

import argparse
import os
import re

import packaging.version


lowerCAmelCase							:  Optional[Any]            =						"""examples/"""
lowerCAmelCase							:  Dict            =						{
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
lowerCAmelCase							:  Optional[int]            =						{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
lowerCAmelCase							:  int            =						"""README.md"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						with open(_UpperCAmelCase    ,     "r"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												SCREAMING_SNAKE_CASE_:			Dict  		=	f.read()
						SCREAMING_SNAKE_CASE_:			Dict  		=	REPLACE_PATTERNS[pattern]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	replace.replace("VERSION"    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	re_pattern.sub(_UpperCAmelCase    ,     _UpperCAmelCase   )
						with open(_UpperCAmelCase    ,     "w"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												f.write(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						for folder, directories, fnames in os.walk(_UpperCAmelCase   ):
												# Removing some of the folders with non-actively maintained examples from the walk
												if "research_projects" in directories:
																		directories.remove("research_projects"   )
												if "legacy" in directories:
																		directories.remove("legacy"   )
												for fname in fnames:
																		if fname.endswith(".py"   ):
																								update_version_in_file(os.path.join(_UpperCAmelCase    ,     _UpperCAmelCase   )    ,     _UpperCAmelCase    ,     pattern="examples"   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						if not patch:
												update_version_in_examples(_UpperCAmelCase   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"🤗 Transformers currently provides the following architectures"
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"1. Want to contribute a new model?"
						with open(_UpperCAmelCase    ,     "r"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f.readlines()

						# Find the start of the list.
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while not lines[start_index].startswith(_start_prompt   ):
												start_index += 1
						start_index += 1

						SCREAMING_SNAKE_CASE_:			str  		=	start_index
						# Update the lines in the model list.
						while not lines[index].startswith(_end_prompt   ):
												if lines[index].startswith("1."   ):
																		SCREAMING_SNAKE_CASE_:			int  		=	lines[index].replace(
																		    "https://huggingface.co/docs/transformers/main/model_doc"    ,     "https://huggingface.co/docs/transformers/model_doc"    ,     )
												index += 1

						with open(_UpperCAmelCase    ,     "w"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												f.writelines(_UpperCAmelCase   )







def       A_				(   ):
						with open(REPLACE_FILES["init"]    ,     "r"   ) as f:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	f.read()
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	REPLACE_PATTERNS["init"][0].search(_UpperCAmelCase   ).groups()[0]
						return packaging.version.parse(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_version()
						if patch and default_version.is_devrelease:
												raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"   )
						if default_version.is_devrelease:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	default_version.base_version
						elif patch:
												SCREAMING_SNAKE_CASE_:			Any  		=	f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
						else:
												SCREAMING_SNAKE_CASE_:			Dict  		=	f"{default_version.major}.{default_version.minor + 1}.0"

						# Now let's ask nicely if that's the right one.
						SCREAMING_SNAKE_CASE_:			int  		=	input(f"Which version are you releasing? [{default_version}]"   )
						if len(_UpperCAmelCase   ) == 0:
												SCREAMING_SNAKE_CASE_:			str  		=	default_version

						print(f"Updating version to {version}."   )
						global_version_update(_UpperCAmelCase    ,     patch=_UpperCAmelCase   )
						if not patch:
												print("Cleaning main README, don't forget to run `make fix-copies`."   )
												clean_main_ref_in_model_list()







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			int  		=	get_version()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	f"{current_version.major}.{current_version.minor + 1}.0.dev0"
						SCREAMING_SNAKE_CASE_:			List[str]  		=	current_version.base_version

						# Check with the user we got that right.
						SCREAMING_SNAKE_CASE_:			int  		=	input(f"Which version are we developing now? [{dev_version}]"   )
						if len(_UpperCAmelCase   ) == 0:
												SCREAMING_SNAKE_CASE_:			int  		=	dev_version

						print(f"Updating version to {version}."   )
						global_version_update(_UpperCAmelCase   )
						print("Cleaning main README, don't forget to run `make fix-copies`."   )
						clean_main_ref_in_model_list()


if __name__ == "__main__":
					lowerCAmelCase							:  str            =						argparse.ArgumentParser()
					parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
					parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
					lowerCAmelCase							:  int            =						parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("""Nothing to do after a patch :-)""")
					else:
										post_release_work()









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					StableDiffusionInpaintPipeline
			_UpperCAmelCase       :							Tuple      							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase       :							List[str]      							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase       :							Tuple      							=					frozenset(
			    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase       :							Optional[int]      							=					frozenset([]     )



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=9       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						attention_head_dim=(2, 4)       ,						use_linear_projection=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoencoderKL(
									    block_out_channels=[32, 64]       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,						latent_channels=4       ,						sample_size=128       ,						)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextConfig(
									    bos_token_id=0       ,						eos_token_id=2       ,						hidden_size=32       ,						intermediate_size=37       ,						layer_norm_eps=1E-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=1000       ,						hidden_act="gelu"       ,						projection_dim=512       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_:			Dict  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": init_image,
									    "mask_image": mask_image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			int  		=	StableDiffusionInpaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Dict  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			str  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 9E-3



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Any  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_:			str  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Dict  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						safety_checker=lowerCAmelCase__       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 5E-1




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_:			Tuple  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PNDMScheduler.from_pretrained(lowerCAmelCase__       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_:			Any  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=2       ,						output_type="np"       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






