


"""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


__SCREAMING_SNAKE_CASE				:     Dict		=     False





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





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int							)       ->Optional[Any]:



														"""simple docstring"""



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

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

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(UpperCAmelCase_							)
																					snake_case_     							=		VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase_							)

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

														snake_case_     							=		generator.manual_seed(0							)
														snake_case_     							=		pipe(
														    prompt="""first prompt"""		,							image=UpperCAmelCase_		,							text_to_image_strength=0.75		,							generator=UpperCAmelCase_		,							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 		lowerCAmelCase							(					self				:   Union[str, Any]							)       ->Any:



														"""simple docstring"""



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

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

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

														assert image.shape == (1, 512, 512, 3)
														snake_case_     							=		np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]							)

														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   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

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

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

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

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

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



