






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 __A   (				__lowerCAmelCase			,		__lowerCAmelCase=1.0			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None     )->  List[str]:





						"""simple docstring"""



						if rng is None:
												_UpperCAmelCase     =      global_rng

						_UpperCAmelCase     =      []
						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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'facebook/musicgen-small'
							_UpperCAmelCase     =      tempfile.mkdtemp()

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_tokenizer()
							_UpperCAmelCase     =      self.get_feature_extractor()

							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=UpperCAmelCase	,   feature_extractor=UpperCAmelCase   )

							processor.save_pretrained(self.tmpdirname   )
							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=self.get_tokenizer()	,   feature_extractor=self.get_feature_extractor()   )
							processor.save_pretrained(self.tmpdirname   )

							_UpperCAmelCase     =      self.get_tokenizer(bos_token='(BOS)'	,   eos_token='(EOS)'   )
							_UpperCAmelCase     =      self.get_feature_extractor(do_normalize=UpperCAmelCase	,   padding_value=1.0   )

							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_feature_extractor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=UpperCAmelCase	,   feature_extractor=UpperCAmelCase   )

							_UpperCAmelCase     =      floats_list((3, 1000)   )

							_UpperCAmelCase     =      feature_extractor(UpperCAmelCase	,   return_tensors='np'   )
							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_feature_extractor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=UpperCAmelCase	,   feature_extractor=UpperCAmelCase   )

							_UpperCAmelCase     =      'This is a test string'

							_UpperCAmelCase     =      processor(text=UpperCAmelCase   )

							_UpperCAmelCase     =      tokenizer(UpperCAmelCase   )

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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_feature_extractor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=UpperCAmelCase	,   feature_extractor=UpperCAmelCase   )

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

							_UpperCAmelCase     =      processor.batch_decode(sequences=UpperCAmelCase   )
							_UpperCAmelCase     =      tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_feature_extractor()
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.get_feature_extractor(padding_side='left'   )
							_UpperCAmelCase     =      self.get_tokenizer()

							_UpperCAmelCase     =      MusicgenProcessor(tokenizer=UpperCAmelCase	,   feature_extractor=UpperCAmelCase   )

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

							_UpperCAmelCase     =      np.asarray(floats_list((3, 20)   )   )[:, None, :]
							_UpperCAmelCase     =      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)   )








from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						if not inplace:
												_UpperCAmelCase     =      ma + ma
						else:
												ma += ma

						return ma


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      -1 * len(__lowerCAmelCase     )
						_UpperCAmelCase     =      list(range(len(tensor.shape[:zero_index]     )     )     )
						return tensor.permute(first_inds + [zero_index + i for i in inds]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						return t.reshape(t.shape[:-no_dims] + (-1,)     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 1E-4     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      mask.expand(*value.shape     )
						return torch.sum(mask * value			,		dim=__lowerCAmelCase     ) / (eps + torch.sum(__lowerCAmelCase			,		dim=__lowerCAmelCase     ))


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase = 2.31_25			,		__lowerCAmelCase = 21.68_75			,		__lowerCAmelCase = 64     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.linspace(__lowerCAmelCase			,		__lowerCAmelCase			,		no_bins - 1			,		device=pts.device     )
						_UpperCAmelCase     =      torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2			,		dim=-1     )     )
						return torch.bucketize(__lowerCAmelCase			,		__lowerCAmelCase     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						_UpperCAmelCase     =      dicts[0]
						_UpperCAmelCase     =      {}
						for k, v in first.items():
												_UpperCAmelCase     =      [d[k] for d in dicts]
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_multimap(__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      fn(__lowerCAmelCase     )

						return new_dict


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      v_bins.view(((1,) * len(x.shape     )) + (len(__lowerCAmelCase     ),)     )
						_UpperCAmelCase     =      x[..., None] - reshaped_bins
						_UpperCAmelCase     =      torch.argmin(torch.abs(__lowerCAmelCase     )			,		dim=-1     )
						return nn.functional.one_hot(__lowerCAmelCase			,		num_classes=len(__lowerCAmelCase     )     ).float()


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 0			,		__lowerCAmelCase = 0     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      []
						for i, s in enumerate(data.shape[:no_batch_dims]     ):
												_UpperCAmelCase     =      torch.arange(__lowerCAmelCase     )
												_UpperCAmelCase     =      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
												ranges.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      [slice(__lowerCAmelCase     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
						_UpperCAmelCase     =      inds
						ranges.extend(__lowerCAmelCase     )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(__lowerCAmelCase     )]


_a				     =  TypeVar('''T''')


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict[Any, Union[dict, list, tuple, Any]]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}
						for k, v in dic.items():
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						return new_dict


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  list:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  tuple:





						"""simple docstring"""



						...


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return [tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree]
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return tuple(tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return fn(__lowerCAmelCase     )
						else:
												print(type(__lowerCAmelCase     )     )
												raise ValueError('Not supported'     )

_a				     =  partial(tree_map, leaf_type=torch.Tensor)
