





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			:			int	  = random.Random()
def   _A (	SCREAMING_SNAKE_CASE  :     Optional[int] , SCREAMING_SNAKE_CASE  :     Dict=1.0 , SCREAMING_SNAKE_CASE  :     Any=None , SCREAMING_SNAKE_CASE  :     Optional[Any]=None		):





  """simple docstring"""



  if rng is None:
    a__			:      Union[str, Any]            =global_rng

  a__			:      Dict            =[]
  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):







      def      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        a__			:      Any            ="facebook/musicgen-small"
        a__			:      Any            =tempfile.mkdtemp()







      def      _lowercase						(					self	,   **lowerCAmelCase__							)     ->		Optional[int]:



        '''simple docstring'''




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







      def      _lowercase						(					self	,   **lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




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







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        a__			:      List[Any]            =self.get_tokenizer()
        a__			:      str            =self.get_feature_extractor()

        a__			:      Optional[Any]            =MusicgenProcessor(tokenizer=lowerCAmelCase__	,   feature_extractor=lowerCAmelCase__							)

        processor.save_pretrained(self.tmpdirname							)
        a__			:      List[str]            =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      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        a__			:      List[Any]            =MusicgenProcessor(tokenizer=self.get_tokenizer()	,   feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__			:      Optional[Any]            =self.get_tokenizer(bos_token="(BOS)"	,   eos_token="(EOS)"							)
        a__			:      Union[str, Any]            =self.get_feature_extractor(do_normalize=lowerCAmelCase__	,   padding_value=1.0							)

        a__			:      str            =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      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        a__			:      Tuple            =self.get_feature_extractor()
        a__			:      Optional[int]            =self.get_tokenizer()

        a__			:      str            =MusicgenProcessor(tokenizer=lowerCAmelCase__	,   feature_extractor=lowerCAmelCase__							)

        a__			:      int            =floats_list((3, 1_0_0_0)							)

        a__			:      List[Any]            =feature_extractor(lowerCAmelCase__	,   return_tensors="np"							)
        a__			:      Union[str, Any]            =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      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        a__			:      Any            =self.get_feature_extractor()
        a__			:      Dict            =self.get_tokenizer()

        a__			:      Optional[int]            =MusicgenProcessor(tokenizer=lowerCAmelCase__	,   feature_extractor=lowerCAmelCase__							)

        a__			:      Any            ="This is a test string"

        a__			:      Optional[int]            =processor(text=lowerCAmelCase__							)

        a__			:      int            =tokenizer(lowerCAmelCase__							)

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







      def      _lowercase						(					self							)     ->		int:



        '''simple docstring'''




        a__			:      Optional[Any]            =self.get_feature_extractor()
        a__			:      Any            =self.get_tokenizer()

        a__			:      Tuple            =MusicgenProcessor(tokenizer=lowerCAmelCase__	,   feature_extractor=lowerCAmelCase__							)

        a__			:      List[Any]            =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__			:      Dict            =processor.batch_decode(sequences=lowerCAmelCase__							)
        a__			:      Any            =tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__	,   lowerCAmelCase__							)







      def      _lowercase						(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




        a__			:      Any            =self.get_feature_extractor()
        a__			:      List[str]            =self.get_tokenizer()

        a__			:      List[str]            =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      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        a__			:      Dict            =self.get_feature_extractor(padding_side="left"							)
        a__			:      Any            =self.get_tokenizer()

        a__			:      List[str]            =MusicgenProcessor(tokenizer=lowerCAmelCase__	,   feature_extractor=lowerCAmelCase__							)

        a__			:      List[str]            =[floats_list((1, x)							)[0] for x in range(5	,   2_0	,   5							)]
        a__			:      List[Any]            =processor(lowerCAmelCase__							).padding_mask

        a__			:      Optional[int]            =np.asarray(floats_list((3, 2_0)							)							)[:, None, :]
        a__			:      Union[str, Any]            =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)							)













import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=False							)     ->		List[Any]:



        '''simple docstring'''




        a__      ,							a__      ,							a__			:      Optional[int]            =(
            filename_skip_take["filename"],
            filename_skip_take["skip"] if "skip" in filename_skip_take else None,
            filename_skip_take["take"] if "take" in filename_skip_take else None,
        )
        open(os.path.join(lowerCAmelCase__							)	,   "wb"							).close()
        a__			:      Optional[int]            =pa.Table.from_pydict({"filename": [Path(lowerCAmelCase__							).name] * 1_0_0}							)
        if take == -1:
          a__			:      int            =len(lowerCAmelCase__							) - skip
        if skip is not None and take is not None:
          a__			:      Optional[Any]            =pa_table.slice(lowerCAmelCase__	,   lowerCAmelCase__							)
        return pa_table







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        a__			:      List[str]            ="my_name"
        a__			:      List[Any]            =SplitInfo(name="train"	,   num_examples=1_0_0							)
        a__			:      List[str]            =SplitInfo(name="test"	,   num_examples=1_0_0							)
        a__			:      List[Any]            =[train_info, test_info]
        a__			:      Union[str, Any]            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        split_dict.add(lowerCAmelCase__							)
        a__			:      Dict            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Optional[Any]            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Any            ="test[:33%]"
          a__			:      int            =Dataset(**reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)							)
          self.assertEqual(dset["filename"][0]	,   F'''{name}-test'''							)
          self.assertEqual(dset.num_rows	,   3_3							)
          self.assertEqual(dset.num_columns	,   1							)

          a__			:      Tuple            =["train", "test[:33%]"]
          a__			:      str            =[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
          for instructions in [instructionsa, instructionsa]:
            a__			:      Optional[int]            =[reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							) for instr in instructions]
            a__      ,							a__			:      Optional[int]            =(Dataset(**lowerCAmelCase__							) for dataset_kwargs in datasets_kwargs)
            self.assertEqual(train_dset["filename"][0]	,   F'''{name}-train'''							)
            self.assertEqual(train_dset.num_rows	,   1_0_0							)
            self.assertEqual(train_dset.num_columns	,   1							)
            self.assertIsInstance(train_dset.split	,   lowerCAmelCase__							)
            self.assertEqual(str(train_dset.split							)	,   "train"							)
            self.assertEqual(test_dset["filename"][0]	,   F'''{name}-test'''							)
            self.assertEqual(test_dset.num_rows	,   3_3							)
            self.assertEqual(test_dset.num_columns	,   1							)
            self.assertIsInstance(test_dset.split	,   lowerCAmelCase__							)
            self.assertEqual(str(test_dset.split							)	,   "test[:33%]"							)
            del train_dset, test_dset







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



        '''simple docstring'''




        a__			:      Any            ="my_name"
        a__			:      List[Any]            =SplitInfo(name="train"	,   num_examples=1_0_0_0	,   shard_lengths=[1_0_0] * 1_0							)
        a__			:      Optional[Any]            =[train_info]
        a__			:      Any            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        a__			:      Any            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Optional[int]            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Union[str, Any]            ="train[:33%]"
          a__			:      Union[str, Any]            =Dataset(**reader.read(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)							)
          self.assertEqual(dset["filename"][0]	,   F'''{name}-train-00000-of-00010'''							)
          self.assertEqual(dset["filename"][-1]	,   F'''{name}-train-00003-of-00010'''							)
          self.assertEqual(dset.num_rows	,   3_3_0							)
          self.assertEqual(dset.num_columns	,   1							)







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        a__			:      Dict            =SplitInfo(name="train"	,   num_examples=1_0_0							)
        a__			:      str            =SplitInfo(name="test"	,   num_examples=1_0_0							)
        a__			:      int            =SplitDict()
        split_dict.add(lowerCAmelCase__							)
        split_dict.add(lowerCAmelCase__							)
        a__			:      int            =DatasetInfo(splits=lowerCAmelCase__							)

        with tempfile.TemporaryDirectory() as tmp_dir:
          a__			:      Tuple            =ReaderTest(lowerCAmelCase__	,   lowerCAmelCase__							)

          a__			:      Tuple            =[
              {"filename": os.path.join(lowerCAmelCase__	,   "train"							)},
              {"filename": os.path.join(lowerCAmelCase__	,   "test"							), "skip": 1_0, "take": 1_0},
          ]
          a__			:      Optional[Any]            =Dataset(**reader.read_files(lowerCAmelCase__	,   original_instructions="train+test[10:20]"							)							)
          self.assertEqual(dset.num_rows	,   1_1_0							)
          self.assertEqual(dset.num_columns	,   1							)
          del dset
@pytest.mark.parametrize("in_memory" , [False, True]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any]		):





  """simple docstring"""



  a__			:      int            =arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__			:      str            =ArrowReader.read_table(SCREAMING_SNAKE_CASE , in_memory=SCREAMING_SNAKE_CASE		)
  assert table.shape == dataset.data.shape
  assert set(table.column_names		) == set(dataset.data.column_names		)
  assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict
@pytest.mark.parametrize("in_memory" , [False, True]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     List[Any] , SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Optional[int]            =arrow_file
  a__			:      Tuple            =ArrowReader("" , SCREAMING_SNAKE_CASE		)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__			:      Union[str, Any]            =reader.read_files([{"filename": filename}] , in_memory=SCREAMING_SNAKE_CASE		)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  a__			:      Dict            =dataset_kwargs["arrow_table"]
  assert table.shape == dataset.data.shape
  assert set(table.column_names		) == set(dataset.data.column_names		)
  assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict
def   _A (	):





  """simple docstring"""



  assert ReadInstruction("train" , to=10 , unit="abs"		).to_spec() == "train[:10]"
  assert ReadInstruction("train" , from_=-80 , to=10 , unit="%"		).to_spec() == "train[-80%:10%]"

  a__			:      List[Any]            ="train+test"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_test

  a__			:      Union[str, Any]            ="train[2:10]"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_abs

  a__			:      List[Any]            ="train[15%:-20%]"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_pct

  a__			:      Dict            ="train[:10%](closest)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == "train[:10%]"

  a__			:      Tuple            ="train[:10%](pct1_dropremainder)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_pct_rounding

  a__			:      List[Any]            ="train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
  assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE		).to_spec() == spec_train_test_pct_rounding
def   _A (	):





  """simple docstring"""



  a__			:      Optional[Any]            ="dummy"
  a__			:      Dict            =[SplitInfo(name="train" , num_examples=100		)]
  a__			:      Optional[Any]            ="train[:33%]"
  a__			:      int            ="arrow"
  a__			:      Any            ="prefix"

  a__			:      Tuple            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train.arrow'''		), "skip": 0, "take": 33}
  ]

  a__			:      List[str]            =[SplitInfo(name="train" , num_examples=100 , shard_lengths=[10] * 10		)]
  a__			:      Union[str, Any]            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00000-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00001-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00002-of-00010.arrow'''		), "skip": 0, "take": -1},
      {"filename": os.path.join(SCREAMING_SNAKE_CASE , f'''{name}-train-00003-of-00010.arrow'''		), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize("name, expected_exception" , [(None, TypeError), ("", ValueError)]		)
def   _A (	SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     Tuple		):





  """simple docstring"""



  a__			:      Dict            =[SplitInfo(name="train" , num_examples=100		)]
  a__			:      Any            ="train"
  a__			:      Optional[Any]            ="arrow"
  a__			:      List[Any]            ="prefix_path"
  with pytest.raises(SCREAMING_SNAKE_CASE		):
    a__			:      Optional[Any]            =make_file_instructions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)






