


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list






class 				__lowerCAmelCase      (      unittest.TestCase	):

      """simple docstring"""






      def 							_UpperCAmelCase					(     self       :			List[str]  ):
             A_ 						=		'''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(" "  )
             A_ 						=		dict(zip(_snake_case   ,     range(len(_snake_case  )  )  )  )

             A_ 						=		{
                 '''pad_token''': '''<pad>''',
                 '''unk_token''': '''<unk>''',
                 '''bos_token''': '''<s>''',
                 '''eos_token''': '''</s>''',
             }
             A_ 						=		{
                 '''feature_size''': 1,
                 '''padding_value''': 0.0,
                 '''sampling_rate''': 1_60_00,
                 '''return_attention_mask''': False,
                 '''do_normalize''': True,
             }

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

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






      def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Union[str, Any]  ):
             A_ 						=		self.add_kwargs_tokens_map.copy()
             kwargs.update(_snake_case  )
             return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,     **_snake_case  )






      def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			int  ):
             return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **_snake_case  )






      def 							_UpperCAmelCase					(     self       :			str  ):
             shutil.rmtree(self.tmpdirname  )






      def 							_UpperCAmelCase					(     self       :			Tuple  ):
             A_ 						=		self.get_tokenizer()
             A_ 						=		self.get_feature_extractor()

             A_ 						=		WavaVecaProcessor(tokenizer=_snake_case   ,     feature_extractor=_snake_case  )

             processor.save_pretrained(self.tmpdirname  )
             A_ 						=		WavaVecaProcessor.from_pretrained(self.tmpdirname  )

             self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()  )
             self.assertIsInstance(processor.tokenizer   ,     _snake_case  )

             self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()  )
             self.assertIsInstance(processor.feature_extractor   ,     _snake_case  )






      def 							_UpperCAmelCase					(     self       :			List[Any]  ):
             A_ 						=		WavaVecaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
             processor.save_pretrained(self.tmpdirname  )

             A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
             A_ 						=		self.get_feature_extractor(do_normalize=_snake_case   ,     padding_value=1.0  )

             A_ 						=		WavaVecaProcessor.from_pretrained(
                 self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=_snake_case   ,     padding_value=1.0  )

             self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()  )
             self.assertIsInstance(processor.tokenizer   ,     _snake_case  )

             self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()  )
             self.assertIsInstance(processor.feature_extractor   ,     _snake_case  )






      def 							_UpperCAmelCase					(     self       :			str  ):
             A_ 						=		self.get_feature_extractor()
             A_ 						=		self.get_tokenizer()

             A_ 						=		WavaVecaProcessor(tokenizer=_snake_case   ,     feature_extractor=_snake_case  )

             A_ 						=		floats_list((3, 10_00)  )

             A_ 						=		feature_extractor(_snake_case   ,     return_tensors="np"  )
             A_ 						=		processor(_snake_case   ,     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 							_UpperCAmelCase					(     self       :			Optional[Any]  ):
             A_ 						=		self.get_feature_extractor()
             A_ 						=		self.get_tokenizer()

             A_ 						=		WavaVecaProcessor(tokenizer=_snake_case   ,     feature_extractor=_snake_case  )

             A_ 						=		'''This is a test string'''

             A_ 						=		processor(text=_snake_case  )

             A_ 						=		tokenizer(_snake_case  )

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






      def 							_UpperCAmelCase					(     self       :			int  ):
             A_ 						=		self.get_feature_extractor()
             A_ 						=		self.get_tokenizer()

             A_ 						=		WavaVecaProcessor(tokenizer=_snake_case   ,     feature_extractor=_snake_case  )

             A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             A_ 						=		processor.batch_decode(_snake_case  )
             A_ 						=		tokenizer.batch_decode(_snake_case  )

             self.assertListEqual(_snake_case   ,     _snake_case  )






      def 							_UpperCAmelCase					(     self       :			Any  ):
             A_ 						=		self.get_feature_extractor()
             A_ 						=		self.get_tokenizer()

             A_ 						=		WavaVecaProcessor(tokenizer=_snake_case   ,     feature_extractor=_snake_case  )

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









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





