

import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil



@pytest.fixture
def   UpperCamelCase(   lowercase_				)      ->							int:





  '''simple docstring'''





  snake_case_					= tmp_path / """text.txt"""
  snake_case_					= textwrap.dedent(
      """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """				)
  with open(lowercase_     ,   """w"""     ,   encoding="""utf-8"""				) as f:
    f.write(lowercase_				)
  return str(lowercase_				)



@pytest.fixture
def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							List[str]:





  '''simple docstring'''





  snake_case_					= tmp_path / """text_with_image.txt"""
  with open(lowercase_     ,   """w"""     ,   encoding="""utf-8"""				) as f:
    f.write(lowercase_				)
  return str(lowercase_				)



@pytest.mark.parametrize("""keep_linebreaks"""     ,   [True, False]				)
def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Optional[Any]:





  '''simple docstring'''





  with open(lowercase_     ,   encoding="""utf-8"""				) as f:
    snake_case_					= f.read().splitlines(keepends=lowercase_				)
  snake_case_					= Text(keep_linebreaks=lowercase_     ,   encoding="""utf-8"""				)
  snake_case_					= text._generate_tables([[text_file]]				)
  snake_case_					= pa.concat_tables([table for _, table in generator]				).to_pydict()["""text"""]
  assert generated_content == expected_content



@require_pil
def   UpperCamelCase(   lowercase_				)      ->							Optional[Any]:





  '''simple docstring'''





  with open(lowercase_     ,   encoding="""utf-8"""				) as f:
    snake_case_					= f.read().splitlines()[0]
  snake_case_					= Text(encoding="""utf-8"""     ,   features=Features({"""image""": Image()}				)				)
  snake_case_					= text._generate_tables([[text_file_with_image]]				)
  snake_case_					= pa.concat_tables([table for _, table in generator]				)
  assert pa_table.schema.field("""image"""				).type == Image()()
  snake_case_					= pa_table.to_pydict()["""image"""]
  assert generated_content == [{"path": image_file, "bytes": None}]



@pytest.mark.parametrize("""sample_by"""     ,   ["""line""", """paragraph""", """document"""]				)
def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Any:





  '''simple docstring'''





  with open(lowercase_     ,   encoding="""utf-8"""				) as f:
    snake_case_					= f.read()
  if sample_by == "line":
    snake_case_					= expected_content.splitlines()
  elif sample_by == "paragraph":
    snake_case_					= expected_content.split("""\n\n"""				)
  elif sample_by == "document":
    snake_case_					= [expected_content]
  snake_case_					= Text(sample_by=lowercase_     ,   encoding="""utf-8"""     ,   chunksize=100				)
  snake_case_					= text._generate_tables([[text_file]]				)
  snake_case_					= pa.concat_tables([table for _, table in generator]				).to_pydict()["""text"""]
  assert generated_content == expected_content


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   UpperCamelCase(   lowercase_     ,   lowercase_=1.0     ,   lowercase_=None     ,   lowercase_=None				)      ->							List[str]:





  '''simple docstring'''





  if rng is None:
    snake_case_					= global_rng

  snake_case_					= []
  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       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/musicgen-small"""
        snake_case_					= tempfile.mkdtemp()







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			int:
        return TaTokenizer.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Dict:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        processor.save_pretrained(self.tmpdirname  )
        snake_case_					= 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       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= MusicgenProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

        snake_case_					= self.get_tokenizer(bos_token="""(BOS)"""				,    eos_token="""(EOS)"""  )
        snake_case_					= self.get_feature_extractor(do_normalize=lowerCamelCase				,    padding_value=1.0  )

        snake_case_					= 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       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= 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       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

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

        snake_case_					= processor.batch_decode(sequences=lowerCamelCase  )
        snake_case_					= tokenizer.batch_decode(lowerCamelCase  )

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

        snake_case_					= 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       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.get_feature_extractor(padding_side="""left"""  )
        snake_case_					= self.get_tokenizer()

        snake_case_					= MusicgenProcessor(tokenizer=lowerCamelCase				,    feature_extractor=lowerCamelCase  )

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

        snake_case_					= np.asarray(floats_list((3, 20)  )  )[:, None, :]
        snake_case_					= 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, 10)  )
        self.assertTrue(decoded_audios[1].shape == (1, 15)  )
        self.assertTrue(decoded_audios[2].shape == (1, 20)  )