


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						(     SCREAMING_SNAKE_CASE__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase		:					List[Any]          ="""BridgeTowerImageProcessor"""
							__UpperCAmelCase		:					Tuple          =("""RobertaTokenizer""", """RobertaTokenizerFast""")

							def __init__(  self					,     __a					,     __a			):
										super().__init__(snake_case__					,     snake_case__			)

							def __call__(  self					,     __a					,     __a = None					,     __a = True					,     __a = False					,     __a = None					,     __a = None					,     __a = 0					,     __a = None					,     __a = None					,     __a = None					,     __a = False					,     __a = False					,     __a = False					,     __a = False					,     __a = True					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    self.tokenizer(
										    text=snake_case__					,     add_special_tokens=snake_case__					,     padding=snake_case__					,     truncation=snake_case__					,     max_length=snake_case__					,     stride=snake_case__					,     pad_to_multiple_of=snake_case__					,     return_token_type_ids=snake_case__					,     return_attention_mask=snake_case__					,     return_overflowing_tokens=snake_case__					,     return_special_tokens_mask=snake_case__					,     return_offsets_mapping=snake_case__					,     return_length=snake_case__					,     verbose=snake_case__					,     return_tensors=snake_case__					,     **snake_case__					,     )
										# add pixel_values + pixel_mask
										__lowerCAmelCase       					=    self.image_processor(
										    snake_case__					,     return_tensors=snake_case__					,     do_normalize=snake_case__					,     do_center_crop=snake_case__					,     **snake_case__			)
										encoding.update(snake_case__			)

										return encoding

							def     snake_case       (  self					,     *__a					,     **__a			):
										return self.tokenizer.batch_decode(*snake_case__					,     **snake_case__			)

							def     snake_case       (  self					,     *__a					,     **__a			):
										return self.tokenizer.decode(*snake_case__					,     **snake_case__			)



							@property
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.tokenizer.model_input_names
										__lowerCAmelCase       					=    self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)








"""simple docstring"""


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

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


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

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



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




