




"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class lowerCAmelCase__	(     lowercase,	lowercase,	lowercase						):






			'''simple docstring'''



			@register_to_config
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = False  ,      ):
							super().__init__()

							_lowerCamelCase     :			Any    =    nn.Embedding(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Tuple    =    nn.Embedding(lowercase  ,      lowercase  )
							_lowerCamelCase     :			str    =    False

							_lowerCamelCase     :			List[str]    =    nn.Dropout(p=lowercase  )

							_lowerCamelCase     :			Dict    =    TaConfig(
							    vocab_size=lowercase  ,      d_model=lowercase  ,      num_heads=lowercase  ,      d_kv=lowercase  ,      d_ff=lowercase  ,      dropout_rate=lowercase  ,      feed_forward_proj=lowercase  ,      is_decoder=lowercase  ,      is_encoder_decoder=lowercase  ,      )

							_lowerCamelCase     :			int    =    nn.ModuleList()
							for lyr_num in range(lowercase  ):
											_lowerCamelCase     :			List[Any]    =    TaBlock(lowercase  )
											self.encoders.append(lowercase  )

							_lowerCamelCase     :			int    =    TaLayerNorm(lowercase  )
							_lowerCamelCase     :			int    =    nn.Dropout(p=lowercase  )






			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			List[Any]    =    self.token_embedder(lowercase  )

							_lowerCamelCase     :			List[str]    =    encoder_input_tokens.shape[1]
							_lowerCamelCase     :			Union[str, Any]    =    torch.arange(lowercase  ,      device=encoder_input_tokens.device  )
							x += self.position_encoding(lowercase  )

							_lowerCamelCase     :			List[str]    =    self.dropout_pre(lowercase  )

							# inverted the attention mask
							_lowerCamelCase     :			List[str]    =    encoder_input_tokens.size()
							_lowerCamelCase     :			int    =    self.get_extended_attention_mask(lowercase  ,      lowercase  )

							for lyr in self.encoders:
											_lowerCamelCase     :			Any    =    lyr(lowercase  ,      lowercase  )[0]
							_lowerCamelCase     :			int    =    self.layer_norm(lowercase  )

							return self.dropout_post(lowercase  ), encoder_inputs_mask





"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

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






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states