






"""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


lowercase_								=	random.Random()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase=1.0						,			__UpperCamelCase=None						,			__UpperCamelCase=None					):




  """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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					'''facebook/musicgen-small'''
     __A		     =					tempfile.mkdtemp()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     return TaTokenizer.from_pretrained(self.checkpoint,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			**_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     shutil.rmtree(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.get_tokenizer()
     __A		     =					self.get_feature_extractor()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     processor.save_pretrained(self.tmpdirname    )
     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''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=_lowerCamelCase,			padding_value=1.0    )

     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					floats_list((3, 10_00)    )

     __A		     =					feature_extractor(_lowerCamelCase,			return_tensors='''np'''    )
     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					'''This is a test string'''

     __A		     =					processor(text=_lowerCamelCase    )

     __A		     =					tokenizer(_lowerCamelCase    )

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

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

     __A		     =					processor.batch_decode(sequences=_lowerCamelCase    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase    )

     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor(padding_side='''left'''    )
     __A		     =					self.get_tokenizer()

     __A		     =					MusicgenProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

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

     __A		     =					np.asarray(floats_list((3, 20)    )    )[:, None, :]
     __A		     =					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, 10)    )
     self.assertTrue(decoded_audios[1].shape == (1, 15)    )
     self.assertTrue(decoded_audios[2].shape == (1, 20)    )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Dict    =     """opt"""
 A__    :					int    =     ["""past_key_values"""]

 def __init__(   self  ,						__UpperCamelCase=5_0_2_7_2  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=True  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        UpperCamelCase_											=							ffn_dim
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							activation_function
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							use_cache
        UpperCamelCase_											=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        UpperCamelCase_											=							enable_bias
        UpperCamelCase_											=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        UpperCamelCase_											=							_remove_final_layer_norm



