


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


_SCREAMING_SNAKE_CASE       :   List[str]        = random.Random()



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__=1.0     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None		):






				"""simple docstring"""



				if rng is None:
								__magic_name__				:					Optional[int]						       =       global_rng

				__magic_name__				:					Union[str, Any]						       =       []
				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 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						def lowerCAmelCase__ (  self:	str     )							->	Optional[Any]:
										__magic_name__				:					List[str]						       =       "facebook/musicgen-small"
										__magic_name__				:					Union[str, Any]						       =       tempfile.mkdtemp()







						def lowerCAmelCase__ (  self:	int      ,							**__UpperCamelCase:	Tuple     )							->	Union[str, Any]:
										return TaTokenizer.from_pretrained(self.checkpoint      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[Any]      ,							**__UpperCamelCase:	Optional[Any]     )							->	Optional[Any]:
										return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	str:
										shutil.rmtree(self.tmpdirname     )







						def lowerCAmelCase__ (  self:	Tuple     )							->	Dict:
										__magic_name__				:					str						       =       self.get_tokenizer()
										__magic_name__				:					Union[str, Any]						       =       self.get_feature_extractor()

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

										processor.save_pretrained(self.tmpdirname     )
										__magic_name__				:					List[str]						       =       MusicgenProcessor.from_pretrained(self.tmpdirname     )

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

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







						def lowerCAmelCase__ (  self:	Dict     )							->	Union[str, Any]:
										__magic_name__				:					List[Any]						       =       MusicgenProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()     )
										processor.save_pretrained(self.tmpdirname     )

										__magic_name__				:					str						       =       self.get_tokenizer(bos_token="(BOS)"      ,							eos_token="(EOS)"     )
										__magic_name__				:					Tuple						       =       self.get_feature_extractor(do_normalize=__UpperCamelCase      ,							padding_value=1.0     )

										__magic_name__				:					List[str]						       =       MusicgenProcessor.from_pretrained(
										    self.tmpdirname      ,							bos_token="(BOS)"      ,							eos_token="(EOS)"      ,							do_normalize=__UpperCamelCase      ,							padding_value=1.0     )

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

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







						def lowerCAmelCase__ (  self:	List[Any]     )							->	Any:
										__magic_name__				:					str						       =       self.get_feature_extractor()
										__magic_name__				:					List[Any]						       =       self.get_tokenizer()

										__magic_name__				:					Union[str, Any]						       =       MusicgenProcessor(tokenizer=__UpperCamelCase      ,							feature_extractor=__UpperCamelCase     )

										__magic_name__				:					Optional[Any]						       =       floats_list((3, 1000)     )

										__magic_name__				:					Tuple						       =       feature_extractor(__UpperCamelCase      ,							return_tensors="np"     )
										__magic_name__				:					Any						       =       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:	Tuple     )							->	Tuple:
										__magic_name__				:					List[str]						       =       self.get_feature_extractor()
										__magic_name__				:					Any						       =       self.get_tokenizer()

										__magic_name__				:					Any						       =       MusicgenProcessor(tokenizer=__UpperCamelCase      ,							feature_extractor=__UpperCamelCase     )

										__magic_name__				:					Optional[Any]						       =       "This is a test string"

										__magic_name__				:					List[str]						       =       processor(text=__UpperCamelCase     )

										__magic_name__				:					List[Any]						       =       tokenizer(__UpperCamelCase     )

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







						def lowerCAmelCase__ (  self:	str     )							->	str:
										__magic_name__				:					Tuple						       =       self.get_feature_extractor()
										__magic_name__				:					Any						       =       self.get_tokenizer()

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

										__magic_name__				:					int						       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__				:					Tuple						       =       processor.batch_decode(sequences=__UpperCamelCase     )
										__magic_name__				:					List[Any]						       =       tokenizer.batch_decode(__UpperCamelCase     )

										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	List[str]:
										__magic_name__				:					List[Any]						       =       self.get_feature_extractor()
										__magic_name__				:					Optional[Any]						       =       self.get_tokenizer()

										__magic_name__				:					Dict						       =       MusicgenProcessor(tokenizer=__UpperCamelCase      ,							feature_extractor=__UpperCamelCase     )

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







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					int						       =       self.get_feature_extractor(padding_side="left"     )
										__magic_name__				:					Dict						       =       self.get_tokenizer()

										__magic_name__				:					List[Any]						       =       MusicgenProcessor(tokenizer=__UpperCamelCase      ,							feature_extractor=__UpperCamelCase     )

										__magic_name__				:					List[Any]						       =       [floats_list((1, x)     )[0] for x in range(5      ,							20      ,							5     )]
										__magic_name__				:					List[str]						       =       processor(__UpperCamelCase     ).padding_mask

										__magic_name__				:					int						       =       np.asarray(floats_list((3, 20)     )     )[:, None, :]
										__magic_name__				:					List[str]						       =       processor.batch_decode(__UpperCamelCase      ,							padding_mask=__UpperCamelCase     )

										self.assertIsInstance(__UpperCamelCase      ,							__UpperCamelCase     )

										for audio in decoded_audios:
														self.assertIsInstance(__UpperCamelCase      ,							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 unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]