
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   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
					A__  :	Optional[Any]         =							dict(zip(UpperCamelCase__				,       range(len(UpperCamelCase__		)		)		)		)

					A__  :	str         =							{
					    '''unk_token''': '''<unk>''',
					    '''bos_token''': '''<s>''',
					    '''eos_token''': '''</s>''',
					}
					A__  :	Dict         =							{
					    '''feature_size''': 1,
					    '''padding_value''': 0.0,
					    '''sampling_rate''': 1_6000,
					    '''return_attention_mask''': False,
					    '''do_normalize''': True,
					}

					A__  :	Optional[int]         =							tempfile.mkdtemp()
					A__  :	Optional[Any]         =							os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['''vocab_file''']		)
					A__  :	str         =							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
					A__  :	Any         =							'''hf-internal-testing/ngram-beam-search-decoder'''





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					A__  :	int         =							self.add_kwargs_tokens_map.copy()
					kwargs.update(UpperCamelCase__		)
					return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,       **UpperCamelCase__		)





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_tokenizer()
					A__  :	Any         =							self.get_feature_extractor()
					A__  :	List[str]         =							self.get_decoder()

					A__  :	Dict         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					processor.save_pretrained(self.tmpdirname		)
					A__  :	Union[str, Any]         =							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   __snake_case				(			self		):
					A__  :	str         =							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
					A__  :	Tuple         =							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   __snake_case				(			self		):
					A__  :	List[str]         =							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   __snake_case				(			self		):
					A__  :	str         =							self.get_feature_extractor()
					A__  :	Dict         =							self.get_tokenizer()
					A__  :	str         =							self.get_decoder()

					A__  :	Optional[Any]         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	Tuple         =							floats_list((3, 1000)		)

					A__  :	List[Any]         =							feature_extractor(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	int         =							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   __snake_case				(			self		):
					A__  :	List[Any]         =							self.get_feature_extractor()
					A__  :	Optional[Any]         =							self.get_tokenizer()
					A__  :	List[Any]         =							self.get_decoder()

					A__  :	Dict         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	Union[str, Any]         =							'''This is a test string'''

					A__  :	Optional[Any]         =							processor(text=UpperCamelCase__		)

					A__  :	str         =							tokenizer(UpperCamelCase__		)

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





	def   __snake_case				(			self				,       UpperCamelCase__=(2, 10, 16)				,       UpperCamelCase__=77		):
					np.random.seed(UpperCamelCase__		)
					return np.random.rand(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_feature_extractor()
					A__  :	Dict         =							self.get_tokenizer()
					A__  :	Optional[Any]         =							self.get_decoder()

					A__  :	int         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	Any         =							self._get_dummy_logits(shape=(10, 16)				,       seed=13		)

					A__  :	str         =							processor.decode(UpperCamelCase__		)

					A__  :	List[Any]         =							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   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	int         =							self.get_feature_extractor()
					A__  :	int         =							self.get_tokenizer()
					A__  :	int         =							self.get_decoder()

					A__  :	str         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	Optional[Any]         =							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:
									A__  :	Tuple         =							processor.batch_decode(UpperCamelCase__		)
					else:
									with get_context(UpperCamelCase__		).Pool() as pool:
													A__  :	List[Any]         =							processor.batch_decode(UpperCamelCase__				,       UpperCamelCase__		)

					A__  :	Tuple         =							list(UpperCamelCase__		)

					with get_context('''fork'''		).Pool() as p:
									A__  :	str         =							decoder.decode_beams_batch(UpperCamelCase__				,       UpperCamelCase__		)

					A__       ,							A__       ,							A__  :	int         =							[], [], []
					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   __snake_case				(			self		):
					A__  :	List[Any]         =							self.get_feature_extractor()
					A__  :	List[str]         =							self.get_tokenizer()
					A__  :	Dict         =							self.get_decoder()

					A__  :	Optional[Any]         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	Optional[int]         =							self._get_dummy_logits()

					A__  :	Dict         =							15
					A__  :	Union[str, Any]         =							-2_0.0
					A__  :	List[Any]         =							-4.0

					A__  :	List[Any]         =							processor.batch_decode(
					    UpperCamelCase__				,       beam_width=UpperCamelCase__				,       beam_prune_logp=UpperCamelCase__				,       token_min_logp=UpperCamelCase__				,       )
					A__  :	Optional[Any]         =							decoded_processor_out.text

					A__  :	str         =							list(UpperCamelCase__		)

					with get_context('''fork'''		).Pool() as pool:
									A__  :	Dict         =							decoder.decode_beams_batch(
									    UpperCamelCase__				,       UpperCamelCase__				,       beam_width=UpperCamelCase__				,       beam_prune_logp=UpperCamelCase__				,       token_min_logp=UpperCamelCase__				,       )

					A__  :	List[str]         =							[d[0][0] for d in decoded_decoder_out]
					A__  :	Any         =							[d[0][2] for d in decoded_decoder_out]
					A__  :	List[Any]         =							[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([-2_0.0_5_4, -1_8.4_4_7]				,       UpperCamelCase__				,       atol=1e-3		)		)

					self.assertTrue(np.array_equal(UpperCamelCase__				,       decoded_processor_out.lm_score		)		)
					self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]				,       UpperCamelCase__				,       atol=1e-3		)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_feature_extractor()
					A__  :	Tuple         =							self.get_tokenizer()
					A__  :	Optional[int]         =							self.get_decoder()

					A__  :	List[Any]         =							WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__				,       feature_extractor=UpperCamelCase__				,       decoder=UpperCamelCase__		)

					A__  :	int         =							self._get_dummy_logits()

					A__  :	Optional[Any]         =							2.0
					A__  :	Union[str, Any]         =							5.0
					A__  :	Tuple         =							-2_0.0
					A__  :	Tuple         =							True

					A__  :	int         =							processor.batch_decode(
					    UpperCamelCase__				,       alpha=UpperCamelCase__				,       beta=UpperCamelCase__				,       unk_score_offset=UpperCamelCase__				,       lm_score_boundary=UpperCamelCase__				,       )
					A__  :	Any         =							decoded_processor_out.text

					A__  :	int         =							list(UpperCamelCase__		)
					decoder.reset_params(
					    alpha=UpperCamelCase__				,       beta=UpperCamelCase__				,       unk_score_offset=UpperCamelCase__				,       lm_score_boundary=UpperCamelCase__				,       )

					with get_context('''fork'''		).Pool() as pool:
									A__  :	Optional[int]         =							decoder.decode_beams_batch(
									    UpperCamelCase__				,       UpperCamelCase__				,       )

					A__  :	List[str]         =							[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__		)
					A__  :	Optional[int]         =							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				,       -2_0.0		)
					self.assertEqual(lm_model.score_boundary				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	str         =							WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)

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

					A__  :	Optional[Any]         =							os.listdir(UpperCamelCase__		)
					A__  :	Tuple         =							['''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   __snake_case				(			self		):
					A__  :	Optional[Any]         =							snapshot_download('''hf-internal-testing/processor_with_lm'''		)

					A__  :	Dict         =							WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__		)

					A__  :	List[str]         =							processor.decoder.model_container[processor.decoder._model_key]
					A__  :	int         =							Path(language_model._kenlm_model.path.decode('''utf-8'''		)		).parent.parent.absolute()

					A__  :	Tuple         =							os.listdir(UpperCamelCase__		)
					A__  :	int         =							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   __snake_case				(			self		):
					A__  :	Dict         =							WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
					A__  :	Any         =							AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''		)

					A__  :	Dict         =							floats_list((3, 1000)		)

					A__  :	List[Any]         =							processor_wavaveca(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	Tuple         =							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		)

					A__  :	int         =							self._get_dummy_logits()

					A__  :	str         =							processor_wavaveca.batch_decode(UpperCamelCase__		)
					A__  :	Union[str, Any]         =							processor_auto.batch_decode(UpperCamelCase__		)

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





	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_feature_extractor()
					A__  :	List[str]         =							self.get_tokenizer()
					A__  :	int         =							self.get_decoder()

					A__  :	List[Any]         =							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   __snake_case				(			UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							[d[key] for d in offsets]
					return retrieved_list





	def   __snake_case				(			self		):
					A__  :	str         =							WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
					A__  :	str         =							self._get_dummy_logits()[0]

					A__  :	str         =							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   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
					A__  :	Optional[int]         =							self._get_dummy_logits()

					A__  :	Any         =							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   __snake_case				(			self		):
					import torch

					A__  :	Any         =							load_dataset('''common_voice'''				,       '''en'''				,       split='''train'''				,       streaming=UpperCamelCase__		)
					A__  :	str         =							ds.cast_column('''audio'''				,       datasets.Audio(sampling_rate=1_6000		)		)
					A__  :	int         =							iter(UpperCamelCase__		)
					A__  :	Dict         =							next(UpperCamelCase__		)

					A__  :	str         =							AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''		)
					A__  :	Optional[Any]         =							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
					A__  :	Union[str, Any]         =							processor(sample['''audio''']['''array''']				,       return_tensors='''pt'''		).input_values

					with torch.no_grad():
									A__  :	Union[str, Any]         =							model(UpperCamelCase__		).logits.cpu().numpy()

					A__  :	Tuple         =							processor.decode(logits[0]				,       output_word_offsets=UpperCamelCase__		)

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

					A__  :	Union[str, Any]         =							'''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
					A__  :	List[str]         =							torch.tensor(self.get_from_offsets(UpperCamelCase__				,       '''start_time'''		)		)
					A__  :	Tuple         =							torch.tensor(self.get_from_offsets(UpperCamelCase__				,       '''end_time'''		)		)

					# fmt: off
					A__  :	str         =							torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]		)
					A__  :	List[str]         =							torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]		)
					# fmt: on

					self.assertTrue(torch.allclose(UpperCamelCase__				,       UpperCamelCase__				,       atol=0.0_1		)		)
					self.assertTrue(torch.allclose(UpperCamelCase__				,       UpperCamelCase__				,       atol=0.0_1		)		)

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids