import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCamelCase  							=						logging.get_logger(__name__)



class   snake_case_       (		__A       ):
   __A	:		Dict         =				"upernet"






   def __init__(      self				:     List[str]	,						lowercase_				:     List[Any]=None	,						lowercase_				:     Optional[int]=5_12	,						lowercase_				:     str=0.02	,						lowercase_				:     Optional[int]=[1, 2, 3, 6]	,						lowercase_				:     List[str]=True	,						lowercase_				:     Dict=0.4	,						lowercase_				:     Optional[Any]=3_84	,						lowercase_				:     Optional[int]=2_56	,						lowercase_				:     int=1	,						lowercase_				:     str=False	,						lowercase_				:     Optional[int]=2_55	,						**lowercase_				:     Union[str, Any]	,						)					->						List[Any]:
       super().__init__(**lowercase_  )

       if backbone_config is None:
           logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."  )
           lowercase__ :   str    					=				CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]  )
       elif isinstance(lowercase_	,						lowercase_  ):
           lowercase__ :   List[Any]    					=				backbone_config.get("model_type"  )
           lowercase__ :   List[str]    					=				CONFIG_MAPPING[backbone_model_type]
           lowercase__ :   Tuple    					=				config_class.from_dict(lowercase_  )

       lowercase__ :   int    					=				backbone_config
       lowercase__ :   str    					=				hidden_size
       lowercase__ :   Dict    					=				initializer_range
       lowercase__ :   int    					=				pool_scales
       lowercase__ :   int    					=				use_auxiliary_head
       lowercase__ :   List[str]    					=				auxiliary_loss_weight
       lowercase__ :   Union[str, Any]    					=				auxiliary_in_channels
       lowercase__ :   Tuple    					=				auxiliary_channels
       lowercase__ :   List[Any]    					=				auxiliary_num_convs
       lowercase__ :   Tuple    					=				auxiliary_concat_input
       lowercase__ :   Optional[Any]    					=				loss_ignore_index







   def       __UpperCamelCase     (      self				:     List[str]  )					->						Union[str, Any]:
       lowercase__ :   Union[str, Any]    					=				copy.deepcopy(self.__dict__  )
       lowercase__ :   Dict    					=				self.backbone_config.to_dict()
       lowercase__ :   List[Any]    					=				self.__class__.model_type
       return output










'''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
