'''simple docstring'''




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class        __magic_name__		(       enum.Enum):
			UpperCamelCase__							=			'timm'
			UpperCamelCase__							=			'transformers'




def 				lowerCamelCase      ( UpperCAmelCase__			:   str , UpperCAmelCase__			:   str , UpperCAmelCase__			:   Tuple					)     -> Union[str, Any]:





							if stage_names is None:
														raise ValueError("""Stage_names must be set for transformers backbones"""					)

							if out_features is not None:
														if not isinstance(UpperCAmelCase__ , (list,)					):
																					raise ValueError(F'''out_features must be a list {type(UpperCAmelCase__					)}'''					)
														if any(feat not in stage_names for feat in out_features					):
																					raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''					)

							if out_indices is not None:
														if not isinstance(UpperCAmelCase__ , (list, tuple)					):
																					raise ValueError(F'''out_indices must be a list or tuple, got {type(UpperCAmelCase__					)}'''					)
														if any(idx >= len(UpperCAmelCase__					) for idx in out_indices					):
																					raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""					)

							if out_features is not None and out_indices is not None:
														if len(UpperCAmelCase__					) != len(UpperCAmelCase__					):
																					raise ValueError("""out_features and out_indices should have the same length if both are set"""					)
														if out_features != [stage_names[idx] for idx in out_indices]:
																					raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   int , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Dict , )     -> int:





							if out_indices is None and out_features is None:
														lowercase_   :     List[str]									=			[len(UpperCAmelCase__					) - 1]
														lowercase_   :     List[str]									=			[stage_names[-1]]
							elif out_indices is None and out_features is not None:
														lowercase_   :     List[str]									=			[stage_names.index(UpperCAmelCase__					) for layer in out_features]
							elif out_features is None and out_indices is not None:
														lowercase_   :     Tuple									=			[stage_names[idx] for idx in out_indices]
							return out_features, out_indices




def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Dict , )     -> Tuple[List[str], List[int]]:
							verify_out_features_out_indices(out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=UpperCAmelCase__					)
							lowercase_		,		lowercase_   :     Tuple									=			_align_output_features_output_indices(
							    out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=UpperCAmelCase__					)
							# Verify that the aligned out_features and out_indices are valid
							verify_out_features_out_indices(out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=UpperCAmelCase__					)
							return output_features, output_indices





class        __magic_name__		:
			UpperCamelCase__							=			None







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Any       ):






										if getattr(self							,     """_backbone"""							,     lowercase_       ) is None:
																	raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""       )

										# These will diagree with the defaults for the transformers models e.g. for resnet50
										# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
										# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
										lowercase_   :     Optional[int]									=			[stage["""module"""] for stage in self._backbone.feature_info.info]
										lowercase_   :     Optional[Any]									=			[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
										lowercase_   :     Tuple									=			self._backbone.feature_info.out_indices
										lowercase_   :     Union[str, Any]									=			self._backbone.feature_info.module_name()

										# We verify the out indices and out features are valid
										verify_out_features_out_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=self.stage_names       )
										lowercase_		,		lowercase_   :     Dict									=			out_features, out_indices







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    str       ):
										lowercase_   :     List[Any]									=			getattr(lowercase_							,     """stage_names"""       )
										lowercase_   :     List[Any]									=			getattr(lowercase_							,     """out_features"""							,     lowercase_       )
										lowercase_   :     Optional[int]									=			getattr(lowercase_							,     """out_indices"""							,     lowercase_       )

										lowercase_   :     Optional[int]									=			stage_names
										lowercase_		,		lowercase_   :     List[Any]									=			get_aligned_output_features_output_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=lowercase_       )
										# Number of channels for each stage. This is set in the transformer backbone model init
										lowercase_   :     Optional[Any]									=			None







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    List[Any]       ):
										lowercase_   :     List[Any]									=			config

										lowercase_   :     Tuple									=			getattr(lowercase_							,     """use_timm_backbone"""							,     lowercase_       )
										lowercase_   :     int									=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

										if self.backbone_type == BackboneType.TIMM:
																	self._init_timm_backbone(lowercase_       )
										elif self.backbone_type == BackboneType.TRANSFORMERS:
																	self._init_transformers_backbone(lowercase_       )
										else:
																	raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return self._out_features







			@out_features.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    List[str]       ):
										lowercase_		,		lowercase_   :     Dict									=			get_aligned_output_features_output_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=self.stage_names       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										return self._out_indices







			@out_indices.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Union[Tuple[int], List[int]]       ):
										lowercase_		,		lowercase_   :     Any									=			get_aligned_output_features_output_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=self.stage_names       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names       )}







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										return [self.out_feature_channels[name] for name in self.out_features]







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     *lowercase_							:    Optional[Any]							,     **lowercase_							:    Dict       ):
										lowercase_   :     str									=			dict(inspect.signature(self.forward       ).parameters       )
										lowercase_   :     Any									=			{k: v for k, v in kwargs.items() if k in signature}
										return self(*lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[bool] = None							,     lowercase_							:    Optional[bool] = None							,     lowercase_							:    Optional[bool] = None							,     ):
										raise NotImplementedError("""This method should be implemented by the derived class."""       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										lowercase_   :     Union[str, Any]									=			super().to_dict()
										lowercase_   :     int									=			output.pop("""_out_features"""       )
										lowercase_   :     str									=			output.pop("""_out_indices"""       )
										return output





class        __magic_name__		:







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										return self._out_features







			@out_features.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    List[str]       ):
										lowercase_		,		lowercase_   :     List[str]									=			get_aligned_output_features_output_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=self.stage_names       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										return self._out_indices







			@out_indices.setter
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Union[Tuple[int], List[int]]       ):
										lowercase_		,		lowercase_   :     int									=			get_aligned_output_features_output_indices(
										    out_features=lowercase_							,     out_indices=lowercase_							,     stage_names=self.stage_names       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     str									=			super().to_dict()
										lowercase_   :     Optional[int]									=			output.pop("""_out_features"""       )
										lowercase_   :     List[Any]									=			output.pop("""_out_indices"""       )
										return output







"""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


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

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""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 SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]			)			->   List[str]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[int]:

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

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

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


	def    _UpperCamelCase							(  self :Dict			)			->   Optional[int]:

			'''simple docstring'''






			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=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

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

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


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    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 :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

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


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			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 :Any			)			->   List[str]:

			'''simple docstring'''






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

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

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

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

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




