
"""simple docstring"""

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


_A				=					random.Random()
def 						UpperCAmelCase   (						a_,		a_=1.0,		a_=None,		a_=None     ):





		'''simple docstring'''


		if rng is None:
				lowerCamelCase	:     Optional[Any]            =       global_rng

		lowerCamelCase	:     int            =       []
		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 			_lowercase  (							unittest.TestCase						):




	def      _UpperCamelCase       (					self   )						->				Tuple:
			lowerCamelCase	:     Union[str, Any]            =       'facebook/musicgen-small'
			lowerCamelCase	:     Tuple            =       tempfile.mkdtemp()




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Optional[Any]:
			return TaTokenizer.from_pretrained(self.checkpoint						,					**_snake_case   )




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Optional[int]:
			return EncodecFeatureExtractor.from_pretrained(self.checkpoint						,					**_snake_case   )




	def      _UpperCamelCase       (					self   )						->				Any:
			shutil.rmtree(self.tmpdirname   )




	def      _UpperCamelCase       (					self   )						->				int:
			lowerCamelCase	:     Optional[int]            =       self.get_tokenizer()
			lowerCamelCase	:     List[str]            =       self.get_feature_extractor()

			lowerCamelCase	:     Tuple            =       MusicgenProcessor(tokenizer=_snake_case						,					feature_extractor=_snake_case   )

			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						,					_snake_case   )

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




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     Tuple            =       MusicgenProcessor(tokenizer=self.get_tokenizer()						,					feature_extractor=self.get_feature_extractor()   )
			processor.save_pretrained(self.tmpdirname   )

			lowerCamelCase	:     Dict            =       self.get_tokenizer(bos_token='(BOS)'						,					eos_token='(EOS)'   )
			lowerCamelCase	:     Union[str, Any]            =       self.get_feature_extractor(do_normalize=_snake_case						,					padding_value=1.0   )

			lowerCamelCase	:     List[str]            =       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      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     Tuple            =       self.get_feature_extractor()
			lowerCamelCase	:     List[Any]            =       self.get_tokenizer()

			lowerCamelCase	:     Tuple            =       MusicgenProcessor(tokenizer=_snake_case						,					feature_extractor=_snake_case   )

			lowerCamelCase	:     Optional[int]            =       floats_list((3, 1000)   )

			lowerCamelCase	:     int            =       feature_extractor(_snake_case						,					return_tensors='np'   )
			lowerCamelCase	:     List[Any]            =       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      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     Any            =       self.get_feature_extractor()
			lowerCamelCase	:     Optional[Any]            =       self.get_tokenizer()

			lowerCamelCase	:     Dict            =       MusicgenProcessor(tokenizer=_snake_case						,					feature_extractor=_snake_case   )

			lowerCamelCase	:     Optional[Any]            =       'This is a test string'

			lowerCamelCase	:     Any            =       processor(text=_snake_case   )

			lowerCamelCase	:     List[Any]            =       tokenizer(_snake_case   )

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




	def      _UpperCamelCase       (					self   )						->				int:
			lowerCamelCase	:     List[Any]            =       self.get_feature_extractor()
			lowerCamelCase	:     Dict            =       self.get_tokenizer()

			lowerCamelCase	:     Optional[Any]            =       MusicgenProcessor(tokenizer=_snake_case						,					feature_extractor=_snake_case   )

			lowerCamelCase	:     Optional[int]            =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCamelCase	:     int            =       processor.batch_decode(sequences=_snake_case   )
			lowerCamelCase	:     Union[str, Any]            =       tokenizer.batch_decode(_snake_case   )

			self.assertListEqual(_snake_case						,					_snake_case   )




	def      _UpperCamelCase       (					self   )						->				Tuple:
			lowerCamelCase	:     Dict            =       self.get_feature_extractor()
			lowerCamelCase	:     Dict            =       self.get_tokenizer()

			lowerCamelCase	:     int            =       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      _UpperCamelCase       (					self   )						->				int:
			lowerCamelCase	:     Optional[int]            =       self.get_feature_extractor(padding_side='left'   )
			lowerCamelCase	:     List[str]            =       self.get_tokenizer()

			lowerCamelCase	:     Optional[Any]            =       MusicgenProcessor(tokenizer=_snake_case						,					feature_extractor=_snake_case   )

			lowerCamelCase	:     Union[str, Any]            =       [floats_list((1, x)   )[0] for x in range(5						,					20						,					5   )]
			lowerCamelCase	:     Union[str, Any]            =       processor(_snake_case   ).padding_mask

			lowerCamelCase	:     Any            =       np.asarray(floats_list((3, 20)   )   )[:, None, :]
			lowerCamelCase	:     Union[str, Any]            =       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 collections import namedtuple

UpperCamelCase											=      namedtuple('''from_to''', '''from_ to''')

UpperCamelCase											=      {
    '''millimeter''': '''mm''',
    '''centimeter''': '''cm''',
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''inch''': '''in''',
    '''inche''': '''in''',  # Trailing 's' has been stripped off
    '''feet''': '''ft''',
    '''foot''': '''ft''',
    '''yard''': '''yd''',
    '''mile''': '''mi''',
}

UpperCamelCase											=      {
    '''mm''': from_to(0.001, 1_0_0_0),
    '''cm''': from_to(0.01, 1_0_0),
    '''m''': from_to(1, 1),
    '''km''': from_to(1_0_0_0, 0.001),
    '''in''': from_to(0.0254, 39.3701),
    '''ft''': from_to(0.3048, 3.2_8084),
    '''yd''': from_to(0.9144, 1.0_9361),
    '''mi''': from_to(1609.34, 0.0_0062_1371),
}




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      float:
 lowerCamelCase_							:    List[Any]							=     from_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[Any]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 lowerCamelCase_							:    List[Any]							=     to_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[int]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 if new_from not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[Any]							=     (
      F'Invalid \'from_type\' value: {from_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 if new_to not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[str]							=     (
      F'Invalid \'to_type\' value: {to_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
       import doctest

       doctest.testmod()
