
'''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


SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       random.Random()



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase=1.0 ,						__UpperCamelCase=None ,						__UpperCamelCase=None     ):
				if rng is None:
								A_       	=  global_rng

				A_       	=  []
				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        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  '''facebook/musicgen-small'''
									A_       	=  tempfile.mkdtemp()


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							Optional[int]:





									'''simple docstring'''
									return TaTokenizer.from_pretrained(self.checkpoint       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       **a__						)		->							List[Any]:





									'''simple docstring'''
									return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,       **a__						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname						)


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  self.get_tokenizer()
									A_       	=  self.get_feature_extractor()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									processor.save_pretrained(self.tmpdirname						)
									A_       	=  MusicgenProcessor.from_pretrained(self.tmpdirname						)

									self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer.get_vocab()						)
									self.assertIsInstance(processor.tokenizer       ,       a__						)

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


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





									'''simple docstring'''
									A_       	=  MusicgenProcessor(tokenizer=self.get_tokenizer()       ,       feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

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

									A_       	=  MusicgenProcessor.from_pretrained(
									    self.tmpdirname       ,       bos_token='''(BOS)'''       ,       eos_token='''(EOS)'''       ,       do_normalize=a__       ,       padding_value=1.0						)

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

									self.assertEqual(processor.feature_extractor.to_json_string()       ,       feature_extractor_add_kwargs.to_json_string()						)
									self.assertIsInstance(processor.feature_extractor       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									A_       	=  floats_list((3, 1_0_0_0)						)

									A_       	=  feature_extractor(a__       ,       return_tensors='''np'''						)
									A_       	=  processor(a__       ,       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						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									A_       	=  '''This is a test string'''

									A_       	=  processor(text=a__						)

									A_       	=  tokenizer(a__						)

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


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

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

									A_       	=  processor.batch_decode(sequences=a__						)
									A_       	=  tokenizer.batch_decode(a__						)

									self.assertListEqual(a__       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor()
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

									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						)		->							List[str]:





									'''simple docstring'''
									A_       	=  self.get_feature_extractor(padding_side='''left'''						)
									A_       	=  self.get_tokenizer()

									A_       	=  MusicgenProcessor(tokenizer=a__       ,       feature_extractor=a__						)

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

									A_       	=  np.asarray(floats_list((3, 2_0)						)						)[:, None, :]
									A_       	=  processor.batch_decode(a__       ,       padding_mask=a__						)

									self.assertIsInstance(a__       ,       a__						)

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


SCREAMING_SNAKE_CASE   :		Optional[int]					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

									A_       	=  do_center_crop
									A_       	=  crop_size
									A_       	=  do_resize
									A_       	=  size
									A_       	=  resample
									A_       	=  do_rescale
									A_       	=  rescale_factor
									A_       	=  do_normalize
									A_       	=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A_       	=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

									A_	,					A_       	=  get_image_size(a__						)
									A_       	=  min(a__       ,       a__						)
									A_       	=  (size['''height'''] / crop_size['''height''']) * min_dim
									A_       	=  (size['''width'''] / crop_size['''width''']) * min_dim
									return center_crop(a__       ,       size=(cropped_height, cropped_width)       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									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(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  make_list_of_images(a__						)

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

									if do_center_crop and crop_size is None:
													raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

									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.'''						)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''						)

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

									if do_center_crop:
													A_       	=  [self.center_crop(a__       ,       a__       ,       size=a__						) for image in images]

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

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

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

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)