
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case = 16		,    snake_case = 88		,    snake_case = None		,    snake_case = 1		,    snake_case = 0.0		,    snake_case = 32		,    snake_case = None		,    snake_case = False		,    snake_case = None		,    snake_case = None		,    snake_case = "geglu"		,    snake_case = None		,    ):
		super().__init__()
		lowercase       =    nn.ModuleList(
		    [
		        TransformeraDModel(
		            num_attention_heads=snake_case		,    attention_head_dim=snake_case		,    in_channels=snake_case		,    num_layers=snake_case		,    dropout=snake_case		,    norm_num_groups=snake_case		,    cross_attention_dim=snake_case		,    attention_bias=snake_case		,    sample_size=snake_case		,    num_vector_embeds=snake_case		,    activation_fn=snake_case		,    num_embeds_ada_norm=snake_case		,    )
		        for _ in range(2       )
		    ]       )

		# Variables that can be set by a pipeline:

		# The ratio of transformer1 to transformer2's output states to be combined during inference
		lowercase       =    0.5

		# The shape of `encoder_hidden_states` is expected to be
		# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
		lowercase       =    [77, 257]

		# Which transformer to use to encode which condition.
		# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
		lowercase       =    [1, 0]





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    snake_case = True		,    ):
		lowercase       =    hidden_states

		lowercase       =    []
		lowercase       =    0
		# attention_mask is not used yet
		for i in range(2       ):
			# for each of the two transformers, pass the corresponding condition tokens
			lowercase       =    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
			lowercase       =    self.transformer_index_for_condition[i]
			lowercase       =    self.transformers[transformer_index](
			    snake_case		,    encoder_hidden_states=snake_case		,    timestep=snake_case		,    cross_attention_kwargs=snake_case		,    return_dict=snake_case		,    )[0]
			encoded_states.append(encoded_state - input_states       )
			tokens_start += self.condition_lengths[i]

		lowercase       =    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
		lowercase       =    output_states + input_states

		if not return_dict:
			return (output_states,)

		return TransformeraDModelOutput(sample=snake_case       )



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

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


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


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

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

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





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

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

