
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      :					Dict   	=       random.Random()

def a__   (	A_,					A_=1.0,					A_=None,					A_=None ):






  '''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 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__    					=	"""facebook/musicgen-small"""
    __magic_name__    					=	tempfile.mkdtemp()
  def 							_lowercase							(			self      :			Tuple      ,							**UpperCamelCase__      :			str   )      ->     Any:







    """simple docstring"""






    return TaTokenizer.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[int]      ,							**UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     List[Any]:







    """simple docstring"""






    shutil.rmtree(self.tmpdirname   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






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

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

    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      ,							UpperCamelCase__   )

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     List[str]:







    """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=UpperCamelCase__      ,							padding_value=1.0   )

    __magic_name__    					=	MusicgenProcessor.from_pretrained(
        self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=UpperCamelCase__      ,							padding_value=1.0   )

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

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor_add_kwargs.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






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

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

    __magic_name__    					=	floats_list((3, 1000)   )

    __magic_name__    					=	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""   )
    __magic_name__    					=	processor(UpperCamelCase__      ,							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      :			Optional[Any]   )      ->     str:







    """simple docstring"""






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

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

    __magic_name__    					=	"""This is a test string"""

    __magic_name__    					=	processor(text=UpperCamelCase__   )

    __magic_name__    					=	tokenizer(UpperCamelCase__   )

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]   )
  def 							_lowercase							(			self      :			Dict   )      ->     int:







    """simple docstring"""






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

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

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

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

    self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple   )      ->     int:







    """simple docstring"""






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

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

    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      :			List[str]   )      ->     int:







    """simple docstring"""






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

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

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

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

    self.assertIsInstance(UpperCamelCase__      ,							UpperCamelCase__   )

    for audio in decoded_audios:
      self.assertIsInstance(UpperCamelCase__      ,							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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase      :					str   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""pixel_values"""]
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Dict[str, int]] = None      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[int, float] = 1 / 255      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )
    __magic_name__    					=	size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    __magic_name__    					=	do_resize
    __magic_name__    					=	do_rescale
    __magic_name__    					=	do_normalize
    __magic_name__    					=	size
    __magic_name__    					=	resample
    __magic_name__    					=	rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__    					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Dict[str, int]      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Optional[int]      ,							)      ->     np.ndarray:







    """simple docstring"""






    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
    return resize(
        UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			float      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     np.ndarray:







    """simple docstring"""






    return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     np.ndarray:







    """simple docstring"""






    return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )





  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			ImageInput      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Dict[str, int] = None      ,							UpperCamelCase__      :			PILImageResampling = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[float] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__      :			Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**UpperCamelCase__      :			List[str]      ,							)      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	do_resize if do_resize is not None else self.do_resize
    __magic_name__    					=	do_rescale if do_rescale is not None else self.do_rescale
    __magic_name__    					=	do_normalize if do_normalize is not None else self.do_normalize
    __magic_name__    					=	resample if resample is not None else self.resample
    __magic_name__    					=	rescale_factor if rescale_factor is not None else self.rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else self.image_mean
    __magic_name__    					=	image_std if image_std is not None else self.image_std

    __magic_name__    					=	size if size is not None else self.size
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )

    __magic_name__    					=	make_list_of_images(UpperCamelCase__   )

    if not valid_images(UpperCamelCase__   ):
      raise ValueError(
          """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
          """torch.Tensor, tf.Tensor or jax.ndarray."""   )

    if do_resize and size is None:
      raise ValueError("""Size must be specified if do_resize is True."""   )

    if do_rescale and rescale_factor is None:
      raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

    # All transformations expect numpy arrays.
    __magic_name__    					=	[to_numpy_array(UpperCamelCase__   ) for image in images]

    if do_resize:
      __magic_name__    					=	[self.resize(image=UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__   ) for image in images]

    if do_rescale:
      __magic_name__    					=	[self.rescale(image=UpperCamelCase__      ,							scale=UpperCamelCase__   ) for image in images]

    if do_normalize:
      __magic_name__    					=	[self.normalize(image=UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__   ) for image in images]

    __magic_name__    					=	[to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__   ) for image in images]

    __magic_name__    					=	{"""pixel_values""": images}
    return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__   )

