


import unittest

from transformers.image_processing_utils import get_size_dict

class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								# Test a dict with the wrong keys raises an error
								a_     			=   {"""wrong_key""": 224}
								with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									get_size_dict(_SCREAMING_SNAKE_CASE    )

								a_     			=   {"""height""": 224}
								with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									get_size_dict(_SCREAMING_SNAKE_CASE    )

								a_     			=   {"""width""": 224, """shortest_edge""": 224}
								with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									get_size_dict(_SCREAMING_SNAKE_CASE    )

								# Test a dict with the correct keys is returned as is
								a_     			=   {"""height""": 224, """width""": 224}
								a_     			=   get_size_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								a_     			=   {"""shortest_edge""": 224}
								a_     			=   get_size_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""shortest_edge""": 224}    )

								a_     			=   {"""longest_edge""": 224, """shortest_edge""": 224}
								a_     			=   get_size_dict(_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""longest_edge""": 224, """shortest_edge""": 224}    )

								# Test a single int value which  represents (size, size)
								a_     			=   get_size_dict(224    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""height""": 224, """width""": 224}    )

								# Test a single int value which represents the shortest edge
								a_     			=   get_size_dict(224			,       default_to_square=_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""shortest_edge""": 224}    )

								# Test a tuple of ints which represents (height, width)
								a_     			=   get_size_dict((150, 200)    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""height""": 150, """width""": 200}    )

								# Test a tuple of ints which represents (width, height)
								a_     			=   get_size_dict((150, 200)			,       height_width_order=_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""height""": 200, """width""": 150}    )

								# Test an int representing the shortest edge and max_size which represents the longest edge
								a_     			=   get_size_dict(224			,       max_size=256			,       default_to_square=_SCREAMING_SNAKE_CASE    )
								self.assertEqual(_SCREAMING_SNAKE_CASE			,       {"""shortest_edge""": 224, """longest_edge""": 256}    )

								# Test int with default_to_square=True and max_size fails
								with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
									get_size_dict(224			,       max_size=256			,       default_to_square=_SCREAMING_SNAKE_CASE    )



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


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""simple docstring"""






	if rng is None:
		a_     			=   global_rng

	a_     			=   []
	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 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   MusicgenProcessor.from_pretrained(self.tmpdirname    )

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

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





							def   __magic_name__      (							self    ):
								a_     			=   MusicgenProcessor(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=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
								a_     			=   processor(_SCREAMING_SNAKE_CASE    ).padding_mask

								a_     			=   np.asarray(floats_list((3, 20)    )    )[:, None, :]
								a_     			=   processor.batch_decode(_SCREAMING_SNAKE_CASE			,       padding_mask=_SCREAMING_SNAKE_CASE    )

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       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)    )