
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__  :	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]         =							-20.0
     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([-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   __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         =							-20.0
     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				,       -20.0		)
     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 argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE    :      str          =			1_6
_SCREAMING_SNAKE_CASE    :      Tuple          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				A__  :	Optional[int]         =							load_dataset('''glue'''							,						'''mrpc'''       )

				def tokenize_function(__UpperCamelCase :						Union[str, Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							tokenizer(examples['''sentence1''']							,						examples['''sentence2''']							,						truncation=__UpperCamelCase							,						max_length=__UpperCamelCase       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								A__  :	Optional[int]         =							datasets.map(
								    __UpperCamelCase							,						batched=__UpperCamelCase							,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				A__  :	List[Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Optional[Any]         =							1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__  :	int         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Any         =							8
								else:
												A__  :	Union[str, Any]         =							None

								return tokenizer.pad(
								    __UpperCamelCase							,						padding='''longest'''							,						max_length=__UpperCamelCase							,						pad_to_multiple_of=__UpperCamelCase							,						return_tensors='''pt'''							,						)

				# Instantiate dataloaders.
				A__  :	Optional[int]         =							DataLoader(
				    tokenized_datasets['''train''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )
				A__  :	Tuple         =							DataLoader(
				    tokenized_datasets['''validation''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_SCREAMING_SNAKE_CASE    :      Dict          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,						__UpperCamelCase       ) == "1":
								A__  :	List[str]         =							2
				# Initialize accelerator
				A__  :	Optional[Any]         =							Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__  :	Tuple         =							config['''lr''']
				A__  :	Dict         =							int(config['''num_epochs''']       )
				A__  :	int         =							int(config['''seed''']       )
				A__  :	Optional[Any]         =							int(config['''batch_size''']       )

				A__  :	int         =							evaluate.load('''glue'''							,						'''mrpc'''       )

				# If the batch size is too big we use gradient accumulation
				A__  :	Union[str, Any]         =							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__  :	List[Any]         =							batch_size // MAX_GPU_BATCH_SIZE
								A__  :	Dict         =							MAX_GPU_BATCH_SIZE

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	int         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Optional[int]         =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,						return_dict=__UpperCamelCase       )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				A__  :	Tuple         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Optional[int]         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	Any         =							get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase							,						num_warmup_steps=1_00							,						num_training_steps=(len(__UpperCamelCase       ) * num_epochs) // gradient_accumulation_steps							,						)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# Now we train the model
				for epoch in range(__UpperCamelCase       ):
								model.train()
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												A__  :	Dict         =							model(**__UpperCamelCase       )
												A__  :	Dict         =							outputs.loss
												A__  :	List[str]         =							loss / gradient_accumulation_steps
												accelerator.backward(__UpperCamelCase       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								A__  :	Optional[int]         =							0
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )
												A__  :	int         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	Optional[Any]         =							accelerator.gather((predictions, batch['''labels'''])       )
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(__UpperCamelCase       ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				A__  :	Tuple         =							predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				A__  :	int         =							references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

								A__  :	Union[str, Any]         =							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	Tuple         =							argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''							,						type=__UpperCamelCase							,						default=__UpperCamelCase							,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']							,						help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''							,						)
				parser.add_argument('''--cpu'''							,						action='''store_true'''							,						help='''If passed, will train on the CPU.'''       )
				A__  :	Dict         =							parser.parse_args()
				A__  :	Any         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()