



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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""image_processor""", """tokenizer"""]
					lowercase_								=		"""OwlViTImageProcessor"""
					lowercase_								=		("""CLIPTokenizer""", """CLIPTokenizerFast""")







					def __init__(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     int=None				,       **SCREAMING_SNAKE_CASE :     Tuple					):
											lowercase__					:   List[str]													=					None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																	    " instead."				,       lowercase_				,       )
																	lowercase__					:   int													=					kwargs.pop("feature_extractor"					)

											lowercase__					:   Optional[int]													=					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__(lowercase_				,       lowercase_					)







					def __call__(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Dict=None				,       SCREAMING_SNAKE_CASE :     Tuple=None				,       SCREAMING_SNAKE_CASE :     str=None				,       SCREAMING_SNAKE_CASE :     Optional[int]="max_length"				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="np"				,       **SCREAMING_SNAKE_CASE :     List[Any]					):







											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(lowercase_				,       lowercase_					) or (isinstance(lowercase_				,       lowercase_					) and not isinstance(text[0]				,       lowercase_					)):
																							lowercase__					:   Union[str, Any]													=					[self.tokenizer(lowercase_				,       padding=lowercase_				,       return_tensors=lowercase_				,       **lowercase_					)]

																	elif isinstance(lowercase_				,       lowercase_					) and isinstance(text[0]				,       lowercase_					):
																							lowercase__					:   Optional[int]													=					[]

																							# Maximum number of queries across batch
																							lowercase__					:   str													=					max([len(lowercase_					) for t in text]					)

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

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

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

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

																							lowercase__					:   List[Any]													=					jnp.concatenate([encoding["input_ids"] for encoding in encodings]				,       axis=0					)
																							lowercase__					:   Dict													=					jnp.concatenate([encoding["attention_mask"] for encoding in encodings]				,       axis=0					)

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

																							lowercase__					:   List[str]													=					torch.cat([encoding["input_ids"] for encoding in encodings]				,       dim=0					)
																							lowercase__					:   Dict													=					torch.cat([encoding["attention_mask"] for encoding in encodings]				,       dim=0					)

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

																							lowercase__					:   str													=					tf.stack([encoding["input_ids"] for encoding in encodings]				,       axis=0					)
																							lowercase__					:   List[str]													=					tf.stack([encoding["attention_mask"] for encoding in encodings]				,       axis=0					)

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

																	lowercase__					:   int													=					BatchEncoding()
																	lowercase__					:   str													=					input_ids
																	lowercase__					:   Any													=					attention_mask

											if query_images is not None:
																	lowercase__					:   int													=					BatchEncoding()
																	lowercase__					:   Dict													=					self.image_processor(
																	    lowercase_				,       return_tensors=lowercase_				,       **lowercase_					).pixel_values
																	lowercase__					:   Optional[int]													=					query_pixel_values

											if images is not None:
																	lowercase__					:   Optional[int]													=					self.image_processor(lowercase_				,       return_tensors=lowercase_				,       **lowercase_					)

											if text is not None and images is not None:
																	lowercase__					:   Optional[Any]													=					image_features.pixel_values
																	return encoding
											elif query_images is not None and images is not None:
																	lowercase__					:   Optional[Any]													=					image_features.pixel_values
																	return encoding
											elif text is not None or query_images is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**lowercase_					)				,       tensor_type=lowercase_					)







					def 			snake_case					(							self :     List[Any]				,       *SCREAMING_SNAKE_CASE :     str				,       **SCREAMING_SNAKE_CASE :     int					):
											return self.image_processor.post_process(*lowercase_				,       **lowercase_					)







					def 			snake_case					(							self :     Dict				,       *SCREAMING_SNAKE_CASE :     str				,       **SCREAMING_SNAKE_CASE :     List[str]					):
											return self.image_processor.post_process_object_detection(*lowercase_				,       **lowercase_					)







					def 			snake_case					(							self :     Union[str, Any]				,       *SCREAMING_SNAKE_CASE :     Dict				,       **SCREAMING_SNAKE_CASE :     Dict					):
											return self.image_processor.post_process_image_guided_detection(*lowercase_				,       **lowercase_					)







					def 			snake_case					(							self :     str				,       *SCREAMING_SNAKE_CASE :     int				,       **SCREAMING_SNAKE_CASE :     List[str]					):
											return self.tokenizer.batch_decode(*lowercase_				,       **lowercase_					)







					def 			snake_case					(							self :     List[str]				,       *SCREAMING_SNAKE_CASE :     Optional[int]				,       **SCREAMING_SNAKE_CASE :     Optional[int]					):
											return self.tokenizer.decode(*lowercase_				,       **lowercase_					)







					@property
					def 			snake_case					(							self :     Dict					):
											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,       lowercase_				,       )
											return self.image_processor_class







					@property
					def 			snake_case					(							self :     List[Any]					):
											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,       lowercase_				,       )
											return self.image_processor









import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@property
					def 			snake_case					(							self :     Any					):
									torch.manual_seed(0					)
									lowercase__					:   Tuple													=					UNetaDModel(
									    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=("DownBlock2D", "AttnDownBlock2D")				,       up_block_types=("AttnUpBlock2D", "UpBlock2D")				,       )
									return model







					@property
					def 			snake_case					(							self :     List[str]					):
									torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					VQModel(
									    block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=3				,       )
									return model







					@property
					def 			snake_case					(							self :     Dict					):
									torch.manual_seed(0					)
									lowercase__					:   str													=					CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_000				,       )
									return CLIPTextModel(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   Any													=					self.dummy_uncond_unet
									lowercase__					:   Dict													=					DDIMScheduler()
									lowercase__					:   Optional[Any]													=					self.dummy_vq_model

									lowercase__					:   Union[str, Any]													=					LDMPipeline(unet=SCREAMING_SNAKE_CASE				,       vqvae=SCREAMING_SNAKE_CASE				,       scheduler=SCREAMING_SNAKE_CASE					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"					).images

									lowercase__					:   str													=					torch.manual_seed(0					)
									lowercase__					:   List[Any]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"				,       return_dict=SCREAMING_SNAKE_CASE					)[0]

									lowercase__					:   Any													=					image[0, -3:, -3:, -1]
									lowercase__					:   Optional[Any]													=					image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__					:   List[Any]													=					np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]					)
									lowercase__					:   Optional[Any]													=					1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice					).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < tolerance






@slow
@require_torch
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					torch.manual_seed(0					)
									lowercase__					:   Tuple													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=5				,       output_type="numpy"					).images

									lowercase__					:   Tuple													=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									lowercase__					:   Optional[Any]													=					np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]					)
									lowercase__					:   int													=					1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice					).max() < tolerance




