


"""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


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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE=1.0					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=None					)     ->					Dict:
							if rng is None:
														snake_case_     							=		global_rng

							snake_case_     							=		[]
							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   __A  (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   str							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		"""facebook/musicgen-small"""
														snake_case_     							=		tempfile.mkdtemp()



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							**UpperCAmelCase_				:   Any							)       ->Any:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Tuple:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname							)



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get_tokenizer()
														snake_case_     							=		self.get_feature_extractor()

														snake_case_     							=		MusicgenProcessor(tokenizer=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_							)

														processor.save_pretrained(self.tmpdirname							)
														snake_case_     							=		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 		lowerCAmelCase							(					self				:   int							)       ->Any:



														"""simple docstring"""



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

														snake_case_     							=		self.get_tokenizer(bos_token="""(BOS)"""		,							eos_token="""(EOS)"""							)
														snake_case_     							=		self.get_feature_extractor(do_normalize=UpperCAmelCase_		,							padding_value=1.0							)

														snake_case_     							=		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 		lowerCAmelCase							(					self				:   Tuple							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get_feature_extractor()
														snake_case_     							=		self.get_tokenizer()

														snake_case_     							=		MusicgenProcessor(tokenizer=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_							)

														snake_case_     							=		floats_list((3, 1_000)							)

														snake_case_     							=		feature_extractor(UpperCAmelCase_		,							return_tensors="""np"""							)
														snake_case_     							=		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 		lowerCAmelCase							(					self				:   List[str]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.get_feature_extractor()
														snake_case_     							=		self.get_tokenizer()

														snake_case_     							=		MusicgenProcessor(tokenizer=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_							)

														snake_case_     							=		"""This is a test string"""

														snake_case_     							=		processor(text=UpperCAmelCase_							)

														snake_case_     							=		tokenizer(UpperCAmelCase_							)

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



							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self.get_feature_extractor()
														snake_case_     							=		self.get_tokenizer()

														snake_case_     							=		MusicgenProcessor(tokenizer=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_							)

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

														snake_case_     							=		processor.batch_decode(sequences=UpperCAmelCase_							)
														snake_case_     							=		tokenizer.batch_decode(UpperCAmelCase_							)

														self.assertListEqual(UpperCAmelCase_		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   int							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		self.get_feature_extractor()
														snake_case_     							=		self.get_tokenizer()

														snake_case_     							=		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 		lowerCAmelCase							(					self				:   Tuple							)       ->str:



														"""simple docstring"""



														snake_case_     							=		self.get_feature_extractor(padding_side="""left"""							)
														snake_case_     							=		self.get_tokenizer()

														snake_case_     							=		MusicgenProcessor(tokenizer=UpperCAmelCase_		,							feature_extractor=UpperCAmelCase_							)

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

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







"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



