def test_fuyu_processing_multiple_image_sample(self):
        """
        Test to check processor works with multiple image inputs for a single text input
        """
        # fmt: off
        SINGLE_IMAGE_PATCH_INPUTS = torch.Tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,]]).to(torch.int64)
        SINGLE_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122,]]).to(torch.int64)

        SINGLE_RESIZED_IMAGE_PATCH_INPUTS = torch.Tensor([[ 0,  1,  2, -1,  3,  4,  5, -1,  6,  7,  8, -1,  9, 10, 11, -1, 12, 13, 14, -1, 15, 16, 17, -1, 18, 19, 20, -1, 21, 22, 23, -1, 24, 25, 26, -1, 27, 28, 29, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]])
        SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[ 71011,  71011,  71011,  71019,  71011,  71011,  71011,  71019,  71011, 71011,  71011,  71019,  71011,  71011,  71011,  71019,  71011,  71011, 71011,  71019,  71011,  71011,  71011,  71019,  71011,  71011,  71011, 71019,  71011,  71011,  71011,  71019,  71011,  71011,  71011,  71019, 71011,  71011,  71011,  71019,      1, 128340,  71374,  71389, 120412, 71377,  71835,  71374,  73615,  71375,  71399,  71435,  71122]])
        # fmt: on

        # Batch of two images - equally sized
        images = [self.bus_image_pil, self.bus_image_pil]
        processor_outputs = self.processor(text=[self.text_prompt, self.text_prompt], images=images)

        self.assertTrue(
            (
                processor_outputs["image_patches_indices"]
                == torch.cat([SINGLE_IMAGE_PATCH_INPUTS, SINGLE_IMAGE_PATCH_INPUTS], dim=0)
            ).all()
        )
        self.assertTrue(
            (
                processor_outputs["input_ids"]
                == torch.cat([SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, SINGLE_PADDED_UNPACKED_TOKEN_INPUTS], dim=0)
            ).all()
        )

        # Processes single images with different sizes as expected
        images = [self.bus_image_pil]
        processor_outputs = self.processor(text=self.text_prompt, images=images)
        self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_IMAGE_PATCH_INPUTS).all())
        self.assertTrue((processor_outputs["input_ids"] == SINGLE_PADDED_UNPACKED_TOKEN_INPUTS).all())

        images = [self.bus_image_pil.resize((64, 300))]
        processor_outputs = self.processor(text=self.text_prompt, images=images)
        self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_RESIZED_IMAGE_PATCH_INPUTS).all())
        self.assertTrue((processor_outputs["input_ids"] == SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS).all())

        # Batch of two images - different sizes. Left-pads the smaller image inputs
        images = [self.bus_image_pil, self.bus_image_pil.resize((64, 300))]
        processor_outputs = self.processor(text=[self.text_prompt, self.text_prompt], images=images)

        padding_len_patch = SINGLE_IMAGE_PATCH_INPUTS.shape[1] - SINGLE_RESIZED_IMAGE_PATCH_INPUTS.shape[1]
        padded_single_resized_image_patch = torch.cat(
            [torch.ones([1, padding_len_patch]) * -1, SINGLE_RESIZED_IMAGE_PATCH_INPUTS], dim=1
        )
        expected_image_patch_inputs = torch.cat([SINGLE_IMAGE_PATCH_INPUTS, padded_single_resized_image_patch], dim=0)

        padding_len_token = (
            SINGLE_PADDED_UNPACKED_TOKEN_INPUTS.shape[1] - SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS.shape[1]
        )
        padded_single_resized_padded_unpacked_token_inputs = torch.cat(
            [torch.zeros([1, padding_len_token]), SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS], dim=1
        )
        expected_padded_unpacked_token_inputs = torch.cat(
            [SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, padded_single_resized_padded_unpacked_token_inputs], dim=0
        )

        self.assertTrue((processor_outputs["image_patches_indices"] == expected_image_patch_inputs).all())
        self.assertTrue((processor_outputs["input_ids"] == expected_padded_unpacked_token_inputs).all())

        Test to check processor works with multiple image inputs for a single text input
        
dpkd/transformers/tests/models/fuyu/test_processing_fuyu.py