





'''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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='OwlViTImageProcessor'
				__a            =('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(  self     :					List[Any]					,				__a     :					str=None					,				__a     :					List[str]=None					,				**__a     :					List[Any]					):
								_a								= 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					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= 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     :					Union[str, Any]					,				__a     :					Any=None					,				__a     :					List[str]=None					,				__a     :					int=None					,				__a     :					Optional[int]="max_length"					,				__a     :					List[str]="np"					,				**__a     :					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(__a					,				__a					) or (isinstance(__a					,				__a					) and not isinstance(text[0]					,				__a					)):
																_a								= [self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)]

												elif isinstance(__a					,				__a					) and isinstance(text[0]					,				__a					):
																_a								= []

																# Maximum number of queries across batch
																_a								= 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:
																								_a								= t + [" "] * (max_num_queries - len(__a					))

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

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

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

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

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

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

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

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

												_a								= BatchEncoding()
												_a								= input_ids
												_a								= attention_mask

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

								if images is not None:
												_a								= self.image_processor(__a					,				return_tensors=__a					,				**__a					)

								if text is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_a								= 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   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					Union[str, Any]					,				**__a     :					int					):
								return self.image_processor.post_process(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				*__a     :					Optional[Any]					,				**__a     :					List[str]					):
								return self.image_processor.post_process_object_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*__a     :					Dict					,				**__a     :					Union[str, Any]					):
								return self.image_processor.post_process_image_guided_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					Tuple					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					List[Any]					,				**__a     :					Optional[int]					):
								return self.tokenizer.decode(*__a					,				**__a					)




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







'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
