
'''simple docstring'''





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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    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    =					logging.get_logger(__name__)






class        UpperCAmelCase_       (						lowerCamelCase__							):

 '''simple docstring'''
 lowercase_				:						Optional[int]				=       ["pixel_values"]







 def __init__(				self		:     str ,							snake_case__		:     bool = True ,							snake_case__		:     Dict[str, int] = None ,							snake_case__		:     PILImageResampling = PIL.Image.NEAREST ,							snake_case__		:     bool = False ,							snake_case__		:     Dict[str, int] = None ,							snake_case__		:     Union[int, float] = 1 / 2_55 ,							snake_case__		:     bool = False ,							snake_case__		:     bool = True ,							snake_case__		:     bool = True ,							snake_case__		:     Optional[Union[float, List[float]]] = None ,							snake_case__		:     Optional[Union[float, List[float]]] = None ,							snake_case__		:     bool = True ,							**snake_case__		:     Dict ,							):



   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_   )
   UpperCAmelCase__			:				Dict												=       size if size is not None else {"height": 3_46, "width": 3_46}
   UpperCAmelCase__			:				List[str]												=       get_size_dict(SCREAMING_SNAKE_CASE_   )
   UpperCAmelCase__			:				Dict												=       crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
   UpperCAmelCase__			:				Optional[int]												=       get_size_dict(SCREAMING_SNAKE_CASE_ ,							param_name="crop_size"   )

   UpperCAmelCase__			:				str												=       do_resize
   UpperCAmelCase__			:				Dict												=       size
   UpperCAmelCase__			:				Any												=       resample
   UpperCAmelCase__			:				int												=       do_center_crop
   UpperCAmelCase__			:				Any												=       crop_size
   UpperCAmelCase__			:				Any												=       do_rescale
   UpperCAmelCase__			:				List[Any]												=       rescale_factor
   UpperCAmelCase__			:				Any												=       rescale_offset
   UpperCAmelCase__			:				Optional[int]												=       do_normalize
   UpperCAmelCase__			:				int												=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   UpperCAmelCase__			:				List[Any]												=       image_std if image_std is not None else IMAGENET_STANDARD_STD
   UpperCAmelCase__			:				Tuple												=       include_top







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     np.ndarray ,							snake_case__		:     Dict[str, int] ,							snake_case__		:     PILImageResampling = PIL.Image.NEAREST ,							snake_case__		:     Optional[Union[str, ChannelDimension]] = None ,							**snake_case__		:     Optional[Any] ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[Any]												=       get_size_dict(SCREAMING_SNAKE_CASE_   )
   if "height" not in size or "width" not in size:
     raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""   )
   return resize(
       SCREAMING_SNAKE_CASE_ ,							size=(size["height"], size["width"]) ,							resample=SCREAMING_SNAKE_CASE_ ,							data_format=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     np.ndarray ,							snake_case__		:     Dict[str, int] ,							snake_case__		:     Optional[Union[str, ChannelDimension]] = None ,							**snake_case__		:     int ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       get_size_dict(SCREAMING_SNAKE_CASE_   )
   if "height" not in size or "width" not in size:
     raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""   )
   return center_crop(SCREAMING_SNAKE_CASE_ ,							size=(size["height"], size["width"]) ,							data_format=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_   )







 def        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     np.ndarray ,							snake_case__		:     Union[int, float] ,							snake_case__		:     bool = True ,							snake_case__		:     Optional[Union[str, ChannelDimension]] = None ,							**snake_case__		:     Optional[Any] ,							):



   '''simple docstring'''




   if offset:
     UpperCAmelCase__			:				Tuple												=       (image - 127.5) * scale
     if data_format is not None:
       UpperCAmelCase__			:				List[Any]												=       to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_   )
     UpperCAmelCase__			:				str												=       rescaled_image.astype(np.floataa   )
   else:
     UpperCAmelCase__			:				Union[str, Any]												=       rescale(SCREAMING_SNAKE_CASE_ ,							scale=SCREAMING_SNAKE_CASE_ ,							data_format=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_   )
   return rescaled_image







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     np.ndarray ,							snake_case__		:     Union[float, List[float]] ,							snake_case__		:     Union[float, List[float]] ,							snake_case__		:     Optional[Union[str, ChannelDimension]] = None ,							**snake_case__		:     Any ,							):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ImageInput ,							snake_case__		:     bool = None ,							snake_case__		:     Dict[str, int] = None ,							snake_case__		:     List[str]=None ,							snake_case__		:     bool = None ,							snake_case__		:     Dict[str, int] = None ,							snake_case__		:     bool = None ,							snake_case__		:     float = None ,							snake_case__		:     bool = None ,							snake_case__		:     bool = None ,							snake_case__		:     Optional[Union[float, List[float]]] = None ,							snake_case__		:     Optional[Union[float, List[float]]] = None ,							snake_case__		:     bool = None ,							snake_case__		:     Optional[Union[str, TensorType]] = None ,							snake_case__		:     ChannelDimension = ChannelDimension.FIRST ,							**snake_case__		:     str ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase__			:				Dict												=       resample if resample is not None else self.resample
   UpperCAmelCase__			:				Tuple												=       do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase__			:				Optional[int]												=       do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase__			:				List[str]												=       rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase__			:				List[Any]												=       rescale_offset if rescale_offset is not None else self.rescale_offset
   UpperCAmelCase__			:				Any												=       do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase__			:				Tuple												=       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]												=       include_top if include_top is not None else self.include_top

   UpperCAmelCase__			:				List[Any]												=       size if size is not None else self.size
   UpperCAmelCase__			:				List[Any]												=       get_size_dict(SCREAMING_SNAKE_CASE_   )
   UpperCAmelCase__			:				Dict												=       crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase__			:				Tuple												=       get_size_dict(SCREAMING_SNAKE_CASE_ ,							param_name="crop_size"   )

   UpperCAmelCase__			:				Any												=       make_list_of_images(SCREAMING_SNAKE_CASE_   )

   if not valid_images(SCREAMING_SNAKE_CASE_   ):
     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 or resample is None:
     raise ValueError("Size and resample must be specified if do_resize is True."   )

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."   )

   # All transformations expect numpy arrays.
   UpperCAmelCase__			:				str												=       [to_numpy_array(SCREAMING_SNAKE_CASE_   ) for image in images]

   if do_resize:
     UpperCAmelCase__			:				List[Any]												=       [self.resize(image=SCREAMING_SNAKE_CASE_ ,							size=SCREAMING_SNAKE_CASE_ ,							resample=SCREAMING_SNAKE_CASE_   ) for image in images]

   if do_center_crop:
     UpperCAmelCase__			:				Optional[Any]												=       [self.center_crop(image=SCREAMING_SNAKE_CASE_ ,							size=SCREAMING_SNAKE_CASE_   ) for image in images]

   if do_rescale:
     UpperCAmelCase__			:				Optional[int]												=       [self.rescale(image=SCREAMING_SNAKE_CASE_ ,							scale=SCREAMING_SNAKE_CASE_ ,							offset=SCREAMING_SNAKE_CASE_   ) for image in images]

   if do_normalize:
     UpperCAmelCase__			:				List[Any]												=       [self.normalize(image=SCREAMING_SNAKE_CASE_ ,							mean=SCREAMING_SNAKE_CASE_ ,							std=SCREAMING_SNAKE_CASE_   ) for image in images]

   if include_top:
     UpperCAmelCase__			:				Union[str, Any]												=       [self.normalize(image=SCREAMING_SNAKE_CASE_ ,							mean=[0, 0, 0] ,							std=SCREAMING_SNAKE_CASE_   ) for image in images]

   UpperCAmelCase__			:				Any												=       [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_   ) for image in images]

   UpperCAmelCase__			:				Dict												=       {"pixel_values": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,							tensor_type=SCREAMING_SNAKE_CASE_   )





'''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    =					random.Random()







def snake_case_    (				lowercase__      ,					lowercase__=1.0      ,					lowercase__=None      ,					lowercase__=None		):
  if rng is None:
    UpperCAmelCase__			:				Optional[int]												=       global_rng

  UpperCAmelCase__			:				List[str]												=       []
  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        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       "facebook/musicgen-small"
   UpperCAmelCase__			:				Union[str, Any]												=       tempfile.mkdtemp()







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     Dict   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     str   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   shutil.rmtree(self.tmpdirname   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_tokenizer()
   UpperCAmelCase__			:				Tuple												=       self.get_feature_extractor()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   processor.save_pretrained(self.tmpdirname   )
   UpperCAmelCase__			:				Dict												=       MusicgenProcessor.from_pretrained(self.tmpdirname   )

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

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







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"   )
   UpperCAmelCase__			:				int												=       self.get_feature_extractor(do_normalize=snake_case__ ,							padding_value=1.0   )

   UpperCAmelCase__			:				Optional[Any]												=       MusicgenProcessor.from_pretrained(
       self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=snake_case__ ,							padding_value=1.0   )

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

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







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor()
   UpperCAmelCase__			:				int												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[str]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       floats_list((3, 10_00)   )

   UpperCAmelCase__			:				List[Any]												=       feature_extractor(snake_case__ ,							return_tensors="np"   )
   UpperCAmelCase__			:				List[Any]												=       processor(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        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




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

   UpperCAmelCase__			:				Optional[int]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       "This is a test string"

   UpperCAmelCase__			:				Dict												=       processor(text=snake_case__   )

   UpperCAmelCase__			:				Any												=       tokenizer(snake_case__   )

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







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




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

   UpperCAmelCase__			:				List[Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

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

   UpperCAmelCase__			:				Any												=       processor.batch_decode(sequences=snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.batch_decode(snake_case__   )

   self.assertListEqual(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




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

   UpperCAmelCase__			:				Union[str, Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=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        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor(padding_side="left"   )
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       [floats_list((1, x)   )[0] for x in range(5 ,							20 ,							5   )]
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__   ).padding_mask

   UpperCAmelCase__			:				Dict												=       np.asarray(floats_list((3, 20)   )   )[:, None, :]
   UpperCAmelCase__			:				Optional[Any]												=       processor.batch_decode(snake_case__ ,							padding_mask=snake_case__   )

   self.assertIsInstance(snake_case__ ,							snake_case__   )

   for audio in decoded_audios:
     self.assertIsInstance(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)   )



