





"""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_				(     UpperCamelCase__ ):
				"""simple docstring"""
				__snake_case :							int									=		["""image_processor""", """tokenizer"""]
				__snake_case :							Union[str, Any]									=		"""OwlViTImageProcessor"""
				__snake_case :							Tuple									=		("""CLIPTokenizer""", """CLIPTokenizerFast""")




				def __init__(  self	:Optional[Any]    ,     __lowercase	:int=None    ,     __lowercase	:Optional[int]=None    ,     **__lowercase	:str      ):
											__lowerCamelCase   :   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.'''    ,     __A    ,     )
																		__lowerCamelCase   :   str       =kwargs.pop('''feature_extractor'''      )

											__lowerCamelCase   :   Optional[Any]       =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    ,     __lowercase	:Dict=None    ,     __lowercase	:List[str]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[int]="max_length"    ,     __lowercase	:Tuple="np"    ,     **__lowercase	:int      ):



											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      )):
																									__lowerCamelCase   :   int       =[self.tokenizer(__A    ,     padding=__A    ,     return_tensors=__A    ,     **__A      )]

																		elif isinstance(__A    ,     __A      ) and isinstance(text[0]    ,     __A      ):
																									__lowerCamelCase   :   List[str]       =[]

																									# Maximum number of queries across batch
																									__lowerCamelCase   :   Dict       =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:
																																							__lowerCamelCase   :   List[str]       =t + [''' '''] * (max_num_queries - len(__A      ))

																																__lowerCamelCase   :   int       =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":
																									__lowerCamelCase   :   str       =np.concatenate([encoding['''input_ids'''] for encoding in encodings]    ,     axis=0      )
																									__lowerCamelCase   :   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

																									__lowerCamelCase   :   Union[str, Any]       =jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]    ,     axis=0      )
																									__lowerCamelCase   :   List[str]       =jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]    ,     axis=0      )

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

																									__lowerCamelCase   :   Dict       =torch.cat([encoding['''input_ids'''] for encoding in encodings]    ,     dim=0      )
																									__lowerCamelCase   :   int       =torch.cat([encoding['''attention_mask'''] for encoding in encodings]    ,     dim=0      )

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

																									__lowerCamelCase   :   List[Any]       =tf.stack([encoding['''input_ids'''] for encoding in encodings]    ,     axis=0      )
																									__lowerCamelCase   :   Union[str, Any]       =tf.stack([encoding['''attention_mask'''] for encoding in encodings]    ,     axis=0      )

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

																		__lowerCamelCase   :   Optional[Any]       =BatchEncoding()
																		__lowerCamelCase   :   str       =input_ids
																		__lowerCamelCase   :   List[Any]       =attention_mask

											if query_images is not None:
																		__lowerCamelCase   :   str       =BatchEncoding()
																		__lowerCamelCase   :   Tuple       =self.image_processor(
																		    __A    ,     return_tensors=__A    ,     **__A      ).pixel_values
																		__lowerCamelCase   :   List[str]       =query_pixel_values

											if images is not None:
																		__lowerCamelCase   :   Union[str, Any]       =self.image_processor(__A    ,     return_tensors=__A    ,     **__A      )

											if text is not None and images is not None:
																		__lowerCamelCase   :   Optional[int]       =image_features.pixel_values
																		return encoding
											elif query_images is not None and images is not None:
																		__lowerCamelCase   :   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(**__A      )    ,     tensor_type=__A      )




				def        __lowercase	(  self	:List[Any]    ,     *__lowercase	:Dict    ,     **__lowercase	:Dict      ):
											return self.image_processor.post_process(*__A    ,     **__A      )




				def        __lowercase	(  self	:Optional[int]    ,     *__lowercase	:Dict    ,     **__lowercase	:List[str]      ):
											return self.image_processor.post_process_object_detection(*__A    ,     **__A      )




				def        __lowercase	(  self	:str    ,     *__lowercase	:List[str]    ,     **__lowercase	:Union[str, Any]      ):
											return self.image_processor.post_process_image_guided_detection(*__A    ,     **__A      )




				def        __lowercase	(  self	:Dict    ,     *__lowercase	:List[str]    ,     **__lowercase	:List[str]      ):
											return self.tokenizer.batch_decode(*__A    ,     **__A      )




				def        __lowercase	(  self	:Dict    ,     *__lowercase	:Dict    ,     **__lowercase	:List[str]      ):
											return self.tokenizer.decode(*__A    ,     **__A      )




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











from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@register_to_config
							def __init__(				self     :Union[str, Any]				,					__A     :int = 3				,					__A     :int = 3				,					__A     :Tuple[str] = ("DownEncoderBlock2D",)				,					__A     :Tuple[str] = ("UpDecoderBlock2D",)				,					__A     :Tuple[int] = (64,)				,					__A     :int = 1				,					__A     :str = "silu"				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 256				,					__A     :int = 32				,					__A     :Optional[int] = None				,					__A     :float = 0.1_8_2_1_5				,					__A     :str = "group"				,					) -> Any:



									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE__								=   Encoder(
									    in_channels=__A				,					out_channels=__A				,					down_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					double_z=__A				,					)

									SCREAMING_SNAKE_CASE__								=   vq_embed_dim if vq_embed_dim is not None else latent_channels

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   VectorQuantizer(__A				,					__A				,					beta=0.2_5				,					remap=__A				,					sane_index_shape=__A		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE__								=   Decoder(
									    in_channels=__A				,					out_channels=__A				,					up_block_types=__A				,					block_out_channels=__A				,					layers_per_block=__A				,					act_fn=__A				,					norm_num_groups=__A				,					norm_type=__A				,					)


							@apply_forward_hook
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> VQEncoderOutput:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.encoder(__A		)
									SCREAMING_SNAKE_CASE__								=   self.quant_conv(__A		)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=__A		)


							@apply_forward_hook
							def   _snake_case	(				self     :Tuple				,					__A     :torch.FloatTensor				,					__A     :bool = False				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									if not force_not_quantize:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   h
									SCREAMING_SNAKE_CASE__								=   self.post_quant_conv(__A		)
									SCREAMING_SNAKE_CASE__								=   self.decoder(__A				,					quant if self.config.norm_type == """spatial""" else None		)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)


							def   _snake_case	(				self     :int				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sample
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		).latents
									SCREAMING_SNAKE_CASE__								=   self.decode(__A		).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)