





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   :							List[Any]     =	random.Random()







def 				__UpperCamelCase  (		snake_case    ,	snake_case=1.0    ,	snake_case=None    ,	snake_case=None     )  ->						str:


   '''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 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       '''facebook/musicgen-small'''
     __A				 =       tempfile.mkdtemp()

  def 		SCREAMING_SNAKE_CASE__	( self  ,    **UpperCAmelCase     )-> Dict:
     return TaTokenizer.from_pretrained(self.checkpoint  ,    **UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    **UpperCAmelCase     )-> str:
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint  ,    **UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     shutil.rmtree(self.tmpdirname     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       self.get_tokenizer()
     __A				 =       self.get_feature_extractor()

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

     processor.save_pretrained(self.tmpdirname     )
     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     __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=UpperCAmelCase  ,    padding_value=1.0     )

     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

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

     __A				 =       floats_list((3, 10_00)     )

     __A				 =       feature_extractor(UpperCAmelCase  ,    return_tensors='''np'''     )
     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

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

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

     __A				 =       processor(text=UpperCAmelCase     )

     __A				 =       tokenizer(UpperCAmelCase     )

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

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

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

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

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

     __A				 =       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 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       self.get_feature_extractor(padding_side='''left'''     )
     __A				 =       self.get_tokenizer()

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

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

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








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_UpperCamelCase   :							str     =	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class 						_lowerCAmelCase(						unittest.TestCase		,   _a):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A				 =       load_tool('''summarization'''     )
     self.tool.setup()
     __A				 =       load_tool('''summarization'''  ,    remote=UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     __A				 =       self.tool(UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.remote_tool(UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       self.tool(text=UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __A				 =       self.remote_tool(text=UpperCAmelCase     )
     self.assertEqual(
         UpperCAmelCase  ,    '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''  ,    )

