

'''simple docstring'''

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       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Any	=	['image_processor', 'tokenizer']
				__UpperCamelCase    :		str	=	'OwlViTImageProcessor'
				__UpperCamelCase    :		Tuple	=	('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(self       ,			__lowercase=None       ,			__lowercase=None       ,			**__lowercase		):
										__lowerCAmelCase							       =				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       ,			)
																__lowerCAmelCase							       =				kwargs.pop('''feature_extractor'''		)

										__lowerCAmelCase							       =				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       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase="max_length"       ,			__lowercase="np"       ,			**__lowercase		):

										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		)):
																						__lowerCAmelCase							       =				[self.tokenizer(__lowercase       ,			padding=__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		)]

																elif isinstance(__lowercase       ,			__lowercase		) and isinstance(text[0]       ,			__lowercase		):
																						__lowerCAmelCase							       =				[]

																						# Maximum number of queries across batch
																						__lowerCAmelCase							       =				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:
																																		__lowerCAmelCase							       =				t + [''' '''] * (max_num_queries - len(__lowercase		))

																												__lowerCAmelCase							       =				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":
																						__lowerCAmelCase							       =				np.concatenate([encoding['''input_ids'''] for encoding in encodings]       ,			axis=0		)
																						__lowerCAmelCase							       =				np.concatenate([encoding['''attention_mask'''] for encoding in encodings]       ,			axis=0		)

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

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

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

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

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

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

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

																__lowerCAmelCase							       =				BatchEncoding()
																__lowerCAmelCase							       =				input_ids
																__lowerCAmelCase							       =				attention_mask

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

										if images is not None:
																__lowerCAmelCase							       =				self.image_processor(__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		)

										if text is not None and images is not None:
																__lowerCAmelCase							       =				image_features.pixel_values
																return encoding
										elif query_images is not None and images is not None:
																__lowerCAmelCase							       =				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       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process_object_detection(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process_image_guided_detection(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.batch_decode(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.decode(*__lowercase       ,			**__lowercase		)




				@property
				def 					_snake_case  (self		):
										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		):
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,			__lowercase       ,			)
										return self.image_processor





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

