__UpperCamelCase )

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

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Dict=(2, 1_0, 1_6) ,       __UpperCamelCase       :  Optional[int]=7_7 ):
												np.random.seed(__UpperCamelCase )
												return np.random.rand(*__UpperCamelCase )

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

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

												lowerCamelCase_					=       processor.decode(__UpperCamelCase )

												lowerCamelCase_					=       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  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												lowerCamelCase_					=       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:
																			lowerCamelCase_					=       processor.batch_decode(__UpperCamelCase )
												else:
																			with get_context(__UpperCamelCase ).Pool() as pool:
																										lowerCamelCase_					=       processor.batch_decode(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       list(__UpperCamelCase )

												with get_context("""fork""" ).Pool() as p:
																			lowerCamelCase_					=       decoder.decode_beams_batch(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       [], [], []
												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  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												lowerCamelCase_					=       self._get_dummy_logits()

												lowerCamelCase_					=       1_5
												lowerCamelCase_					=       -20.0
												lowerCamelCase_					=       -4.0

												lowerCamelCase_					=       processor.batch_decode(
												    __UpperCamelCase ,       beam_width=__UpperCamelCase ,       beam_prune_logp=__UpperCamelCase ,       token_min_logp=__UpperCamelCase ,       )
												lowerCamelCase_					=       decoded_processor_out.text

												lowerCamelCase_					=       list(__UpperCamelCase )

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

												lowerCamelCase_					=       [d[0][0] for d in decoded_decoder_out]
												lowerCamelCase_					=       [d[0][2] for d in decoded_decoder_out]
												lowerCamelCase_					=       [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, -13.9474] ,       __UpperCamelCase ,       atol=1E-3 ) )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       WavaVecaProcessorWithLM(tokenizer=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       decoder=__UpperCamelCase )

												lowerCamelCase_					=       self._get_dummy_logits()

												lowerCamelCase_					=       2.0
												lowerCamelCase_					=       5.0
												lowerCamelCase_					=       -20.0
												lowerCamelCase_					=       True

												lowerCamelCase_					=       processor.batch_decode(
												    __UpperCamelCase ,       alpha=__UpperCamelCase ,       beta=__UpperCamelCase ,       unk_score_offset=__UpperCamelCase ,       lm_score_boundary=__UpperCamelCase ,       )
												lowerCamelCase_					=       decoded_processor_out.text

												lowerCamelCase_					=       list(__UpperCamelCase )
												decoder.reset_params(
												    alpha=__UpperCamelCase ,       beta=__UpperCamelCase ,       unk_score_offset=__UpperCamelCase ,       lm_score_boundary=__UpperCamelCase ,       )

												with get_context("""fork""" ).Pool() as pool:
																			lowerCamelCase_					=       decoder.decode_beams_batch(
																			    __UpperCamelCase ,       __UpperCamelCase ,       )

												lowerCamelCase_					=       [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 )
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )

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

												lowerCamelCase_					=       os.listdir(__UpperCamelCase )
												lowerCamelCase_					=       ["""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  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       snapshot_download("""hf-internal-testing/processor_with_lm""" )

												lowerCamelCase_					=       WavaVecaProcessorWithLM.from_pretrained(__UpperCamelCase )

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

												lowerCamelCase_					=       os.listdir(__UpperCamelCase )
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
												lowerCamelCase_					=       AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )

												lowerCamelCase_					=       floats_list((3, 1_0_0_0) )

												lowerCamelCase_					=       processor_wavaveca(__UpperCamelCase ,       return_tensors="""np""" )
												lowerCamelCase_					=       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 )

												lowerCamelCase_					=       self._get_dummy_logits()

												lowerCamelCase_					=       processor_wavaveca.batch_decode(__UpperCamelCase )
												lowerCamelCase_					=       processor_auto.batch_decode(__UpperCamelCase )

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

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.get_feature_extractor()
												lowerCamelCase_					=       self.get_tokenizer()
												lowerCamelCase_					=       self.get_decoder()

												lowerCamelCase_					=       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  lowercase__   (						__UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       [d[key] for d in offsets]
												return retrieved_list

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
												lowerCamelCase_					=       self._get_dummy_logits()[0]

												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
												lowerCamelCase_					=       self._get_dummy_logits()

												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												import torch

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

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

												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase ).logits.cpu().numpy()

												lowerCamelCase_					=       processor.decode(logits[0] ,       output_word_offsets=__UpperCamelCase )

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

												lowerCamelCase_					=       """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
												lowerCamelCase_					=       torch.tensor(self.get_from_offsets(__UpperCamelCase ,       """start_time""" ) )
												lowerCamelCase_					=       torch.tensor(self.get_from_offsets(__UpperCamelCase ,       """end_time""" ) )

												# fmt: off
												lowerCamelCase_					=       torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
												lowerCamelCase_					=       torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
												# fmt: on

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







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

from tokenizers import processors

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


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		lowercase   =	None


lowercase   =	logging.get_logger(__name__)


lowercase   =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase   =	{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

lowercase   =	{
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
lowercase   =	['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE           =     MBartTokenizer

					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []

					def __init__(						self       :  Any ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  int=None ,       __UpperCamelCase       :  Dict="<s>" ,       __UpperCamelCase       :  Tuple="</s>" ,       __UpperCamelCase       :  Union[str, Any]="</s>" ,       __UpperCamelCase       :  Optional[Any]="<s>" ,       __UpperCamelCase       :  Optional[Any]="<unk>" ,       __UpperCamelCase       :  Dict="<pad>" ,       __UpperCamelCase       :  Dict="<mask>" ,       __UpperCamelCase       :  Any=None ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  Optional[int]=None ,       **__UpperCamelCase       :  str ,       ):
												# Mask token behave like a normal word, i.e. include the space before it
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else mask_token

												super().__init__(
												    vocab_file=__UpperCamelCase ,       tokenizer_file=__UpperCamelCase ,       bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       src_lang=__UpperCamelCase ,       tgt_lang=__UpperCamelCase ,       additional_special_tokens=__UpperCamelCase ,       **__UpperCamelCase ,       )

												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       False if not self.vocab_file else True

												lowerCamelCase_					=       FAIRSEQ_LANGUAGE_CODES.copy()

												if additional_special_tokens is not None:
																			# Only add those special tokens if they are not already there.
																			_additional_special_tokens.extend(
																			    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

												self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
												lowerCamelCase_					=       {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												lowerCamelCase_					=       src_lang if src_lang is not None else """en_XX"""
												lowerCamelCase_					=       self.convert_tokens_to_ids(self._src_lang )
												lowerCamelCase_					=       tgt_lang
												self.set_src_lang_special_tokens(self._src_lang )

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self._src_lang

					@src_lang.setter
					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       new_src_lang
												self.set_src_lang_special_tokens(self._src_lang )

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] ,       __UpperCamelCase       :  Optional[str] ,       **__UpperCamelCase       :  Dict ):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
												lowerCamelCase_					=       src_lang
												lowerCamelCase_					=       self(__UpperCamelCase ,       add_special_tokens=__UpperCamelCase ,       return_tensors=__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       tgt_lang_id
												return inputs

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str = "en_XX" ,       __UpperCamelCase       :  Optional[List[str]] = None ,       __UpperCamelCase       :  str = "ro_RO" ,       **__UpperCamelCase       :  Optional[int] ,       ):
												lowerCamelCase_					=       src_lang
												lowerCamelCase_					=       tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase ,       __UpperCamelCase ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  int ):
												return self.set_src_lang_special_tokens(self.src_lang )

					def  lowercase__   (						self       :  Dict ):
												return self.set_tgt_lang_special_tokens(self.tgt_lang )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       []
												lowerCamelCase_					=       [self.eos_token_id, self.cur_lang_code]

												lowerCamelCase_					=       self.convert_ids_to_tokens(self.prefix_tokens )
												lowerCamelCase_					=       self.convert_ids_to_tokens(self.suffix_tokens )

												lowerCamelCase_					=       processors.TemplateProcessing(
												    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,       self.prefix_tokens + self.suffix_tokens ) ) ,       )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       []
												lowerCamelCase_					=       [self.eos_token_id, self.cur_lang_code]

												lowerCamelCase_					=       self.convert_ids_to_tokens(self.prefix_tokens )
												lowerCamelCase_					=       self.convert_ids_to_tokens(self.suffix_tokens )

												lowerCamelCase_					=       processors.TemplateProcessing(
												    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,       self.prefix_tokens + self.suffix_tokens ) ) ,       )



					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																			    """tokenizer.""" )

												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
																			return
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

												return (out_vocab_file,)





