

'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_    						=       object()

# For specifying empty leaf dict `{}`
UpperCamelCase_    						=       object()






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     int						)				->  Optional[int]:
						lowercase :							Optional[Any]    		=tuple((re.compile(x + '''$'''						) for x in qs)						)
						for i in range(len(__magic_name__						) - len(__magic_name__						) + 1						):
												lowercase :							Union[str, Any]    		=[x.match(__magic_name__						) for x, y in zip(__magic_name__   , ks[i:]						)]
												if matches and all(__magic_name__						):
																		return True
						return False






def 				_lowerCAmelCase			(   __magic_name__						:     Dict						)				->  List[str]:
						def replace(__magic_name__						:     Optional[int]   , __magic_name__						:     Optional[Any]						):
												for rule, replacement in rules:
																		if _match(__magic_name__   , __magic_name__						):
																								return replacement
												return val

						return replace






def 				_lowerCAmelCase			(   )				->  int:
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P('''mp'''   , __magic_name__						)),
						    (("transformer", "wte", "embedding"), P('''mp'''   , __magic_name__						)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("attention", "out_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("mlp", "c_fc", "bias"), P('''mp'''						)),
						    (("mlp", "c_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]







def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  int:
						lowercase :							int    		=_get_partition_rules()
						lowercase :							Tuple    		=_replacement_rules(__magic_name__						)
						lowercase :							Any    		={k: _unmatched for k in flatten_dict(__magic_name__						)}
						lowercase :							Any    		={k: replace(__magic_name__   , __magic_name__						) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(__magic_name__						)						)










import gc
import tempfile
import unittest

import numpy as np
import torch

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


_lowerCAmelCase	:   Any    =   False


class 	__snake_case   ( unittest.TestCase ):
		pass



@nightly
@require_torch_gpu
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

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

						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a_						)
										lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained(a_    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  generator.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

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

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

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]						)

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

						lowerCAmelCase__		  =  'A painting of a squirrel eating a burger '
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.text_to_image(
						    prompt=a_    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'						).images

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

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

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

						lowerCAmelCase__		  =  pipe.image_variation(a_    ,generator=a_    ,output_type='numpy'						).images

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

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]						)

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