




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


_lowerCamelCase			 =						random.Random()




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[Any]       ,     __UpperCamelCase			:						List[Any]=1.0       ,     __UpperCamelCase			:						Any=None       ,     __UpperCamelCase			:						List[str]=None      )  ->							Any:
    if rng is None:
        UpperCAmelCase_							 =     global_rng

    UpperCAmelCase_							 =     []
    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   :			int				):
         UpperCAmelCase_							 =     '''facebook/musicgen-small'''
         UpperCAmelCase_							 =     tempfile.mkdtemp()
     def   lowerCamelCase_   (	self   :			Optional[int]					,    **__snake_case   :			Tuple				):
         return TaTokenizer.from_pretrained(self.checkpoint					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			List[str]					,    **__snake_case   :			List[Any]				):
         return EncodecFeatureExtractor.from_pretrained(self.checkpoint					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple				):
         shutil.rmtree(self.tmpdirname				)
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     self.get_tokenizer()
         UpperCAmelCase_							 =     self.get_feature_extractor()

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

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

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

         self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor.to_json_string()				)
         self.assertIsInstance(processor.feature_extractor					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			Any				):
         UpperCAmelCase_							 =     MusicgenProcessor(tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()				)
         processor.save_pretrained(self.tmpdirname				)

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

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

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

         self.assertEqual(processor.feature_extractor.to_json_string()					,    feature_extractor_add_kwargs.to_json_string()				)
         self.assertIsInstance(processor.feature_extractor					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

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

         UpperCAmelCase_							 =     floats_list((3, 10_00)				)

         UpperCAmelCase_							 =     feature_extractor(__snake_case					,    return_tensors='''np'''				)
         UpperCAmelCase_							 =     processor(__snake_case					,    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[Any]				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

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

         UpperCAmelCase_							 =     '''This is a test string'''

         UpperCAmelCase_							 =     processor(text=__snake_case				)

         UpperCAmelCase_							 =     tokenizer(__snake_case				)

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,    encoded_processor[key]				)
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

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

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

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

         self.assertListEqual(__snake_case					,    __snake_case				)
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         UpperCAmelCase_							 =     self.get_feature_extractor()
         UpperCAmelCase_							 =     self.get_tokenizer()

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

         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   :			Optional[int]				):
         UpperCAmelCase_							 =     self.get_feature_extractor(padding_side='''left'''				)
         UpperCAmelCase_							 =     self.get_tokenizer()

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

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

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

         self.assertIsInstance(__snake_case					,    __snake_case				)

         for audio in decoded_audios:
             self.assertIsInstance(__snake_case					,    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				= {"""configuration_bit""": ["""BIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BitConfig""", """BitOnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """BIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BitForImageClassification""",
      """BitModel""",
      """BitPreTrainedModel""",
      """BitBackbone""",
  ]


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""BitImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bit import (
        BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BitBackbone,
        BitForImageClassification,
        BitModel,
        BitPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bit import BitImageProcessor

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






