

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    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    int 	=			'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
								__lowerCamelCase    :    Any 	=			dict(zip(UpperCamelCase__       ,range(len(UpperCamelCase__))))

								__lowerCamelCase    :    str 	=			{
								    'unk_token': '<unk>',
								    'bos_token': '<s>',
								    'eos_token': '</s>',
								}
								__lowerCamelCase    :    Union[str, Any] 	=			{
								    'feature_size': 1,
								    'padding_value': 0.0,
								    'sampling_rate': 1_6_0_0_0,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}

								__lowerCamelCase    :    int 	=			tempfile.mkdtemp()
								__lowerCamelCase    :    Union[str, Any] 	=			os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['vocab_file'])
								__lowerCamelCase    :    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
								__lowerCamelCase    :    Tuple 	=			'hf-internal-testing/ngram-beam-search-decoder'
							def    lowerCAmelCase					(		self		:      Tuple       ,**SCREAMING_SNAKE_CASE__		:      Tuple):
								__lowerCamelCase    :    List[Any] 	=			self.add_kwargs_tokens_map.copy()
								kwargs.update(UpperCamelCase__)
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,**UpperCamelCase__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
								return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,**UpperCamelCase__)
							def    lowerCAmelCase					(		self		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name       ,**UpperCamelCase__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								shutil.rmtree(self.tmpdirname)
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_tokenizer()
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_feature_extractor()
								__lowerCamelCase    :    Any 	=			self.get_decoder()

								__lowerCamelCase    :    Union[str, Any] 	=			WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__       ,feature_extractor=UpperCamelCase__       ,decoder=UpperCamelCase__)

								processor.save_pretrained(self.tmpdirname)
								__lowerCamelCase    :    List[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    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			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    :    Any 	=			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    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			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    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Dict 	=			self.get_feature_extractor()
								__lowerCamelCase    :    str 	=			self.get_tokenizer()
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_decoder()

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

								__lowerCamelCase    :    int 	=			floats_list((3, 1_0_0_0))

								__lowerCamelCase    :    int 	=			feature_extractor(UpperCamelCase__       ,return_tensors='np')
								__lowerCamelCase    :    Dict 	=			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    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Optional[Any] 	=			self.get_feature_extractor()
								__lowerCamelCase    :    Optional[Any] 	=			self.get_tokenizer()
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_decoder()

								__lowerCamelCase    :    Tuple 	=			WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__       ,feature_extractor=UpperCamelCase__       ,decoder=UpperCamelCase__)

								__lowerCamelCase    :    List[str] 	=			'This is a test string'

								__lowerCamelCase    :    List[Any] 	=			processor(text=UpperCamelCase__)

								__lowerCamelCase    :    Tuple 	=			tokenizer(UpperCamelCase__)

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]       ,encoded_processor[key])
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]=(2, 1_0, 1_6)       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=7_7):
								np.random.seed(UpperCamelCase__)
								return np.random.rand(*UpperCamelCase__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_feature_extractor()
								__lowerCamelCase    :    Tuple 	=			self.get_tokenizer()
								__lowerCamelCase    :    List[Any] 	=			self.get_decoder()

								__lowerCamelCase    :    List[str] 	=			WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__       ,feature_extractor=UpperCamelCase__       ,decoder=UpperCamelCase__)

								__lowerCamelCase    :    List[Any] 	=			self._get_dummy_logits(shape=(1_0, 1_6)       ,seed=1_3)

								__lowerCamelCase    :    str 	=			processor.decode(UpperCamelCase__)

								__lowerCamelCase    :    Optional[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    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_feature_extractor()
								__lowerCamelCase    :    str 	=			self.get_tokenizer()
								__lowerCamelCase    :    List[Any] 	=			self.get_decoder()

								__lowerCamelCase    :    Optional[int] 	=			WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__       ,feature_extractor=UpperCamelCase__       ,decoder=UpperCamelCase__)

								__lowerCamelCase    :    int 	=			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    :    Any 	=			processor.batch_decode(UpperCamelCase__)
								else:
									with get_context(UpperCamelCase__).Pool() as pool:
										__lowerCamelCase    :    Optional[int] 	=			processor.batch_decode(UpperCamelCase__       ,UpperCamelCase__)

								__lowerCamelCase    :    Dict 	=			list(UpperCamelCase__)

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

								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			[], [], []
								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    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    str 	=			self.get_feature_extractor()
								__lowerCamelCase    :    List[Any] 	=			self.get_tokenizer()
								__lowerCamelCase    :    str 	=			self.get_decoder()

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

								__lowerCamelCase    :    List[Any] 	=			self._get_dummy_logits()

								__lowerCamelCase    :    Any 	=			1_5
								__lowerCamelCase    :    Optional[int] 	=			-20.0
								__lowerCamelCase    :    Any 	=			-4.0

								__lowerCamelCase    :    Any 	=			processor.batch_decode(
								    UpperCamelCase__       ,beam_width=UpperCamelCase__       ,beam_prune_logp=UpperCamelCase__       ,token_min_logp=UpperCamelCase__       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			decoded_processor_out.text

								__lowerCamelCase    :    Any 	=			list(UpperCamelCase__)

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

								__lowerCamelCase    :    Tuple 	=			[d[0][0] for d in decoded_decoder_out]
								__lowerCamelCase    :    str 	=			[d[0][2] for d in decoded_decoder_out]
								__lowerCamelCase    :    List[str] 	=			[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    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			self.get_feature_extractor()
								__lowerCamelCase    :    Dict 	=			self.get_tokenizer()
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_decoder()

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

								__lowerCamelCase    :    Optional[Any] 	=			self._get_dummy_logits()

								__lowerCamelCase    :    Tuple 	=			2.0
								__lowerCamelCase    :    Optional[Any] 	=			5.0
								__lowerCamelCase    :    Optional[int] 	=			-20.0
								__lowerCamelCase    :    List[str] 	=			True

								__lowerCamelCase    :    Optional[Any] 	=			processor.batch_decode(
								    UpperCamelCase__       ,alpha=UpperCamelCase__       ,beta=UpperCamelCase__       ,unk_score_offset=UpperCamelCase__       ,lm_score_boundary=UpperCamelCase__       ,)
								__lowerCamelCase    :    Tuple 	=			decoded_processor_out.text

								__lowerCamelCase    :    List[str] 	=			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    :    str 	=			decoder.decode_beams_batch(
									    UpperCamelCase__       ,UpperCamelCase__       ,)

								__lowerCamelCase    :    List[Any] 	=			[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    :    Optional[Any] 	=			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    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Dict 	=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')

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

								__lowerCamelCase    :    Optional[Any] 	=			os.listdir(UpperCamelCase__)
								__lowerCamelCase    :    List[Any] 	=			['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    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Optional[int] 	=			snapshot_download('hf-internal-testing/processor_with_lm')

								__lowerCamelCase    :    Union[str, Any] 	=			WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__)

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

								__lowerCamelCase    :    Optional[Any] 	=			os.listdir(UpperCamelCase__)
								__lowerCamelCase    :    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    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Any 	=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
								__lowerCamelCase    :    Optional[Any] 	=			AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm')

								__lowerCamelCase    :    List[str] 	=			floats_list((3, 1_0_0_0))

								__lowerCamelCase    :    Optional[Any] 	=			processor_wavaveca(UpperCamelCase__       ,return_tensors='np')
								__lowerCamelCase    :    int 	=			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    :    Optional[int] 	=			self._get_dummy_logits()

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

								self.assertListEqual(decoded_wavaveca.text       ,decoded_auto.text)
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    List[Any] 	=			self.get_feature_extractor()
								__lowerCamelCase    :    str 	=			self.get_tokenizer()
								__lowerCamelCase    :    Tuple 	=			self.get_decoder()

								__lowerCamelCase    :    Optional[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    lowerCAmelCase					(		SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			[d[key] for d in offsets]
								return retrieved_list
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Optional[Any] 	=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
								__lowerCamelCase    :    int 	=			self._get_dummy_logits()[0]

								__lowerCamelCase    :    List[Any] 	=			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    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
								__lowerCamelCase    :    List[str] 	=			self._get_dummy_logits()

								__lowerCamelCase    :    List[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    lowerCAmelCase					(		self		:      Optional[int]):
								import torch

								__lowerCamelCase    :    Optional[int] 	=			load_dataset('common_voice'       ,'en'       ,split='train'       ,streaming=UpperCamelCase__)
								__lowerCamelCase    :    Optional[Any] 	=			ds.cast_column('audio'       ,datasets.Audio(sampling_rate=1_6_0_0_0))
								__lowerCamelCase    :    List[Any] 	=			iter(UpperCamelCase__)
								__lowerCamelCase    :    Any 	=			next(UpperCamelCase__)

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

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

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

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

								__lowerCamelCase    :    Tuple 	=			'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    :    Optional[Any] 	=			torch.tensor(self.get_from_offsets(UpperCamelCase__       ,'start_time'))
								__lowerCamelCase    :    int 	=			torch.tensor(self.get_from_offsets(UpperCamelCase__       ,'end_time'))

								# fmt: off
								__lowerCamelCase    :    Tuple 	=			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    :    Optional[Any] 	=			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 math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a		="""true"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Optional[int] 	=			Accelerator(dispatch_batches=lowerCamelCase__     ,			split_batches=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			get_dataloader(lowerCamelCase__     ,			not dispatch_batches      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'     ,			return_dict=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = False     ,			lowerCamelCase__ = False      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )
	__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			get_mrpc_setup(lowerCamelCase__     ,			lowerCamelCase__      )
	# First do baseline
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			setup['no']
	model.to(lowerCamelCase__      )
	model.eval()
	for batch in dataloader:
		batch.to(lowerCamelCase__      )
		with torch.inference_mode():
			__lowerCamelCase    :    Dict 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			outputs.logits.argmax(dim=-1      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=batch['labels']      )
	__lowerCamelCase    :    str 	=			metric.compute()

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]     ,			distributed[key]      ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('**Testing gather_for_metrics**'      )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
				test_mrpc(lowerCamelCase__     ,			lowerCamelCase__      )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test torch metrics**'      )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			__lowerCamelCase    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




