

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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


if is_vision_available():
      import PIL


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''pixel_values''']




 def __init__(			self  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = PILImageResampling.BILINEAR  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = 1 / 255  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   super().__init__(**_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     size if size is not None else {'height': 2_560, 'width': 1_920}
   if isinstance(_UpperCAmelCase  ,  (tuple, list)							):
     # The previous feature extractor size parameter was in (width, height) format
     __UpperCAmelCase	:  Union[str, Any]		=     size[::-1]
   __UpperCAmelCase	:  Any		=     get_size_dict(_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     do_resize
   __UpperCAmelCase	:  List[Any]		=     size
   __UpperCAmelCase	:  Any		=     resample
   __UpperCAmelCase	:  List[str]		=     do_thumbnail
   __UpperCAmelCase	:  Tuple		=     do_align_long_axis
   __UpperCAmelCase	:  List[str]		=     do_pad
   __UpperCAmelCase	:  Union[str, Any]		=     do_rescale
   __UpperCAmelCase	:  Union[str, Any]		=     rescale_factor
   __UpperCAmelCase	:  str		=     do_normalize
   __UpperCAmelCase	:  Any		=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __UpperCAmelCase	:  Any		=     image_std if image_std is not None else IMAGENET_STANDARD_STD




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     get_image_size(_UpperCAmelCase							)
   __UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     size['height'], size['width']

   if (output_width < output_height and input_width > input_height) or (
       output_width > output_height and input_width < input_height
   ):
     __UpperCAmelCase	:  Any		=     np.rotaa(_UpperCAmelCase  ,  3							)

   if data_format is not None:
     __UpperCAmelCase	:  Optional[Any]		=     to_channel_dimension_format(_UpperCAmelCase  ,  _UpperCAmelCase							)

   return image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  ):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     size['height'], size['width']
   __UpperCAmelCase,			__UpperCAmelCase	:  str		=     get_image_size(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     output_width - input_width
   __UpperCAmelCase	:  Optional[int]		=     output_height - input_height

   if random_padding:
     __UpperCAmelCase	:  Optional[Any]		=     np.random.randint(low=0  ,  high=delta_height + 1							)
     __UpperCAmelCase	:  Optional[int]		=     np.random.randint(low=0  ,  high=delta_width + 1							)
   else:
     __UpperCAmelCase	:  Dict		=     delta_height // 2
     __UpperCAmelCase	:  List[Any]		=     delta_width // 2

   __UpperCAmelCase	:  Optional[Any]		=     delta_height - pad_top
   __UpperCAmelCase	:  Any		=     delta_width - pad_left

   __UpperCAmelCase	:  int		=     ((pad_top, pad_bottom), (pad_left, pad_right))
   return pad(_UpperCAmelCase  ,  _UpperCAmelCase  ,  data_format=_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'							)
   return self.pad_image(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = PILImageResampling.BICUBIC  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     get_image_size(_UpperCAmelCase							)
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[int]		=     size['height'], size['width']

   # We always resize to the smallest of either the input or output size.
   __UpperCAmelCase	:  List[Any]		=     min(_UpperCAmelCase  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     min(_UpperCAmelCase  ,  _UpperCAmelCase							)

   if height == input_height and width == input_width:
     return image

   if input_height > input_width:
     __UpperCAmelCase	:  Optional[Any]		=     int(input_width * height / input_height							)
   elif input_width > input_height:
     __UpperCAmelCase	:  List[str]		=     int(input_height * width / input_width							)

   return resize(
       _UpperCAmelCase  ,  size=(height, width)  ,  resample=_UpperCAmelCase  ,  reducing_gap=2.0  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = PILImageResampling.BICUBIC  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  List[str]		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  List[str]		=     min(size['height']  ,  size['width']							)
   __UpperCAmelCase	:  List[str]		=     get_resize_output_image_size(_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  default_to_square=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     resize(_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  resample=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)
   return resized_image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return rescale(_UpperCAmelCase  ,  scale=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return normalize(_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = ChannelDimension.FIRST  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  str		=     do_resize if do_resize is not None else self.do_resize
   __UpperCAmelCase	:  str		=     size if size is not None else self.size
   if isinstance(_UpperCAmelCase  ,  (tuple, list)							):
     # Previous feature extractor had size in (width, height) format
     __UpperCAmelCase	:  Union[str, Any]		=     size[::-1]
   __UpperCAmelCase	:  Dict		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     resample if resample is not None else self.resample
   __UpperCAmelCase	:  List[str]		=     do_thumbnail if do_thumbnail is not None else self.do_thumbnail
   __UpperCAmelCase	:  Optional[int]		=     do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
   __UpperCAmelCase	:  Optional[int]		=     do_pad if do_pad is not None else self.do_pad
   __UpperCAmelCase	:  List[Any]		=     do_rescale if do_rescale is not None else self.do_rescale
   __UpperCAmelCase	:  Any		=     rescale_factor if rescale_factor is not None else self.rescale_factor
   __UpperCAmelCase	:  str		=     do_normalize if do_normalize is not None else self.do_normalize
   __UpperCAmelCase	:  Dict		=     image_mean if image_mean is not None else self.image_mean
   __UpperCAmelCase	:  Dict		=     image_std if image_std is not None else self.image_std

   __UpperCAmelCase	:  Optional[int]		=     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.'							)

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

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

   # All transformations expect numpy arrays.
   __UpperCAmelCase	:  List[str]		=     [to_numpy_array(_UpperCAmelCase							) for image in images]

   if do_align_long_axis:
     __UpperCAmelCase	:  Dict		=     [self.align_long_axis(_UpperCAmelCase  ,  size=_UpperCAmelCase							) for image in images]

   if do_resize:
     __UpperCAmelCase	:  List[str]		=     [self.resize(image=_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  resample=_UpperCAmelCase							) for image in images]

   if do_thumbnail:
     __UpperCAmelCase	:  Tuple		=     [self.thumbnail(image=_UpperCAmelCase  ,  size=_UpperCAmelCase							) for image in images]

   if do_pad:
     __UpperCAmelCase	:  str		=     [self.pad_image(image=_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  random_padding=_UpperCAmelCase							) for image in images]

   if do_rescale:
     __UpperCAmelCase	:  Optional[int]		=     [self.rescale(image=_UpperCAmelCase  ,  scale=_UpperCAmelCase							) for image in images]

   if do_normalize:
     __UpperCAmelCase	:  List[str]		=     [self.normalize(image=_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  List[str]		=     [to_channel_dimension_format(_UpperCAmelCase  ,  _UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  List[str]		=     {'pixel_values': images}
   return BatchFeature(data=_UpperCAmelCase  ,  tensor_type=_UpperCAmelCase							)









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			:			Union[str, Any]			      =					random.Random()


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=1.0				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=None				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]=None							):






       """simple docstring"""





       if rng is None:
              UpperCamelCase     :						str 						=		global_rng

       UpperCamelCase     :						Union[str, Any] 						=		[]
       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    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		'''facebook/musicgen-small'''
              UpperCamelCase     :						str 						=		tempfile.mkdtemp()






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




              shutil.rmtree(self.tmpdirname    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()
              UpperCamelCase     :						Any 						=		self.get_feature_extractor()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              processor.save_pretrained(self.tmpdirname    )
              UpperCamelCase     :						int 						=		MusicgenProcessor.from_pretrained(self.tmpdirname    )

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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
              processor.save_pretrained(self.tmpdirname    )

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

              UpperCamelCase     :						Union[str, Any] 						=		MusicgenProcessor.from_pretrained(
                  self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						Tuple 						=		self.get_tokenizer()

              UpperCamelCase     :						Any 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		floats_list((3, 1_000)    )

              UpperCamelCase     :						Tuple 						=		feature_extractor(__SCREAMING_SNAKE_CASE			,       return_tensors='''np'''    )
              UpperCamelCase     :						Optional[Any] 						=		processor(__SCREAMING_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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.get_feature_extractor()
              UpperCamelCase     :						List[str] 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		'''This is a test string'''

              UpperCamelCase     :						Dict 						=		processor(text=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Union[str, Any] 						=		tokenizer(__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		self.get_feature_extractor()
              UpperCamelCase     :						int 						=		self.get_tokenizer()

              UpperCamelCase     :						Optional[int] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

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

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

              self.assertListEqual(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		self.get_feature_extractor()
              UpperCamelCase     :						Union[str, Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						str 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		self.get_feature_extractor(padding_side='''left'''    )
              UpperCamelCase     :						Optional[Any] 						=		self.get_tokenizer()

              UpperCamelCase     :						List[Any] 						=		MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,       feature_extractor=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[str] 						=		[floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
              UpperCamelCase     :						Tuple 						=		processor(__SCREAMING_SNAKE_CASE    ).padding_mask

              UpperCamelCase     :						List[str] 						=		np.asarray(floats_list((3, 20)    )    )[:, None, :]
              UpperCamelCase     :						Optional[int] 						=		processor.batch_decode(__SCREAMING_SNAKE_CASE			,       padding_mask=__SCREAMING_SNAKE_CASE    )

              self.assertIsInstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              for audio in decoded_audios:
                     self.assertIsInstance(__SCREAMING_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)    )

