



"""simple docstring"""




import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_lowerCAmelCase							:   Optional[int]				=				collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCAmelCase							:   List[str]				=				"https://storage.googleapis.com/cvdf-datasets/mnist/"

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       numpy.dtype(numpy.uintaa							).newbyteorder(">"							)
     return numpy.frombuffer(bytestream.read(4							)		,    dtype=SCREAMING_SNAKE_CASE__							)[0]

@deprecated(SCREAMING_SNAKE_CASE__		,    "Please use tf.data to implement this functionality."							)
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  int:





     '''simple docstring'''

     print("Extracting"		,    f.name							)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__							) as bytestream:
          _UpperCAmelCase     :      Optional[int]														=       _readaa(SCREAMING_SNAKE_CASE__							)
          if magic != 2_051:
               raise ValueError(
                   "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)							)
          _UpperCAmelCase     :      Union[str, Any]														=       _readaa(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Optional[Any]														=       _readaa(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      List[Any]														=       _readaa(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      List[Any]														=       bytestream.read(rows * cols * num_images							)
          _UpperCAmelCase     :      Union[str, Any]														=       numpy.frombuffer(SCREAMING_SNAKE_CASE__		,    dtype=numpy.uinta							)
          _UpperCAmelCase     :      str														=       data.reshape(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    1							)
          return data

@deprecated(SCREAMING_SNAKE_CASE__		,    "Please use tf.one_hot on tensors."							)
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       labels_dense.shape[0]
     _UpperCAmelCase     :      Dict														=       numpy.arange(SCREAMING_SNAKE_CASE__							) * num_classes
     _UpperCAmelCase     :      List[Any]														=       numpy.zeros((num_labels, num_classes)							)
     _UpperCAmelCase     :      Dict														=       1
     return labels_one_hot

@deprecated(SCREAMING_SNAKE_CASE__		,    "Please use tf.data to implement this functionality."							)
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Optional[int]=False		,    SCREAMING_SNAKE_CASE__      :		Optional[int]=10							)    ->  List[Any]:





     '''simple docstring'''

     print("Extracting"		,    f.name							)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__							) as bytestream:
          _UpperCAmelCase     :      Union[str, Any]														=       _readaa(SCREAMING_SNAKE_CASE__							)
          if magic != 2_049:
               raise ValueError(
                   "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)							)
          _UpperCAmelCase     :      Any														=       _readaa(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Optional[int]														=       bytestream.read(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      str														=       numpy.frombuffer(SCREAMING_SNAKE_CASE__		,    dtype=numpy.uinta							)
          if one_hot:
               return _dense_to_one_hot(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          return labels






class       UpperCAmelCase_    :



      @deprecated(
          A	,      "Please use alternatives such as official/mnist/_DataSet.py"
          " from tensorflow/models."	,      )
      def __init__(	self	:      List[Any]	,      A	:      int	,      A	:      int	,      A	:      str=False	,      A	:      Optional[int]=False	,      A	:      Optional[Any]=dtypes.floataa	,      A	:      List[str]=True	,      A	:      int=None	,      ):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       random_seed.get_seed(A		)
           # If op level seed is not set, use whatever graph level seed is returned
           numpy.random.seed(seeda if seed is None else seeda		)
           _UpperCAmelCase     :      Optional[int]														=       dtypes.as_dtype(A		).base_dtype
           if dtype not in (dtypes.uinta, dtypes.floataa):
                raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype		)
           if fake_data:
                _UpperCAmelCase     :      Tuple														=       1_0_0_0_0
                _UpperCAmelCase     :      int														=       one_hot
           else:
                assert (
                    images.shape[0] == labels.shape[0]
                ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
                _UpperCAmelCase     :      int														=       images.shape[0]

                # Convert shape from [num examples, rows, columns, depth]
                # to [num examples, rows*columns] (assuming depth == 1)
                if reshape:
                     assert images.shape[3] == 1
                     _UpperCAmelCase     :      Optional[Any]														=       images.reshape(
                         images.shape[0]	,      images.shape[1] * images.shape[2]		)
                if dtype == dtypes.floataa:
                     # Convert from [0, 255] -> [0.0, 1.0].
                     _UpperCAmelCase     :      Tuple														=       images.astype(numpy.floataa		)
                     _UpperCAmelCase     :      Tuple														=       numpy.multiply(A	,      1.0 / 255.0		)
           _UpperCAmelCase     :      Tuple														=       images
           _UpperCAmelCase     :      List[Any]														=       labels
           _UpperCAmelCase     :      Dict														=       0
           _UpperCAmelCase     :      Tuple														=       0



      @property
      def 		snake_case_   (	self	:      Dict		):
           return self._images



      @property
      def 		snake_case_   (	self	:      int		):
           return self._labels



      @property
      def 		snake_case_   (	self	:      List[Any]		):
           return self._num_examples



      @property
      def 		snake_case_   (	self	:      List[Any]		):
           return self._epochs_completed



      def 		snake_case_   (	self	:      List[Any]	,      A	:      str	,      A	:      int=False	,      A	:      Any=True		):
           if fake_data:
                _UpperCAmelCase     :      Optional[int]														=       [1] * 7_8_4
                _UpperCAmelCase     :      Dict														=       [1] + [0] * 9 if self.one_hot else 0
                return (
                    [fake_image for _ in range(A		)],
                    [fake_label for _ in range(A		)],
                )
           _UpperCAmelCase     :      str														=       self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
                _UpperCAmelCase     :      List[Any]														=       numpy.arange(self._num_examples		)
                numpy.random.shuffle(A		)
                _UpperCAmelCase     :      Tuple														=       self.images[perma]
                _UpperCAmelCase     :      List[str]														=       self.labels[perma]
           # Go to the next epoch
           if start + batch_size > self._num_examples:
                # Finished epoch
                self._epochs_completed += 1
                # Get the rest examples in this epoch
                _UpperCAmelCase     :      Optional[Any]														=       self._num_examples - start
                _UpperCAmelCase     :      Union[str, Any]														=       self._images[start : self._num_examples]
                _UpperCAmelCase     :      List[str]														=       self._labels[start : self._num_examples]
                # Shuffle the data
                if shuffle:
                     _UpperCAmelCase     :      Any														=       numpy.arange(self._num_examples		)
                     numpy.random.shuffle(A		)
                     _UpperCAmelCase     :      Union[str, Any]														=       self.images[perm]
                     _UpperCAmelCase     :      Any														=       self.labels[perm]
                # Start next epoch
                _UpperCAmelCase     :      List[Any]														=       0
                _UpperCAmelCase     :      int														=       batch_size - rest_num_examples
                _UpperCAmelCase     :      List[str]														=       self._index_in_epoch
                _UpperCAmelCase     :      List[str]														=       self._images[start:end]
                _UpperCAmelCase     :      Tuple														=       self._labels[start:end]
                return (
                    numpy.concatenate((images_rest_part, images_new_part)	,      axis=0		),
                    numpy.concatenate((labels_rest_part, labels_new_part)	,      axis=0		),
                )
           else:
                self._index_in_epoch += batch_size
                _UpperCAmelCase     :      Any														=       self._index_in_epoch
                return self._images[start:end], self._labels[start:end]







@deprecated(SCREAMING_SNAKE_CASE__		,    "Please write your own downloading logic."							)
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  int:





     '''simple docstring'''

     if not gfile.Exists(SCREAMING_SNAKE_CASE__							):
          gfile.MakeDirs(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       os.path.join(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     if not gfile.Exists(SCREAMING_SNAKE_CASE__							):
          urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)  # noqa: S310
          with gfile.GFile(SCREAMING_SNAKE_CASE__							) as f:
               _UpperCAmelCase     :      int														=       f.size()
          print("Successfully downloaded"		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    "bytes."							)
     return filepath

@deprecated(
    SCREAMING_SNAKE_CASE__		,    "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"							)
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Dict=False		,    SCREAMING_SNAKE_CASE__      :		List[Any]=False		,    SCREAMING_SNAKE_CASE__      :		Any=dtypes.floataa		,    SCREAMING_SNAKE_CASE__      :		Any=True		,    SCREAMING_SNAKE_CASE__      :		Optional[int]=5_000		,    SCREAMING_SNAKE_CASE__      :		List[str]=None		,    SCREAMING_SNAKE_CASE__      :		Any=DEFAULT_SOURCE_URL		,    )    ->  Tuple:





     '''simple docstring'''

     if fake_data:

          def fake():
               return _DataSet(
                   []		,    []		,    fake_data=SCREAMING_SNAKE_CASE__		,    one_hot=SCREAMING_SNAKE_CASE__		,    dtype=SCREAMING_SNAKE_CASE__		,    seed=SCREAMING_SNAKE_CASE__							)

          _UpperCAmelCase     :      Optional[int]														=       fake()
          _UpperCAmelCase     :      int														=       fake()
          _UpperCAmelCase     :      str														=       fake()
          return _Datasets(train=SCREAMING_SNAKE_CASE__		,    validation=SCREAMING_SNAKE_CASE__		,    test=SCREAMING_SNAKE_CASE__							)

     if not source_url:  # empty string check
          _UpperCAmelCase     :      Union[str, Any]														=       DEFAULT_SOURCE_URL

     _UpperCAmelCase     :      List[str]														=       "train-images-idx3-ubyte.gz"
     _UpperCAmelCase     :      Dict														=       "train-labels-idx1-ubyte.gz"
     _UpperCAmelCase     :      List[Any]														=       "t10k-images-idx3-ubyte.gz"
     _UpperCAmelCase     :      Any														=       "t10k-labels-idx1-ubyte.gz"

     _UpperCAmelCase     :      Any														=       _maybe_download(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    source_url + train_images_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE__		,    "rb"							) as f:
          _UpperCAmelCase     :      str														=       _extract_images(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Union[str, Any]														=       _maybe_download(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    source_url + train_labels_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE__		,    "rb"							) as f:
          _UpperCAmelCase     :      int														=       _extract_labels(SCREAMING_SNAKE_CASE__		,    one_hot=SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Tuple														=       _maybe_download(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    source_url + test_images_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE__		,    "rb"							) as f:
          _UpperCAmelCase     :      int														=       _extract_images(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[Any]														=       _maybe_download(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    source_url + test_labels_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE__		,    "rb"							) as f:
          _UpperCAmelCase     :      List[Any]														=       _extract_labels(SCREAMING_SNAKE_CASE__		,    one_hot=SCREAMING_SNAKE_CASE__							)

     if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      str														=       (
              "Validation size should be between 0 and "
              f'{len(SCREAMING_SNAKE_CASE__							)}. Received: {validation_size}.'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       train_images[:validation_size]
     _UpperCAmelCase     :      Dict														=       train_labels[:validation_size]
     _UpperCAmelCase     :      List[str]														=       train_images[validation_size:]
     _UpperCAmelCase     :      List[str]														=       train_labels[validation_size:]

     _UpperCAmelCase     :      Dict														=       {"dtype": dtype, "reshape": reshape, "seed": seed}

     _UpperCAmelCase     :      List[Any]														=       _DataSet(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       _DataSet(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Dict														=       _DataSet(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)

     return _Datasets(train=SCREAMING_SNAKE_CASE__		,    validation=SCREAMING_SNAKE_CASE__		,    test=SCREAMING_SNAKE_CASE__							)





"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase     :      List[str]														=       ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase     :      Tuple														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Any														=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase     :      Optional[Any]														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)
           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

           _UpperCAmelCase     :      str														=       [Image.fromarray(np.moveaxis(A	,      0	,      -1		)		) for x in image_inputs]

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       CLIPSegProcessor.from_pretrained(self.tmpdirname		)

           self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()		)
           self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()		)
           self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()		)
           self.assertIsInstance(processor_slow.tokenizer	,      A		)
           self.assertIsInstance(processor_fast.tokenizer	,      A		)

           self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()		)
           self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()		)
           self.assertIsInstance(processor_slow.image_processor	,      A		)
           self.assertIsInstance(processor_fast.image_processor	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.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.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()		)
           self.assertIsInstance(processor.image_processor	,      A		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=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 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["input_ids", "attention_mask", "pixel_values"]		)

           # test if it raises when no input is passed
           with pytest.raises(A		):
                processor()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["pixel_values", "conditional_pixel_values"]		)

           # test if it raises when no input is passed
           with pytest.raises(A		):
                processor()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
