



"""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


UpperCAmelCase:   Any       			=							random.Random()


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase=1.0					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None	):
		if rng is None:
				_lowercase							:      Union[str, Any]			=					global_rng

		_lowercase							:      int			=					[]
		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    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					"""facebook/musicgen-small"""
			_lowercase							:      Tuple			=					tempfile.mkdtemp()






	def 					lowerCamelCase__ (			self							,**UpperCAmelCase_					):
			return TaTokenizer.from_pretrained(self.checkpoint							,**UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,**UpperCAmelCase_					):
			return EncodecFeatureExtractor.from_pretrained(self.checkpoint							,**UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			shutil.rmtree(self.tmpdirname					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					self.get_tokenizer()
			_lowercase							:      Dict			=					self.get_feature_extractor()

			_lowercase							:      Tuple			=					MusicgenProcessor(tokenizer=UpperCAmelCase_							,feature_extractor=UpperCAmelCase_					)

			processor.save_pretrained(self.tmpdirname					)
			_lowercase							:      Optional[int]			=					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					):
			_lowercase							:      int			=					MusicgenProcessor(tokenizer=self.get_tokenizer()							,feature_extractor=self.get_feature_extractor()					)
			processor.save_pretrained(self.tmpdirname					)

			_lowercase							:      List[str]			=					self.get_tokenizer(bos_token="""(BOS)"""							,eos_token="""(EOS)"""					)
			_lowercase							:      Tuple			=					self.get_feature_extractor(do_normalize=UpperCAmelCase_							,padding_value=1.0					)

			_lowercase							:      Optional[Any]			=					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					):
			_lowercase							:      Union[str, Any]			=					self.get_feature_extractor()
			_lowercase							:      Any			=					self.get_tokenizer()

			_lowercase							:      Union[str, Any]			=					MusicgenProcessor(tokenizer=UpperCAmelCase_							,feature_extractor=UpperCAmelCase_					)

			_lowercase							:      Union[str, Any]			=					floats_list((3, 10_00)					)

			_lowercase							:      Tuple			=					feature_extractor(UpperCAmelCase_							,return_tensors="""np"""					)
			_lowercase							:      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					):
			_lowercase							:      int			=					self.get_feature_extractor()
			_lowercase							:      Tuple			=					self.get_tokenizer()

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

			_lowercase							:      Tuple			=					"""This is a test string"""

			_lowercase							:      List[str]			=					processor(text=UpperCAmelCase_					)

			_lowercase							:      int			=					tokenizer(UpperCAmelCase_					)

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






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					self.get_feature_extractor()
			_lowercase							:      Optional[Any]			=					self.get_tokenizer()

			_lowercase							:      Optional[int]			=					MusicgenProcessor(tokenizer=UpperCAmelCase_							,feature_extractor=UpperCAmelCase_					)

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

			_lowercase							:      Any			=					processor.batch_decode(sequences=UpperCAmelCase_					)
			_lowercase							:      List[str]			=					tokenizer.batch_decode(UpperCAmelCase_					)

			self.assertListEqual(UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					self.get_feature_extractor()
			_lowercase							:      Optional[int]			=					self.get_tokenizer()

			_lowercase							:      List[Any]			=					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					):
			_lowercase							:      List[Any]			=					self.get_feature_extractor(padding_side="""left"""					)
			_lowercase							:      Tuple			=					self.get_tokenizer()

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

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

			_lowercase							:      Union[str, Any]			=					np.asarray(floats_list((3, 20)					)					)[:, None, :]
			_lowercase							:      Optional[int]			=					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)					)










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""						)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""						)

									super().__init__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							, __A							, )
									return self.image_processor_class


							@property
							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor