


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class   __a			(    nn.Module      ):
 def __init__(   self				, lowerCAmelCase__ = 16				, lowerCAmelCase__ = 88				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = 0.0				, lowerCAmelCase__ = 32				, lowerCAmelCase__ = None				, lowerCAmelCase__ = False				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = "geglu"				, lowerCAmelCase__ = None				, )					->				List[str]:




        '''simple docstring'''
        super().__init__()
        lowercase__:   Optional[Any]  			=   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__:   Union[str, Any]  			=   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        lowercase__:   Optional[Any]  			=   [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__:   Optional[Any]  			=   [1, 0]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=None				, lowerCAmelCase__=None				, lowerCAmelCase__=None				, lowerCAmelCase__ = True				, )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   int  			=   hidden_states

        lowercase__:   Dict  			=   []
        lowercase__:   List[str]  			=   0
        # attention_mask is not used yet
        for i in range(2 ):
               # for each of the two transformers, pass the corresponding condition tokens
               lowercase__:   Dict  			=   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
               lowercase__:   Dict  			=   self.transformer_index_for_condition[i]
               lowercase__:   Tuple  			=   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__:   Any  			=   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        lowercase__:   Union[str, Any]  			=   output_states + input_states

        if not return_dict:
               return (output_states,)

        return TransformeraDModelOutput(sample=__snake_case )





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






@slow
@require_torch_gpu
class 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
						__a        =torch.manual_seed(0					)
						__a        =pipe(
						    image=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






