



'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
						from huggingface_hub import snapshot_download
						from pyctcdecode import BeamSearchDecoderCTC

						from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
						from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
						from transformers import WavaVecaForCTC




@require_pyctcdecode
class      __UpperCamelCase       (unittest.TestCase			):







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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

							lowercase        =							{
							    """unk_token""": """<unk>""",
							    """bos_token""": """<s>""",
							    """eos_token""": """</s>""",
							}
							lowercase        =							{
							    """feature_size""": 1,
							    """padding_value""": 0.0,
							    """sampling_rate""": 1_6000,
							    """return_attention_mask""": False,
							    """do_normalize""": True,
							}

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

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

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







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.add_kwargs_tokens_map.copy()
							kwargs.update(_lowerCAmelCase						)
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






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







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

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

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

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

							# 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 ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							WavaVecaProcessorWithLM(
							    tokenizer=self.get_tokenizer() ,				feature_extractor=self.get_feature_extractor() ,				decoder=self.get_decoder()						)
							processor.save_pretrained(self.tmpdirname						)

							# make sure that error is thrown when decoder alphabet doesn't match
							lowercase        =							WavaVecaProcessorWithLM.from_pretrained(
							    self.tmpdirname ,				alpha=5.0 ,				beta=3.0 ,				score_boundary=-7.0 ,				unk_score_offset=3						)

							# decoder
							self.assertEqual(processor.language_model.alpha ,				5.0						)
							self.assertEqual(processor.language_model.beta ,				3.0						)
							self.assertEqual(processor.language_model.score_boundary ,				-7.0						)
							self.assertEqual(processor.language_model.unk_score_offset ,				3						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							floats_list((3, 1000)						)

							lowercase        =							feature_extractor(_lowerCAmelCase ,				return_tensors="""np"""						)
							lowercase        =							processor(_lowerCAmelCase ,				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   _a   (    self						)    ->    int:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							"""This is a test string"""

							lowercase        =							processor(text=_lowerCAmelCase						)

							lowercase        =							tokenizer(_lowerCAmelCase						)

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







	def   _a   (    self ,				_lowerCAmelCase=(2, 10, 16) ,				_lowerCAmelCase=77						)    ->    List[Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							self._get_dummy_logits(shape=(10, 16) ,				seed=13						)

							lowercase        =							processor.decode(_lowerCAmelCase						)

							lowercase        =							decoder.decode_beams(_lowerCAmelCase						)[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   _a   (    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							self._get_dummy_logits()

							# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
							#       otherwise, the LM won't be available to the pool's sub-processes.
							# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
							if pool_context is None:
													lowercase        =							processor.batch_decode(_lowerCAmelCase						)
							else:
													with get_context(_lowerCAmelCase						).Pool() as pool:
																			lowercase        =							processor.batch_decode(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase        =							list(_lowerCAmelCase						)

							with get_context("""fork"""						).Pool() as p:
													lowercase        =							decoder.decode_beams_batch(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase     ,   lowercase     ,   lowercase        =							[], [], []
							for beams in decoded_beams:
													texts_decoder.append(beams[0][0]						)
													logit_scores_decoder.append(beams[0][-2]						)
													lm_scores_decoder.append(beams[0][-1]						)

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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							self._get_dummy_logits()

							lowercase        =							15
							lowercase        =							-20.0
							lowercase        =							-4.0

							lowercase        =							processor.batch_decode(
							    _lowerCAmelCase ,				beam_width=_lowerCAmelCase ,				beam_prune_logp=_lowerCAmelCase ,				token_min_logp=_lowerCAmelCase ,				)
							lowercase        =							decoded_processor_out.text

							lowercase        =							list(_lowerCAmelCase						)

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

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

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

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

							self.assertTrue(np.array_equal(_lowerCAmelCase ,				decoded_processor_out.lm_score						)						)
							self.assertTrue(np.allclose([-15.554, -13.9474] ,				_lowerCAmelCase ,				atol=1E-3						)						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

							lowercase        =							self._get_dummy_logits()

							lowercase        =							2.0
							lowercase        =							5.0
							lowercase        =							-20.0
							lowercase        =							True

							lowercase        =							processor.batch_decode(
							    _lowerCAmelCase ,				alpha=_lowerCAmelCase ,				beta=_lowerCAmelCase ,				unk_score_offset=_lowerCAmelCase ,				lm_score_boundary=_lowerCAmelCase ,				)
							lowercase        =							decoded_processor_out.text

							lowercase        =							list(_lowerCAmelCase						)
							decoder.reset_params(
							    alpha=_lowerCAmelCase ,				beta=_lowerCAmelCase ,				unk_score_offset=_lowerCAmelCase ,				lm_score_boundary=_lowerCAmelCase ,				)

							with get_context("""fork"""						).Pool() as pool:
													lowercase        =							decoder.decode_beams_batch(
													    _lowerCAmelCase ,				_lowerCAmelCase ,				)

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

							self.assertListEqual(_lowerCAmelCase ,				_lowerCAmelCase						)
							self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,				_lowerCAmelCase						)
							lowercase        =							processor.decoder.model_container[processor.decoder._model_key]
							self.assertEqual(lm_model.alpha ,				2.0						)
							self.assertEqual(lm_model.beta ,				5.0						)
							self.assertEqual(lm_model.unk_score_offset ,				-20.0						)
							self.assertEqual(lm_model.score_boundary ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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

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

							lowercase        =							os.listdir(_lowerCAmelCase						)
							lowercase        =							["""alphabet.json""", """language_model"""]

							downloaded_decoder_files.sort()
							expected_decoder_files.sort()

							# test that only decoder relevant files from
							# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
							# are downloaded and none of the rest (e.g. README.md, ...)
							self.assertListEqual(_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM.from_pretrained(_lowerCAmelCase						)

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

							lowercase        =							os.listdir(_lowerCAmelCase						)
							lowercase        =							os.listdir(_lowerCAmelCase						)

							local_decoder_files.sort()
							expected_decoder_files.sort()

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







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






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

							lowercase        =							floats_list((3, 1000)						)

							lowercase        =							processor_wavaveca(_lowerCAmelCase ,				return_tensors="""np"""						)
							lowercase        =							processor_auto(_lowerCAmelCase ,				return_tensors="""np"""						)

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

							lowercase        =							self._get_dummy_logits()

							lowercase        =							processor_wavaveca.batch_decode(_lowerCAmelCase						)
							lowercase        =							processor_auto.batch_decode(_lowerCAmelCase						)

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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






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

							lowercase        =							WavaVecaProcessorWithLM(tokenizer=_lowerCAmelCase ,				feature_extractor=_lowerCAmelCase ,				decoder=_lowerCAmelCase						)

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







	@staticmethod
	def   _a   (    _lowerCAmelCase ,				_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






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

							lowercase        =							processor.decode(_lowerCAmelCase ,				output_word_offsets=_lowerCAmelCase						)
							# 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(_lowerCAmelCase ,				_lowerCAmelCase						)						)

							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   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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

							lowercase        =							processor.batch_decode(_lowerCAmelCase ,				output_word_offsets=_lowerCAmelCase						)

							# 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(_lowerCAmelCase ,				_lowerCAmelCase						)						)

							self.assertListEqual(
							    [""" """.join(self.get_from_offsets(_lowerCAmelCase ,				"""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   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							import torch

							lowercase        =							load_dataset("""common_voice""" ,				"""en""" ,				split="""train""" ,				streaming=_lowerCAmelCase						)
							lowercase        =							ds.cast_column("""audio""" ,				datasets.Audio(sampling_rate=1_6000						)						)
							lowercase        =							iter(_lowerCAmelCase						)
							lowercase        =							next(_lowerCAmelCase						)

							lowercase        =							AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""						)
							lowercase        =							WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""						)

							# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
							lowercase        =							processor(sample["""audio"""]["""array"""] ,				return_tensors="""pt"""						).input_values

							with torch.no_grad():
													lowercase        =							model(_lowerCAmelCase						).logits.cpu().numpy()

							lowercase        =							processor.decode(logits[0] ,				output_word_offsets=_lowerCAmelCase						)

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

							lowercase        =							"""WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

							# output words
							self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase ,				"""word"""						)						) ,				_lowerCAmelCase						)
							self.assertEqual(""" """.join(self.get_from_offsets(_lowerCAmelCase ,				"""word"""						)						) ,				output.text						)

							# output times
							lowercase        =							torch.tensor(self.get_from_offsets(_lowerCAmelCase ,				"""start_time"""						)						)
							lowercase        =							torch.tensor(self.get_from_offsets(_lowerCAmelCase ,				"""end_time"""						)						)

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







'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							HfArgumentParser(lowercase_						)
						lowercase        =							parser.parse_args_into_dataclasses()[0]
						lowercase        =							TensorFlowBenchmark(args=lowercase_						)
						try:
												lowercase        =							parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase        =							"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
												lowercase        =							""" """.join(str(lowercase_						).split(""" """						)[:-1]						)
												lowercase        =							""""""
												lowercase        =							eval(str(lowercase_						).split(""" """						)[-1]						)
												lowercase        =							[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]						)
																		else:
																								wrong_args.append(lowercase_						)
												if len(lowercase_						) > 0:
																		lowercase        =							full_error_msg + begin_error_msg + str(lowercase_						)
												raise ValueError(lowercase_						)
						benchmark.run()


if __name__ == "__main__":
						main()


