






"""simple docstring"""

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

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


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

class 						A_							(      snake_case_  ):







				def __init__(							self							:						str						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						List[Any]=7_6_8	)				->					Union[str, Any]:
										super().__init__(__lowerCamelCase	)
										__magic_name__            =     proj_size

										__magic_name__            =     CLIPVisionModel(__lowerCamelCase	)
										__magic_name__            =     PaintByExampleMapper(__lowerCamelCase	)
										__magic_name__            =     nn.LayerNorm(config.hidden_size	)
										__magic_name__            =     nn.Linear(config.hidden_size						,    self.proj_size	)

										# uncondition for scaling
										__magic_name__            =     nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)







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

										return latent_states





class 						A_							(      nn.Module  ):







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						Optional[int]	)				->					Optional[Any]:
										super().__init__()
										__magic_name__            =     (config.num_hidden_layers + 1) // 5
										__magic_name__            =     config.hidden_size
										__magic_name__            =     1
										__magic_name__            =     nn.ModuleList(
										    [
										        BasicTransformerBlock(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    activation_fn="gelu"						,    attention_bias=__lowerCamelCase	)
										        for _ in range(__lowerCamelCase	)
										    ]	)







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict	)				->					Any:
										for block in self.blocks:
																__magic_name__            =     block(__lowerCamelCase	)

										return hidden_states











"""simple docstring"""

from typing import List, Optional, Union

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

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __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							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										if images is None and text is None:
																raise ValueError("You have to specify either images or text."	)

										# Get only text
										if images is None:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

										if text_encoding is not None:
																encoding_image_processor.update(__lowerCamelCase	)

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



