'''simple docstring'''




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
						pass

if is_speech_available():
						from transformers import EncodecFeatureExtractor, MusicgenProcessor


_lowercase		:					Union[str, Any]													=		random.Random()




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Union[str, Any]=1.0 , UpperCAmelCase__			:   List[Any]=None , UpperCAmelCase__			:   str=None					)     -> Tuple:





							if rng is None:
														lowercase_   :     Tuple									=			global_rng

							lowercase_   :     List[str]									=			[]
							for batch_idx in range(shape[0]					):
														values.append([]					)
														for _ in range(shape[1]					):
																					values[-1].append(rng.random() * scale					)

							return values





@require_torch
@require_sentencepiece
class        __magic_name__		(       unittest.TestCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Optional[int]									=			"""facebook/musicgen-small"""
										lowercase_   :     Tuple									=			tempfile.mkdtemp()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     **lowercase_							:    Union[str, Any]       ):
										return TaTokenizer.from_pretrained(self.checkpoint							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     **lowercase_							:    Any       ):
										return EncodecFeatureExtractor.from_pretrained(self.checkpoint							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										shutil.rmtree(self.tmpdirname       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     int									=			self.get_tokenizer()
										lowercase_   :     List[str]									=			self.get_feature_extractor()

										lowercase_   :     Optional[Any]									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

										processor.save_pretrained(self.tmpdirname       )
										lowercase_   :     int									=			MusicgenProcessor.from_pretrained(self.tmpdirname       )

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

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     str									=			MusicgenProcessor(tokenizer=self.get_tokenizer()							,     feature_extractor=self.get_feature_extractor()       )
										processor.save_pretrained(self.tmpdirname       )

										lowercase_   :     Optional[int]									=			self.get_tokenizer(bos_token="""(BOS)"""							,     eos_token="""(EOS)"""       )
										lowercase_   :     Optional[int]									=			self.get_feature_extractor(do_normalize=lowercase_							,     padding_value=1.0       )

										lowercase_   :     Any									=			MusicgenProcessor.from_pretrained(
										    self.tmpdirname							,     bos_token="""(BOS)"""							,     eos_token="""(EOS)"""							,     do_normalize=lowercase_							,     padding_value=1.0       )

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

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     List[str]									=			self.get_feature_extractor()
										lowercase_   :     Any									=			self.get_tokenizer()

										lowercase_   :     List[str]									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

										lowercase_   :     Any									=			floats_list((3, 1000)       )

										lowercase_   :     List[Any]									=			feature_extractor(lowercase_							,     return_tensors="""np"""       )
										lowercase_   :     List[Any]									=			processor(lowercase_							,     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       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     int									=			self.get_feature_extractor()
										lowercase_   :     int									=			self.get_tokenizer()

										lowercase_   :     int									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

										lowercase_   :     int									=			"""This is a test string"""

										lowercase_   :     int									=			processor(text=lowercase_       )

										lowercase_   :     Optional[Any]									=			tokenizer(lowercase_       )

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     Union[str, Any]									=			self.get_feature_extractor()
										lowercase_   :     Union[str, Any]									=			self.get_tokenizer()

										lowercase_   :     str									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

										lowercase_   :     Tuple									=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowercase_   :     Optional[Any]									=			processor.batch_decode(sequences=lowercase_       )
										lowercase_   :     List[str]									=			tokenizer.batch_decode(lowercase_       )

										self.assertListEqual(lowercase_							,     lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										lowercase_   :     int									=			self.get_feature_extractor()
										lowercase_   :     List[str]									=			self.get_tokenizer()

										lowercase_   :     Any									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     int									=			self.get_feature_extractor(padding_side="""left"""       )
										lowercase_   :     Dict									=			self.get_tokenizer()

										lowercase_   :     Optional[Any]									=			MusicgenProcessor(tokenizer=lowercase_							,     feature_extractor=lowercase_       )

										lowercase_   :     Optional[Any]									=			[floats_list((1, x)       )[0] for x in range(5							,     20							,     5       )]
										lowercase_   :     int									=			processor(lowercase_       ).padding_mask

										lowercase_   :     Union[str, Any]									=			np.asarray(floats_list((3, 20)       )       )[:, None, :]
										lowercase_   :     Optional[int]									=			processor.batch_decode(lowercase_							,     padding_mask=lowercase_       )

										self.assertIsInstance(lowercase_							,     lowercase_       )

										for audio in decoded_audios:
																	self.assertIsInstance(lowercase_							,     np.ndarray       )

										self.assertTrue(decoded_audios[0].shape == (1, 10)       )
										self.assertTrue(decoded_audios[1].shape == (1, 15)       )
										self.assertTrue(decoded_audios[2].shape == (1, 20)       )







"""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)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Tuple			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			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(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




