


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			:					Optional[int]         =							random.Random()
def 				UpperCamelCase_	(					__snake_case      :    int						,      __snake_case      :    Tuple=1.0						,      __snake_case      :    Optional[int]=None						,      __snake_case      :    List[str]=None  )->   int:




						'''simple docstring'''






						if rng is None:
												__magic_name__					=      global_rng

						__magic_name__					=      []
						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):
					def 	a			(						self       :						int  )							->					str:



											'''simple docstring'''





											__magic_name__					=      'facebook/musicgen-small'
											__magic_name__					=      tempfile.mkdtemp()
					def 	a			(						self       :						Tuple	,      **_lowerCAmelCase       :						List[Any]  )							->					List[str]:



											'''simple docstring'''





											return TaTokenizer.from_pretrained(self.checkpoint	,      **_lowerCAmelCase  )
					def 	a			(						self       :						List[Any]	,      **_lowerCAmelCase       :						int  )							->					str:



											'''simple docstring'''





											return EncodecFeatureExtractor.from_pretrained(self.checkpoint	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Tuple  )							->					List[Any]:



											'''simple docstring'''





											shutil.rmtree(self.tmpdirname  )
					def 	a			(						self       :						List[str]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.get_tokenizer()
											__magic_name__					=      self.get_feature_extractor()

											__magic_name__					=      MusicgenProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											processor.save_pretrained(self.tmpdirname  )
											__magic_name__					=      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 	a			(						self       :						Optional[int]  )							->					Optional[Any]:



											'''simple docstring'''





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

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

											__magic_name__					=      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 	a			(						self       :						int  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      MusicgenProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      floats_list((3, 1_0_0_0)  )

											__magic_name__					=      feature_extractor(_lowerCAmelCase	,      return_tensors='np'  )
											__magic_name__					=      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 	a			(						self       :						str  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      MusicgenProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      'This is a test string'

											__magic_name__					=      processor(text=_lowerCAmelCase  )

											__magic_name__					=      tokenizer(_lowerCAmelCase  )

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]  )
					def 	a			(						self       :						List[str]  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      MusicgenProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

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

											__magic_name__					=      processor.batch_decode(sequences=_lowerCAmelCase  )
											__magic_name__					=      tokenizer.batch_decode(_lowerCAmelCase  )

											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )
					def 	a			(						self       :						Tuple  )							->					str:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      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 	a			(						self       :						List[Any]  )							->					Dict:



											'''simple docstring'''





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

											__magic_name__					=      MusicgenProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      [floats_list((1, x)  )[0] for x in range(5	,      2_0	,      5  )]
											__magic_name__					=      processor(_lowerCAmelCase  ).padding_mask

											__magic_name__					=      np.asarray(floats_list((3, 2_0)  )  )[:, None, :]
											__magic_name__					=      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, 1_0)  )
											self.assertTrue(decoded_audios[1].shape == (1, 1_5)  )
											self.assertTrue(decoded_audios[2].shape == (1, 2_0)  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      ["""BartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BartForCausalLM""",
          """BartForConditionalGeneration""",
          """BartForQuestionAnswering""",
          """BartForSequenceClassification""",
          """BartModel""",
          """BartPretrainedModel""",
          """PretrainedBartModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """TFBartForConditionalGeneration""",
          """TFBartForSequenceClassification""",
          """TFBartModel""",
          """TFBartPretrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """FlaxBartDecoderPreTrainedModel""",
          """FlaxBartForCausalLM""",
          """FlaxBartForConditionalGeneration""",
          """FlaxBartForQuestionAnswering""",
          """FlaxBartForSequenceClassification""",
          """FlaxBartModel""",
          """FlaxBartPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      __lowercase				    =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
