






"""simple docstring"""
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     ):
	__magic_name__	      =				['''image_processor''', '''tokenizer''']
	__magic_name__	      =				'''ViltImageProcessor'''
	__magic_name__	      =				('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__(      self					: List[str]	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: int=None	,	**lowerCAmelCase_					: List[Any]						)						->			List[str]:
				UpperCAmelCase_     : Any			     =					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_	,	)
							UpperCAmelCase_     : int			     =					kwargs.pop("feature_extractor"						)

				UpperCAmelCase_     : str			     =					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_						)
				UpperCAmelCase_     : Any			     =					self.image_processor





	def __call__(      self					: Any	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Union[bool, str, PaddingStrategy] = False	,	lowerCAmelCase_					: Union[bool, str, TruncationStrategy] = None	,	lowerCAmelCase_					: Optional[int] = None	,	lowerCAmelCase_					: int = 0	,	lowerCAmelCase_					: Optional[int] = None	,	lowerCAmelCase_					: Optional[bool] = None	,	lowerCAmelCase_					: Optional[bool] = None	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = True	,	lowerCAmelCase_					: Optional[Union[str, TensorType]] = None	,	**lowerCAmelCase_					: List[str]	,	)						->			BatchEncoding:
				UpperCAmelCase_     : Tuple			     =					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
				UpperCAmelCase_     : List[str]			     =					self.image_processor(lowerCAmelCase_	,	return_tensors=lowerCAmelCase_						)
				encoding.update(lowerCAmelCase_						)

				return encoding





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	*lowerCAmelCase_					: Union[str, Any]	,	**lowerCAmelCase_					: List[Any]						)						->			int:
				return self.tokenizer.batch_decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	*lowerCAmelCase_					: Union[str, Any]	,	**lowerCAmelCase_					: Dict						)						->			Any:
				return self.tokenizer.decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Tuple:
				UpperCAmelCase_     : Optional[int]			     =					self.tokenizer.model_input_names
				UpperCAmelCase_     : Tuple			     =					self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			str:
				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      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			str:
				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



