

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 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[Any]=1.0					,   __A     :						List[str]=None					,   __A     :						Tuple=None     )			->	Any:
   if rng is None:
      __lowerCAmelCase  :			List[Any]					    =						global_rng

   __lowerCAmelCase  :			str					    =						[]
   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        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	str:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						"""facebook/musicgen-small"""
     __lowerCAmelCase  :			List[str]					    =						tempfile.mkdtemp()







  def        SCREAMING_SNAKE_CASE    (			self :    str       ,							**lowerCAmelCase :    Tuple    )							->	List[Any]:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							**lowerCAmelCase :    int    )							->	int:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	List[Any]:






     """simple docstring"""







     shutil.rmtree(self.tmpdirname    )







  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :			str					    =						self.get_tokenizer()
     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_feature_extractor()

     __lowerCAmelCase  :			Optional[Any]					    =						MusicgenProcessor(tokenizer=lowerCAmelCase       ,							feature_extractor=lowerCAmelCase    )

     processor.save_pretrained(self.tmpdirname    )
     __lowerCAmelCase  :			str					    =						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 :    List[Any]    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						MusicgenProcessor(tokenizer=self.get_tokenizer()       ,							feature_extractor=self.get_feature_extractor()    )
     processor.save_pretrained(self.tmpdirname    )

     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""    )
     __lowerCAmelCase  :			int					    =						self.get_feature_extractor(do_normalize=lowerCAmelCase       ,							padding_value=1.0    )

     __lowerCAmelCase  :			Union[str, Any]					    =						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 :    Union[str, Any]    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Optional[Any]					    =						MusicgenProcessor(tokenizer=lowerCAmelCase       ,							feature_extractor=lowerCAmelCase    )

     __lowerCAmelCase  :			Optional[int]					    =						floats_list((3, 10_00)    )

     __lowerCAmelCase  :			int					    =						feature_extractor(lowerCAmelCase       ,							return_tensors="""np"""    )
     __lowerCAmelCase  :			Union[str, Any]					    =						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 :    Optional[int]    )							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			List[Any]					    =						MusicgenProcessor(tokenizer=lowerCAmelCase       ,							feature_extractor=lowerCAmelCase    )

     __lowerCAmelCase  :			Optional[int]					    =						"""This is a test string"""

     __lowerCAmelCase  :			List[Any]					    =						processor(text=lowerCAmelCase    )

     __lowerCAmelCase  :			Union[str, Any]					    =						tokenizer(lowerCAmelCase    )

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







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Tuple					    =						self.get_tokenizer()

     __lowerCAmelCase  :			List[Any]					    =						MusicgenProcessor(tokenizer=lowerCAmelCase       ,							feature_extractor=lowerCAmelCase    )

     __lowerCAmelCase  :			Tuple					    =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __lowerCAmelCase  :			Optional[int]					    =						processor.batch_decode(sequences=lowerCAmelCase    )
     __lowerCAmelCase  :			Any					    =						tokenizer.batch_decode(lowerCAmelCase    )

     self.assertListEqual(lowerCAmelCase       ,							lowerCAmelCase    )







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






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Optional[int]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Tuple					    =						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 :    str    )							->	List[str]:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						self.get_feature_extractor(padding_side="""left"""    )
     __lowerCAmelCase  :			str					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Optional[int]					    =						MusicgenProcessor(tokenizer=lowerCAmelCase       ,							feature_extractor=lowerCAmelCase    )

     __lowerCAmelCase  :			Any					    =						[floats_list((1, x)    )[0] for x in range(5       ,							20       ,							5    )]
     __lowerCAmelCase  :			List[Any]					    =						processor(lowerCAmelCase    ).padding_mask

     __lowerCAmelCase  :			Optional[int]					    =						np.asarray(floats_list((3, 20)    )    )[:, None, :]
     __lowerCAmelCase  :			Optional[Any]					    =						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


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


