




'''simple docstring'''
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


A							:      List[str] 	=       False






@nightly
@require_torch_gpu
class        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       def     snake_case__			(    self     )    ->					Tuple:

           """simple docstring"""



           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
           # remove text_unet
           pipe.remove_unused_weights()
           pipe.to(lowerCamelCase__     )
           pipe.set_progress_bar_config(disable=lowerCamelCase__     )

           __lowercase							=						load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )

           __lowercase							=						torch.manual_seed(0     )
           __lowercase							=						pipe(
               prompt="""first prompt"""					,		image=lowerCamelCase__					,		text_to_image_strength=0.75					,		generator=lowerCamelCase__					,		guidance_scale=7.5					,		num_inference_steps=2					,		output_type="""numpy"""					,		).images

           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase__     )
               __lowercase							=						VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase__     )

           pipe.to(lowerCamelCase__     )
           pipe.set_progress_bar_config(disable=lowerCamelCase__     )

           __lowercase							=						generator.manual_seed(0     )
           __lowercase							=						pipe(
               prompt="""first prompt"""					,		image=lowerCamelCase__					,		text_to_image_strength=0.75					,		generator=lowerCamelCase__					,		guidance_scale=7.5					,		num_inference_steps=2					,		output_type="""numpy"""					,		).images

           assert np.abs(image - new_image     ).sum() < 1E-5, "Models don't have the same forward pass"




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

           """simple docstring"""



           __lowercase							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
           pipe.remove_unused_weights()
           pipe.to(lowerCamelCase__     )
           pipe.set_progress_bar_config(disable=lowerCamelCase__     )

           __lowercase							=						"""cyberpunk 2077"""
           __lowercase							=						load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
           __lowercase							=						torch.manual_seed(0     )
           __lowercase							=						pipe(
               prompt=lowerCamelCase__					,		image=lowerCamelCase__					,		text_to_image_strength=0.75					,		generator=lowerCamelCase__					,		guidance_scale=7.5					,		num_inference_steps=50					,		output_type="""numpy"""					,		).images

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

           assert image.shape == (1, 512, 512, 3)
           __lowercase							=						np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]     )

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










'''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        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




