


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


snake_case_  :     Optional[Any]							  =							random.Random()







def   lowerCamelCase(				a__		,a__=1.0		,a__=None		,a__=None):





    if rng is None:
        _SCREAMING_SNAKE_CASE     					=global_rng

    _SCREAMING_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					):




      def 							__UpperCamelCase			(    self   : Tuple					)							->       str:


          """simple docstring"""





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




      def 							__UpperCamelCase			(    self   : str			,      **_a   : str					)							->       Dict:


          """simple docstring"""





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




      def 							__UpperCamelCase			(    self   : str			,      **_a   : Optional[Any]					)							->       List[str]:


          """simple docstring"""





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




      def 							__UpperCamelCase			(    self   : Dict					)							->       List[Any]:


          """simple docstring"""





          shutil.rmtree(self.tmpdirname					)




      def 							__UpperCamelCase			(    self   : Tuple					)							->       str:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=self.get_tokenizer()
          _SCREAMING_SNAKE_CASE     					=self.get_feature_extractor()

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

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

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

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




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


          """simple docstring"""





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

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

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

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

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




      def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Any:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=self.get_feature_extractor()
          _SCREAMING_SNAKE_CASE     					=self.get_tokenizer()

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

          _SCREAMING_SNAKE_CASE     					=floats_list((3, 1000)					)

          _SCREAMING_SNAKE_CASE     					=feature_extractor(lowercase_			,      return_tensors='''np'''					)
          _SCREAMING_SNAKE_CASE     					=processor(lowercase_			,      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   : Optional[int]					)							->       List[str]:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=self.get_feature_extractor()
          _SCREAMING_SNAKE_CASE     					=self.get_tokenizer()

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

          _SCREAMING_SNAKE_CASE     					='This is a test string'

          _SCREAMING_SNAKE_CASE     					=processor(text=lowercase_					)

          _SCREAMING_SNAKE_CASE     					=tokenizer(lowercase_					)

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




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


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=self.get_feature_extractor()
          _SCREAMING_SNAKE_CASE     					=self.get_tokenizer()

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

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

          _SCREAMING_SNAKE_CASE     					=processor.batch_decode(sequences=lowercase_					)
          _SCREAMING_SNAKE_CASE     					=tokenizer.batch_decode(lowercase_					)

          self.assertListEqual(lowercase_			,      lowercase_					)




      def 							__UpperCamelCase			(    self   : List[Any]					)							->       Optional[Any]:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=self.get_feature_extractor()
          _SCREAMING_SNAKE_CASE     					=self.get_tokenizer()

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

          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   : Dict					)							->       Tuple:


          """simple docstring"""





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

          _SCREAMING_SNAKE_CASE     					=MusicgenProcessor(tokenizer=lowercase_			,      feature_extractor=lowercase_					)

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

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

          self.assertIsInstance(lowercase_			,      lowercase_					)

          for audio in decoded_audios:
              self.assertIsInstance(lowercase_			,      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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


snake_case_  :     Tuple							  =							logging.get_logger(__name__)

snake_case_  :     int							  =							{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  "plbart"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(    self   : Union[str, Any]			,      _a   : List[str]=5_0005			,      _a   : List[str]=1024			,      _a   : int=6			,      _a   : Union[str, Any]=3072			,      _a   : List[str]=12			,      _a   : int=6			,      _a   : Optional[int]=3072			,      _a   : Any=12			,      _a   : Any=0.0			,      _a   : Optional[Any]=0.0			,      _a   : List[Any]=True			,      _a   : Any=True			,      _a   : Any="gelu"			,      _a   : int=768			,      _a   : int=0.1			,      _a   : int=0.1			,      _a   : List[str]=0.0			,      _a   : Optional[int]=0.02			,      _a   : Dict=0.0			,      _a   : List[str]=True			,      _a   : List[Any]=1			,      _a   : Tuple=0			,      _a   : Tuple=2			,      _a   : Optional[int]=2			,      **_a   : Optional[int]			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=d_model
										_SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=encoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=decoder_layers
										_SCREAMING_SNAKE_CASE     					=decoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=dropout
										_SCREAMING_SNAKE_CASE     					=attention_dropout
										_SCREAMING_SNAKE_CASE     					=activation_dropout
										_SCREAMING_SNAKE_CASE     					=activation_function
										_SCREAMING_SNAKE_CASE     					=init_std
										_SCREAMING_SNAKE_CASE     					=encoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=decoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=classifier_dropout
										_SCREAMING_SNAKE_CASE     					=use_cache
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      forced_eos_token_id=_a			,      **_a			,      )

class   A__							(						UpperCamelCase__					):




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
										    ]					)




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.use_past:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)
										else:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)