






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


def     lowerCAmelCase_       (     _lowerCamelCase:       int	,				_lowerCamelCase:       str=1.0	,				_lowerCamelCase:       Dict=None	,				_lowerCamelCase:       Dict=None ):
		if rng is None:
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		global_rng

		__SCREAMING_SNAKE_CASE	:		List[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 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		"""facebook/musicgen-small"""
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		tempfile.mkdtemp()

			def UpperCamelCase__ (							self					:			Optional[int]							,					**lowerCAmelCase__					:			Union[str, Any]						):





					"""simple docstring"""






					return TaTokenizer.from_pretrained(self.checkpoint							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					**lowerCAmelCase__					:			Optional[int]						):





					"""simple docstring"""






					return EncodecFeatureExtractor.from_pretrained(self.checkpoint							,					**lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					shutil.rmtree(self.tmpdirname						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.get_tokenizer()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.get_feature_extractor()

					__SCREAMING_SNAKE_CASE	:		int   	=		MusicgenProcessor(tokenizer=lowerCAmelCase__							,					feature_extractor=lowerCAmelCase__						)

					processor.save_pretrained(self.tmpdirname						)
					__SCREAMING_SNAKE_CASE	:		str   	=		MusicgenProcessor.from_pretrained(self.tmpdirname						)

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

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

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		MusicgenProcessor(tokenizer=self.get_tokenizer()							,					feature_extractor=self.get_feature_extractor()						)
					processor.save_pretrained(self.tmpdirname						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_tokenizer(bos_token="""(BOS)"""							,					eos_token="""(EOS)"""						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_feature_extractor(do_normalize=lowerCAmelCase__							,					padding_value=1.0						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		MusicgenProcessor.from_pretrained(
					    self.tmpdirname							,					bos_token="""(BOS)"""							,					eos_token="""(EOS)"""							,					do_normalize=lowerCAmelCase__							,					padding_value=1.0						)

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

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

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.get_feature_extractor()
					__SCREAMING_SNAKE_CASE	:		str   	=		self.get_tokenizer()

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

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		floats_list((3, 1_0_0_0)						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		feature_extractor(lowerCAmelCase__							,					return_tensors="""np"""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		processor(lowerCAmelCase__							,					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					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.get_feature_extractor()
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		MusicgenProcessor(tokenizer=lowerCAmelCase__							,					feature_extractor=lowerCAmelCase__						)

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

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		processor(text=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		str   	=		tokenizer(lowerCAmelCase__						)

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

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_feature_extractor()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE	:		Dict   	=		MusicgenProcessor(tokenizer=lowerCAmelCase__							,					feature_extractor=lowerCAmelCase__						)

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

					__SCREAMING_SNAKE_CASE	:		Any   	=		processor.batch_decode(sequences=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		tokenizer.batch_decode(lowerCAmelCase__						)

					self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.get_feature_extractor()
					__SCREAMING_SNAKE_CASE	:		str   	=		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE	:		Dict   	=		MusicgenProcessor(tokenizer=lowerCAmelCase__							,					feature_extractor=lowerCAmelCase__						)

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



			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.get_feature_extractor(padding_side="""left"""						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.get_tokenizer()

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		MusicgenProcessor(tokenizer=lowerCAmelCase__							,					feature_extractor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		str   	=		[floats_list((1, x)						)[0] for x in range(5							,					2_0							,					5						)]
					__SCREAMING_SNAKE_CASE	:		str   	=		processor(lowerCAmelCase__						).padding_mask

					__SCREAMING_SNAKE_CASE	:		str   	=		np.asarray(floats_list((3, 2_0)						)						)[:, None, :]
					__SCREAMING_SNAKE_CASE	:		Dict   	=		processor.batch_decode(lowerCAmelCase__							,					padding_mask=lowerCAmelCase__						)

					self.assertIsInstance(lowerCAmelCase__							,					lowerCAmelCase__						)

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

					self.assertTrue(decoded_audios[0].shape == (1, 1_0)						)
					self.assertTrue(decoded_audios[1].shape == (1, 1_5)						)
					self.assertTrue(decoded_audios[2].shape == (1, 2_0)						)




'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)