




import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)

lowerCamelCase__			:       Tuple         =							{
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

lowerCamelCase__			:       Union[str, Any]         =							{
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

lowerCamelCase__			:       Union[str, Any]         =							{
    'jukebox': 512,
}





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   VOCAB_FILES_NAMES
 lowercase_       =   PRETRAINED_VOCAB_FILES_MAP
 lowercase_       =   PRETRAINED_LYRIC_TOKENS_SIZES
 lowercase_       =   ["input_ids", "attention_mask"]



 def __init__(							self  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Optional[Any]=["v3", "v2", "v2"]    ,    _lowerCAmelCase  :		List[Any]=512    ,    _lowerCAmelCase  :		Optional[Any]=5    ,    _lowerCAmelCase  :		List[Any]="<|endoftext|>"    ,    **_lowerCAmelCase  :		Optional[int]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      AddedToken(_lowerCAmelCase    ,    lstrip=_lowerCAmelCase    ,    rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase    ,    _lowerCAmelCase						) else unk_token
       super().__init__(
           unk_token=_lowerCAmelCase    ,    n_genres=_lowerCAmelCase    ,    version=_lowerCAmelCase    ,    max_n_lyric_tokens=_lowerCAmelCase    ,    **_lowerCAmelCase    ,    )
       SCREAMING_SNAKE_CASE_     						=      version
       SCREAMING_SNAKE_CASE_     						=      max_n_lyric_tokens
       SCREAMING_SNAKE_CASE_     						=      n_genres

       with open(_lowerCAmelCase    ,    encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_     						=      json.load(_lowerCAmelCase						)

       with open(_lowerCAmelCase    ,    encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_     						=      json.load(_lowerCAmelCase						)

       with open(_lowerCAmelCase    ,    encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_     						=      json.load(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
       # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
       if len(self.lyrics_encoder						) == 79:
             SCREAMING_SNAKE_CASE_     						=      oov.replace(R'\-\''    ,    R'\-+\''						)

       SCREAMING_SNAKE_CASE_     						=      regex.compile(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in self.artists_encoder.items()}
       SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in self.genres_encoder.items()}
       SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in self.lyrics_encoder.items()}



 @property
 def 				lowerCAmelCase_  (							self  :		int						):
       return len(self.artists_encoder						) + len(self.genres_encoder						) + len(self.lyrics_encoder						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       return dict(self.artists_encoder    ,    self.genres_encoder    ,    self.lyrics_encoder						)



 def 				lowerCAmelCase_  (							self  :		List[str]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      [self.artists_encoder.get(_lowerCAmelCase    ,    0						) for artist in list_artists]
       for genres in range(len(_lowerCAmelCase						)						):
             SCREAMING_SNAKE_CASE_     						=      [self.genres_encoder.get(_lowerCAmelCase    ,    0						) for genre in list_genres[genres]]
             SCREAMING_SNAKE_CASE_     						=      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]						))

       SCREAMING_SNAKE_CASE_     						=      [[self.lyrics_encoder.get(_lowerCAmelCase    ,    0						) for character in list_lyrics[0]], [], []]
       return artists_id, list_genres, lyric_ids



 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		Dict						):
       return list(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		List[Any]    ,    **_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_for_tokenization(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self._tokenize(_lowerCAmelCase						)
       return artist, genre, lyrics



 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		bool = False						):
       for idx in range(len(self.version						)						):
             if self.version[idx] == "v3":
                   SCREAMING_SNAKE_CASE_     						=      artists[idx].lower()
                   SCREAMING_SNAKE_CASE_     						=      [genres[idx].lower()]
             else:
                   SCREAMING_SNAKE_CASE_     						=      self._normalize(artists[idx]						) + '.v2'
                   SCREAMING_SNAKE_CASE_     						=      [
                       self._normalize(_lowerCAmelCase						) + '.v2' for genre in genres[idx].split('_'						)
                   ]  # split is for the full dictionary with combined genres

       if self.version[0] == "v2":
             SCREAMING_SNAKE_CASE_     						=      regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'						)
             SCREAMING_SNAKE_CASE_     						=      'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
             SCREAMING_SNAKE_CASE_     						=      {vocab[index]: index + 1 for index in range(len(_lowerCAmelCase						)						)}
             SCREAMING_SNAKE_CASE_     						=      0
             SCREAMING_SNAKE_CASE_     						=      len(_lowerCAmelCase						) + 1
             SCREAMING_SNAKE_CASE_     						=      self.vocab
             SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in self.vocab.items()}
             SCREAMING_SNAKE_CASE_     						=      ''
       else:
             SCREAMING_SNAKE_CASE_     						=      regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'						)

       SCREAMING_SNAKE_CASE_     						=      self._run_strip_accents(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      lyrics.replace('\\'    ,    '\n'						)
       SCREAMING_SNAKE_CASE_     						=      self.out_of_vocab.sub(''    ,    _lowerCAmelCase						), [], []
       return artists, genres, lyrics



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_     						=      unicodedata.normalize('NFD'    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      []
       for char in text:
             SCREAMING_SNAKE_CASE_     						=      unicodedata.category(_lowerCAmelCase						)
             if cat == "Mn":
                   continue
             output.append(_lowerCAmelCase						)
       return "".join(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      (
           [chr(_lowerCAmelCase						) for i in range(ord('a'						)    ,    ord('z'						) + 1						)]
           + [chr(_lowerCAmelCase						) for i in range(ord('A'						)    ,    ord('Z'						) + 1						)]
           + [chr(_lowerCAmelCase						) for i in range(ord('0'						)    ,    ord('9'						) + 1						)]
           + ['.']
       )
       SCREAMING_SNAKE_CASE_     						=      frozenset(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      re.compile(R'_+'						)
       SCREAMING_SNAKE_CASE_     						=      ''.join([c if c in accepted else '_' for c in text.lower()]						)
       SCREAMING_SNAKE_CASE_     						=      pattern.sub('_'    ,    _lowerCAmelCase						).strip('_'						)
       return text



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		List[str]						):
       return " ".join(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Optional[Union[str, TensorType]] = None    ,    _lowerCAmelCase  :		bool = False						):
       # Convert to TensorType
       if not isinstance(_lowerCAmelCase    ,    _lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_     						=      TensorType(_lowerCAmelCase						)

       # Get a function reference for the correct framework
       if tensor_type == TensorType.TENSORFLOW:
             if not is_tf_available():
                   raise ImportError(
                       'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'						)
             import tensorflow as tf

             SCREAMING_SNAKE_CASE_     						=      tf.constant
             SCREAMING_SNAKE_CASE_     						=      tf.is_tensor
       elif tensor_type == TensorType.PYTORCH:
             if not is_torch_available():
                   raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'						)
             import torch

             SCREAMING_SNAKE_CASE_     						=      torch.tensor
             SCREAMING_SNAKE_CASE_     						=      torch.is_tensor
       elif tensor_type == TensorType.JAX:
             if not is_flax_available():
                   raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'						)
             import jax.numpy as jnp  # noqa: F811

             SCREAMING_SNAKE_CASE_     						=      jnp.array
             SCREAMING_SNAKE_CASE_     						=      _is_jax
       else:
             SCREAMING_SNAKE_CASE_     						=      np.asarray
             SCREAMING_SNAKE_CASE_     						=      _is_numpy

       # Do the tensor conversion in batch

       try:
             if prepend_batch_axis:
                   SCREAMING_SNAKE_CASE_     						=      [inputs]

             if not is_tensor(_lowerCAmelCase						):
                   SCREAMING_SNAKE_CASE_     						=      as_tensor(_lowerCAmelCase						)
       except:  # noqa E722
             raise ValueError(
                 'Unable to create tensor, you should probably activate truncation and/or padding '
                 'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'						)

       return inputs



 def __call__(							self  :		int    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Optional[Any]=""    ,    _lowerCAmelCase  :		Tuple="pt"						):
       SCREAMING_SNAKE_CASE_     						=      [0, 0, 0]
       SCREAMING_SNAKE_CASE_     						=      [artist] * len(self.version						)
       SCREAMING_SNAKE_CASE_     						=      [genres] * len(self.version						)

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.tokenize(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self._convert_token_to_id(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      [-INFINITY] * len(full_tokens[-1]						)
       SCREAMING_SNAKE_CASE_     						=      [
           self.convert_to_tensors(
               [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,    tensor_type=_lowerCAmelCase						)
           for i in range(len(self.version						)						)
       ]
       return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
             return

       SCREAMING_SNAKE_CASE_     						=      os.path.join(
           _lowerCAmelCase    ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']						)
       with open(_lowerCAmelCase    ,    'w'    ,    encoding='utf-8'						) as f:
             f.write(json.dumps(self.artists_encoder    ,    ensure_ascii=_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_     						=      os.path.join(
           _lowerCAmelCase    ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']						)
       with open(_lowerCAmelCase    ,    'w'    ,    encoding='utf-8'						) as f:
             f.write(json.dumps(self.genres_encoder    ,    ensure_ascii=_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_     						=      os.path.join(
           _lowerCAmelCase    ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']						)
       with open(_lowerCAmelCase    ,    'w'    ,    encoding='utf-8'						) as f:
             f.write(json.dumps(self.lyrics_encoder    ,    ensure_ascii=_lowerCAmelCase						)						)

       return (artists_file, genres_file, lyrics_file)




 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		List[str]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      self.artists_decoder.get(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      [self.genres_decoder.get(_lowerCAmelCase						) for genre in genres_index]
       SCREAMING_SNAKE_CASE_     						=      [self.lyrics_decoder.get(_lowerCAmelCase						) for character in lyric_index]
       return artist, genres, lyrics





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_mean'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_std'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_normalize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )



 def 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    numpify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    np.ndarray						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    torchify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    torch.Tensor						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )