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      		=					random.Random()


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[Any]=1.0		,    lowercase_:						Optional[Any]=None		,    lowercase_:						Union[str, Any]=None					):





       if rng is None:
              _UpperCamelCase  :							str	    =	global_rng

       _UpperCamelCase  :							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  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Tuple	)			->				int:
        _UpperCamelCase  :							int	    =	'''facebook/musicgen-small'''
        _UpperCamelCase  :							str	    =	tempfile.mkdtemp()

 def        __snake_case       (      self							:		Dict							,      **lowerCamelCase							:		Tuple	)			->				List[str]:
        return TaTokenizer.from_pretrained(self.checkpoint							,      **__SCREAMING_SNAKE_CASE	)

 def        __snake_case       (      self							:		Optional[int]							,      **lowerCamelCase							:		Any	)			->				Optional[Any]:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint							,      **__SCREAMING_SNAKE_CASE	)

 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        shutil.rmtree(self.tmpdirname	)

 def        __snake_case       (      self							:		Optional[int]	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	self.get_tokenizer()
        _UpperCamelCase  :							Any	    =	self.get_feature_extractor()

        _UpperCamelCase  :							Optional[int]	    =	MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,      feature_extractor=__SCREAMING_SNAKE_CASE	)

        processor.save_pretrained(self.tmpdirname	)
        _UpperCamelCase  :							int	    =	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        __snake_case       (      self							:		List[str]	)			->				Dict:
        _UpperCamelCase  :							str	    =	MusicgenProcessor(tokenizer=self.get_tokenizer()							,      feature_extractor=self.get_feature_extractor()	)
        processor.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Any	    =	self.get_tokenizer(bos_token='(BOS)'							,      eos_token='(EOS)'	)
        _UpperCamelCase  :							Union[str, Any]	    =	self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE							,      padding_value=1.0	)

        _UpperCamelCase  :							Union[str, Any]	    =	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        __snake_case       (      self							:		List[str]	)			->				str:
        _UpperCamelCase  :							int	    =	self.get_feature_extractor()
        _UpperCamelCase  :							Tuple	    =	self.get_tokenizer()

        _UpperCamelCase  :							Any	    =	MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,      feature_extractor=__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase  :							Union[str, Any]	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Tuple	    =	feature_extractor(__SCREAMING_SNAKE_CASE							,      return_tensors='np'	)
        _UpperCamelCase  :							Optional[Any]	    =	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        __snake_case       (      self							:		Optional[int]	)			->				Optional[Any]:
        _UpperCamelCase  :							int	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[str]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Optional[int]	    =	MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,      feature_extractor=__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase  :							Tuple	    =	'''This is a test string'''

        _UpperCamelCase  :							Dict	    =	processor(text=__SCREAMING_SNAKE_CASE	)

        _UpperCamelCase  :							Union[str, Any]	    =	tokenizer(__SCREAMING_SNAKE_CASE	)

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

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							Any	    =	self.get_feature_extractor()
        _UpperCamelCase  :							int	    =	self.get_tokenizer()

        _UpperCamelCase  :							Optional[int]	    =	MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,      feature_extractor=__SCREAMING_SNAKE_CASE	)

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

        _UpperCamelCase  :							int	    =	processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE	)
        _UpperCamelCase  :							Union[str, Any]	    =	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

        self.assertListEqual(__SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE	)

 def        __snake_case       (      self							:		Dict	)			->				List[Any]:
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()
        _UpperCamelCase  :							Union[str, Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							str	    =	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        __snake_case       (      self							:		Tuple	)			->				int:
        _UpperCamelCase  :							str	    =	self.get_feature_extractor(padding_side='left'	)
        _UpperCamelCase  :							Optional[Any]	    =	self.get_tokenizer()

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

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

        _UpperCamelCase  :							List[str]	    =	np.asarray(floats_list((3, 2_0)	)	)[:, None, :]
        _UpperCamelCase  :							Optional[int]	    =	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, 1_0)	)
        self.assertTrue(decoded_audios[1].shape == (1, 1_5)	)
        self.assertTrue(decoded_audios[2].shape == (1, 2_0)	)







import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

