





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	UpperCamelCase_							(  UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       UnCLIPImageVariationPipeline
							lowerCamelCase_							     =       IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
							lowerCamelCase_							     =       IMAGE_VARIATION_BATCH_PARAMS

							lowerCamelCase_							     =       [
							    "generator",
							    "return_dict",
							    "decoder_num_inference_steps",
							    "super_res_num_inference_steps",
							]
							lowerCamelCase_							     =       False


							@property
							def   _snake_case	(				self     :List[str]		) -> Tuple:



									"""simple docstring"""



									return 32


							@property
							def   _snake_case	(				self     :List[Any]		) -> Tuple:



									"""simple docstring"""



									return 32


							@property
							def   _snake_case	(				self     :Optional[int]		) -> Optional[Any]:



									"""simple docstring"""



									return self.time_input_dim


							@property
							def   _snake_case	(				self     :List[str]		) -> Dict:



									"""simple docstring"""



									return self.time_input_dim * 4


							@property
							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									return 100


							@property
							def   _snake_case	(				self     :int		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
									return tokenizer


							@property
							def   _snake_case	(				self     :int		) -> Dict:



									"""simple docstring"""



									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   CLIPTextConfig(
									    bos_token_id=0				,					eos_token_id=2				,					hidden_size=self.text_embedder_hidden_size				,					projection_dim=self.text_embedder_hidden_size				,					intermediate_size=37				,					layer_norm_eps=1E-05				,					num_attention_heads=4				,					num_hidden_layers=5				,					pad_token_id=1				,					vocab_size=1000				,					)
									return CLIPTextModelWithProjection(__A		)


							@property
							def   _snake_case	(				self     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size				,					projection_dim=self.text_embedder_hidden_size				,					num_hidden_layers=5				,					num_attention_heads=4				,					image_size=32				,					intermediate_size=37				,					patch_size=1				,					)
									return CLIPVisionModelWithProjection(__A		)


							@property
							def   _snake_case	(				self     :List[str]		) -> Optional[int]:



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__								=   {
									    """clip_embeddings_dim""": self.text_embedder_hidden_size,
									    """time_embed_dim""": self.time_embed_dim,
									    """cross_attention_dim""": self.cross_attention_dim,
									}

									SCREAMING_SNAKE_CASE__								=   UnCLIPTextProjModel(**__A		)
									return model


							@property
							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__								=   {
									    """sample_size""": 32,
									    # RGB in channels
									    """in_channels""": 3,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 6,
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": """identity""",
									}

									SCREAMING_SNAKE_CASE__								=   UNetaDConditionModel(**__A		)
									return model


							@property
							def   _snake_case	(				self     :List[str]		) -> Dict:



									"""simple docstring"""



									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}


							@property
							def   _snake_case	(				self     :Tuple		) -> Tuple:



									"""simple docstring"""



									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__								=   UNetaDModel(**self.dummy_super_res_kwargs		)
									return model


							@property
							def   _snake_case	(				self     :Optional[int]		) -> Union[str, Any]:



									"""simple docstring"""



									torch.manual_seed(1		)

									SCREAMING_SNAKE_CASE__								=   UNetaDModel(**self.dummy_super_res_kwargs		)
									return model


							def   _snake_case	(				self     :str		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.dummy_decoder
									SCREAMING_SNAKE_CASE__								=   self.dummy_text_proj
									SCREAMING_SNAKE_CASE__								=   self.dummy_text_encoder
									SCREAMING_SNAKE_CASE__								=   self.dummy_tokenizer
									SCREAMING_SNAKE_CASE__								=   self.dummy_super_res_first
									SCREAMING_SNAKE_CASE__								=   self.dummy_super_res_last

									SCREAMING_SNAKE_CASE__								=   UnCLIPScheduler(
									    variance_type="""learned_range"""				,					prediction_type="""epsilon"""				,					num_train_timesteps=1000				,					)

									SCREAMING_SNAKE_CASE__								=   UnCLIPScheduler(
									    variance_type="""fixed_small_log"""				,					prediction_type="""epsilon"""				,					num_train_timesteps=1000				,					)

									SCREAMING_SNAKE_CASE__								=   CLIPImageProcessor(crop_size=32				,					size=32		)

									SCREAMING_SNAKE_CASE__								=   self.dummy_image_encoder

									return {
									    "decoder": decoder,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "text_proj": text_proj,
									    "feature_extractor": feature_extractor,
									    "image_encoder": image_encoder,
									    "super_res_first": super_res_first,
									    "super_res_last": super_res_last,
									    "decoder_scheduler": decoder_scheduler,
									    "super_res_scheduler": super_res_scheduler,
									}


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Optional[int]				,					__A     :List[str]=0				,					__A     :str=True		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   floats_tensor((1, 3, 32, 32)				,					rng=random.Random(__A		)		).to(__A		)
									if str(__A		).startswith("""mps"""		):
											SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   torch.Generator(device=__A		).manual_seed(__A		)

									if pil_image:
											SCREAMING_SNAKE_CASE__								=   input_image * 0.5 + 0.5
											SCREAMING_SNAKE_CASE__								=   input_image.clamp(0				,					1		)
											SCREAMING_SNAKE_CASE__								=   input_image.cpu().permute(0				,					2				,					3				,					1		).float().numpy()
											SCREAMING_SNAKE_CASE__								=   DiffusionPipeline.numpy_to_pil(__A		)[0]

									return {
									    "image": input_image,
									    "generator": generator,
									    "decoder_num_inference_steps": 2,
									    "super_res_num_inference_steps": 2,
									    "output_type": "np",
									}


							def   _snake_case	(				self     :str		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """cpu"""

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_components()

									SCREAMING_SNAKE_CASE__								=   self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__								=   pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)

									SCREAMING_SNAKE_CASE__								=   pipe(**__A		)
									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)

									SCREAMING_SNAKE_CASE__								=   pipe(
									    **__A				,					return_dict=__A				,					)[0]

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									SCREAMING_SNAKE_CASE__								=   np.array(
									    [
									        0.9_9_9_7,
									        0.0_0_0_2,
									        0.9_9_9_7,
									        0.9_9_9_7,
									        0.9_9_6_9,
									        0.0_0_2_3,
									        0.9_9_9_7,
									        0.9_9_6_9,
									        0.9_9_7_0,
									    ]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2


							def   _snake_case	(				self     :List[Any]		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """cpu"""

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_components()

									SCREAMING_SNAKE_CASE__								=   self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__								=   pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)

									SCREAMING_SNAKE_CASE__								=   pipe(**__A		)
									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)

									SCREAMING_SNAKE_CASE__								=   pipe(
									    **__A				,					return_dict=__A				,					)[0]

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									SCREAMING_SNAKE_CASE__								=   np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2


							def   _snake_case	(				self     :List[Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """cpu"""

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_components()

									SCREAMING_SNAKE_CASE__								=   self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__								=   pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)
									SCREAMING_SNAKE_CASE__								=   [
									    pipeline_inputs["""image"""],
									    pipeline_inputs["""image"""],
									]

									SCREAMING_SNAKE_CASE__								=   pipe(**__A		)
									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)
									SCREAMING_SNAKE_CASE__								=   [
									    tuple_pipeline_inputs["""image"""],
									    tuple_pipeline_inputs["""image"""],
									]

									SCREAMING_SNAKE_CASE__								=   pipe(
									    **__A				,					return_dict=__A				,					)[0]

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (2, 64, 64, 3)

									SCREAMING_SNAKE_CASE__								=   np.array(
									    [
									        0.9_9_9_7,
									        0.9_9_8_9,
									        0.0_0_0_8,
									        0.0_0_2_1,
									        0.9_9_6_0,
									        0.0_0_1_8,
									        0.0_0_1_4,
									        0.0_0_0_2,
									        0.9_9_3_3,
									    ]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2


							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   torch.device("""cpu"""		)




									class 	UpperCamelCase_							:
																lowerCamelCase_							     =       1



									SCREAMING_SNAKE_CASE__								=   self.get_dummy_components()

									SCREAMING_SNAKE_CASE__								=   self.pipeline_class(**__A		)
									SCREAMING_SNAKE_CASE__								=   pipe.to(__A		)

									pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   torch.Generator(device=__A		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   pipe.decoder.dtype
									SCREAMING_SNAKE_CASE__								=   1

									SCREAMING_SNAKE_CASE__								=   (
									    batch_size,
									    pipe.decoder.config.in_channels,
									    pipe.decoder.config.sample_size,
									    pipe.decoder.config.sample_size,
									)
									SCREAMING_SNAKE_CASE__								=   pipe.prepare_latents(
									    __A				,					dtype=__A				,					device=__A				,					generator=__A				,					latents=__A				,					scheduler=DummyScheduler()		)

									SCREAMING_SNAKE_CASE__								=   (
									    batch_size,
									    pipe.super_res_first.config.in_channels // 2,
									    pipe.super_res_first.config.sample_size,
									    pipe.super_res_first.config.sample_size,
									)
									SCREAMING_SNAKE_CASE__								=   pipe.prepare_latents(
									    __A				,					dtype=__A				,					device=__A				,					generator=__A				,					latents=__A				,					scheduler=DummyScheduler()		)

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)

									SCREAMING_SNAKE_CASE__								=   pipe(
									    **__A				,					decoder_latents=__A				,					super_res_latents=__A		).images

									SCREAMING_SNAKE_CASE__								=   self.get_dummy_inputs(__A				,					pil_image=__A		)
									# Don't pass image, instead pass embedding
									SCREAMING_SNAKE_CASE__								=   pipeline_inputs.pop("""image"""		)
									SCREAMING_SNAKE_CASE__								=   pipe.image_encoder(__A		).image_embeds

									SCREAMING_SNAKE_CASE__								=   pipe(
									    **__A				,					decoder_latents=__A				,					super_res_latents=__A				,					image_embeddings=__A				,					).images

									# make sure passing text embeddings manually is identical
									assert np.abs(img_out_a - img_out_a		).max() < 1E-4


							@skip_mps
							def   _snake_case	(				self     :Any		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   torch_device == """cpu"""

									# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
									SCREAMING_SNAKE_CASE__								=   1E-2

									self._test_attention_slicing_forward_pass(
									    test_max_difference=__A				,					expected_max_diff=__A		)


							@skip_mps
							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   torch_device == """cpu"""
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   [
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=__A				,					relax_max_difference=__A				,					additional_params_copy_to_batched_inputs=__A				,					)


							def   _snake_case	(				self     :Tuple		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											SCREAMING_SNAKE_CASE__								=   [2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=__A				,					additional_params_copy_to_batched_inputs=__A				,					)
									else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=__A		)


							@skip_mps
							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									return super().test_dict_tuple_outputs_equivalent()


							@skip_mps
							def   _snake_case	(				self     :str		) -> int:



									"""simple docstring"""



									return super().test_save_load_local()


							@skip_mps
							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									return super().test_save_load_optional_components()




@slow
@require_torch_gpu
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :List[str]		) -> Any:



									"""simple docstring"""



									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""		)
									SCREAMING_SNAKE_CASE__								=   load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""		)

									SCREAMING_SNAKE_CASE__								=   UnCLIPImageVariationPipeline.from_pretrained(
									    """kakaobrain/karlo-v1-alpha-image-variations"""				,					torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__								=   pipeline.to(__A		)
									pipeline.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   torch.Generator(device="""cpu"""		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   pipeline(
									    __A				,					generator=__A				,					output_type="""np"""				,					)

									SCREAMING_SNAKE_CASE__								=   output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(__A				,					__A				,					15		)






import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""				,					__A				,					)
											SCREAMING_SNAKE_CASE__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""		)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""		)

									super().__init__(__A				,					__A		)


							def __call__(				self     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""simple docstring"""



									if text is None and query_images is None and images is None:
											raise ValueError(
											    """You have to specify at least one text or query image or image. All three cannot be none."""		)

									if text is not None:
											if isinstance(__A				,					__A		) or (isinstance(__A				,					__A		) and not isinstance(text[0]				,					__A		)):
													SCREAMING_SNAKE_CASE__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   max([len(__A		) for t in text]		)

													# Pad all batch samples to max number of text queries
													for t in text:
															if len(__A		) != max_num_queries:
																	SCREAMING_SNAKE_CASE__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)
															encodings.append(__A		)
											else:
													raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""		)

											if return_tensors == "np":
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "jax" and is_flax_available():
													import jax.numpy as jnp

													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											elif return_tensors == "pt" and is_torch_available():
													import torch

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""attention_mask"""] for encoding in encodings]				,					dim=0		)

											elif return_tensors == "tf" and is_tf_available():
													import tensorflow as tf

													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   tf.stack([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

											else:
													raise ValueError("""Target return tensor type could not be returned"""		)

											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

									if query_images is not None:
											SCREAMING_SNAKE_CASE__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   self.image_processor(
											    __A				,					return_tensors=__A				,					**__A		).pixel_values
											SCREAMING_SNAKE_CASE__								=   query_pixel_values

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

									if text is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif query_images is not None and images is not None:
											SCREAMING_SNAKE_CASE__								=   image_features.pixel_values
											return encoding
									elif text is not None or query_images is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**__A		)				,					tensor_type=__A		)


							def   _snake_case	(				self     :List[Any]				,					*__A     :Dict				,					**__A     :Dict		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor.post_process(*__A				,					**__A		)


							def   _snake_case	(				self     :Optional[int]				,					*__A     :Dict				,					**__A     :List[str]		) -> Optional[Any]:



									"""simple docstring"""



									return self.image_processor.post_process_object_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :str				,					*__A     :List[str]				,					**__A     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									return self.image_processor.post_process_image_guided_detection(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A				,					**__A		)


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



									return self.tokenizer.decode(*__A				,					**__A		)


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,					__A				,					)
									return self.image_processor_class


							@property
							def   _snake_case	(				self     :Any		) -> Optional[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,					__A				,					)
									return self.image_processor