






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       __UpperCAmelCase		(__A    ):
			__snake_case				:      str 						=							["image_processor", "tokenizer"]
			__snake_case				:      Optional[Any] 						=							"ViltImageProcessor"
			__snake_case				:      Union[str, Any] 						=							("BertTokenizer", "BertTokenizerFast")







			def __init__(	self:  List[str]      ,	UpperCAmelCase_:  int=None      ,	UpperCAmelCase_:  int=None      ,	**UpperCAmelCase_:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""      ,	lowerCAmelCase_      ,	)
															_SCREAMING_SNAKE_CASE	      =  kwargs.pop("""feature_extractor"""     )

									_SCREAMING_SNAKE_CASE	      =  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__(lowerCAmelCase_      ,	lowerCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  self.image_processor







			def __call__(	self:  Any      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,	UpperCAmelCase_:  bool = True      ,	UpperCAmelCase_:  Union[bool, str, PaddingStrategy] = False      ,	UpperCAmelCase_:  Union[bool, str, TruncationStrategy] = None      ,	UpperCAmelCase_:  Optional[int] = None      ,	UpperCAmelCase_:  int = 0      ,	UpperCAmelCase_:  Optional[int] = None      ,	UpperCAmelCase_:  Optional[bool] = None      ,	UpperCAmelCase_:  Optional[bool] = None      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = True      ,	UpperCAmelCase_:  Optional[Union[str, TensorType]] = None      ,	**UpperCAmelCase_:  List[str]      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.tokenizer(
									    text=lowerCAmelCase_      ,	add_special_tokens=lowerCAmelCase_      ,	padding=lowerCAmelCase_      ,	truncation=lowerCAmelCase_      ,	max_length=lowerCAmelCase_      ,	stride=lowerCAmelCase_      ,	pad_to_multiple_of=lowerCAmelCase_      ,	return_token_type_ids=lowerCAmelCase_      ,	return_attention_mask=lowerCAmelCase_      ,	return_overflowing_tokens=lowerCAmelCase_      ,	return_special_tokens_mask=lowerCAmelCase_      ,	return_offsets_mapping=lowerCAmelCase_      ,	return_length=lowerCAmelCase_      ,	verbose=lowerCAmelCase_      ,	return_tensors=lowerCAmelCase_      ,	**lowerCAmelCase_      ,	)
									# add pixel_values + pixel_mask
									_SCREAMING_SNAKE_CASE	      =  self.image_processor(lowerCAmelCase_      ,	return_tensors=lowerCAmelCase_     )
									encoding.update(lowerCAmelCase_     )

									return encoding







			def 							UpperCamelCase      (	self:  List[str]      ,	*UpperCAmelCase_:  Union[str, Any]      ,	**UpperCAmelCase_:  List[Any]     ):







									'''simple docstring'''




									return self.tokenizer.batch_decode(*lowerCAmelCase_      ,	**lowerCAmelCase_     )







			def 							UpperCamelCase      (	self:  List[str]      ,	*UpperCAmelCase_:  Union[str, Any]      ,	**UpperCAmelCase_:  Dict     ):







									'''simple docstring'''




									return self.tokenizer.decode(*lowerCAmelCase_      ,	**lowerCAmelCase_     )







			@property
			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.tokenizer.model_input_names
									_SCREAMING_SNAKE_CASE	      =  self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







			@property
			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,	lowerCAmelCase_      ,	)
									return self.image_processor_class







			@property
			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,	lowerCAmelCase_      ,	)
									return self.image_processor









"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict=768						)						->			List[Any]:
				super().__init__(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					proj_size

				UpperCAmelCase_     : Optional[Any]			     =					CLIPVisionModel(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					PaintByExampleMapper(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					nn.LayerNorm(config.hidden_size						)
				UpperCAmelCase_     : List[Any]			     =					nn.Linear(config.hidden_size	,	self.proj_size						)

				# uncondition for scaling
				UpperCAmelCase_     : Optional[int]			     =					nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict=False						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					self.model(pixel_values=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					clip_output.pooler_output
				UpperCAmelCase_     : List[Any]			     =					self.mapper(latent_states[:, None]						)
				UpperCAmelCase_     : List[str]			     =					self.final_layer_norm(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					self.proj_out(lowerCAmelCase_						)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]						)						->			Tuple:
				super().__init__()
				UpperCAmelCase_     : List[Any]			     =					(config.num_hidden_layers + 1) // 5
				UpperCAmelCase_     : Optional[Any]			     =					config.hidden_size
				UpperCAmelCase_     : List[str]			     =					1
				UpperCAmelCase_     : Union[str, Any]			     =					nn.ModuleList(
				    [
				        BasicTransformerBlock(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	activation_fn="gelu"	,	attention_bias=lowerCAmelCase_						)
				        for _ in range(lowerCAmelCase_						)
				    ]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			str:
				for block in self.blocks:
							UpperCAmelCase_     : int			     =					block(lowerCAmelCase_						)

				return hidden_states



