
"""simple docstring"""



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 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Dict     =				StableDiffusionInpaintPipeline
    a      :				Any     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    a      :				List[Any]     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a      :				Optional[Any]     =				frozenset(
        []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a      :				List[str]     =				frozenset([]	)







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






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   PNDMScheduler(skip_prk_steps=__UpperCamelCase     )
        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=1_28     ,					)
        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=10_00     ,					hidden_act="""gelu"""     ,					projection_dim=5_12     ,					)
        _UpperCAmelCase          =   CLIPTextModel(__UpperCamelCase     )
        _UpperCAmelCase          =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

        _UpperCAmelCase          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]=0     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   floats_tensor((1, 3, 32, 32)     ,					rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   image.cpu().permute(0     ,					2     ,					3     ,					1     )[0]
        _UpperCAmelCase          =   Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((64, 64)     )
        _UpperCAmelCase          =   Image.fromarray(np.uinta(image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )
        if str(__UpperCamelCase     ).startswith("""mps"""     ):
            _UpperCAmelCase          =   torch.manual_seed(__UpperCamelCase     )
        else:
            _UpperCAmelCase          =   torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
        _UpperCAmelCase          =   {
            """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      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase          =   self.get_dummy_components()
        _UpperCAmelCase          =   StableDiffusionInpaintPipeline(**__UpperCamelCase     )
        _UpperCAmelCase          =   sd_pipe.to(__UpperCamelCase     )
        sd_pipe.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   self.get_dummy_inputs(__UpperCamelCase     )
        _UpperCAmelCase          =   sd_pipe(**__UpperCamelCase     ).images
        _UpperCAmelCase          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]     )

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







    def      _snake_case					(			self			:       str     )		->List[Any]:






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







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







    def      _snake_case					(			self			:       Tuple     )		->Optional[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-inpaint/init_image.png"""     )
        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""     )
        _UpperCAmelCase          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
            """/yellow_cat_sitting_on_a_park_bench.npy"""     )

        _UpperCAmelCase          =   """stabilityai/stable-diffusion-2-inpainting"""
        _UpperCAmelCase          =   StableDiffusionInpaintPipeline.from_pretrained(__UpperCamelCase     ,					safety_checker=__UpperCamelCase     )
        pipe.to(__UpperCamelCase     )
        pipe.set_progress_bar_config(disable=__UpperCamelCase     )
        pipe.enable_attention_slicing()

        _UpperCAmelCase          =   """Face of a yellow cat, high resolution, sitting on a park bench"""

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   pipe(
            prompt=__UpperCamelCase     ,					image=__UpperCamelCase     ,					mask_image=__UpperCamelCase     ,					generator=__UpperCamelCase     ,					output_type="""np"""     ,					)
        _UpperCAmelCase          =   output.images[0]

        assert image.shape == (5_12, 5_12, 3)
        assert np.abs(expected_image - image     ).max() < 9e-3







    def      _snake_case					(			self			:       List[Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-inpaint/init_image.png"""     )
        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""     )
        _UpperCAmelCase          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
            """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""     )

        _UpperCAmelCase          =   """stabilityai/stable-diffusion-2-inpainting"""
        _UpperCAmelCase          =   StableDiffusionInpaintPipeline.from_pretrained(
            __UpperCamelCase     ,					torch_dtype=torch.floataa     ,					safety_checker=__UpperCamelCase     ,					)
        pipe.to(__UpperCamelCase     )
        pipe.set_progress_bar_config(disable=__UpperCamelCase     )
        pipe.enable_attention_slicing()

        _UpperCAmelCase          =   """Face of a yellow cat, high resolution, sitting on a park bench"""

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   pipe(
            prompt=__UpperCamelCase     ,					image=__UpperCamelCase     ,					mask_image=__UpperCamelCase     ,					generator=__UpperCamelCase     ,					output_type="""np"""     ,					)
        _UpperCAmelCase          =   output.images[0]

        assert image.shape == (5_12, 5_12, 3)
        assert np.abs(expected_image - image     ).max() < 5e-1







    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-inpaint/init_image.png"""     )
        _UpperCAmelCase          =   load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""     )

        _UpperCAmelCase          =   """stabilityai/stable-diffusion-2-inpainting"""
        _UpperCAmelCase          =   PNDMScheduler.from_pretrained(__UpperCamelCase     ,					subfolder="""scheduler"""     )
        _UpperCAmelCase          =   StableDiffusionInpaintPipeline.from_pretrained(
            __UpperCamelCase     ,					safety_checker=__UpperCamelCase     ,					scheduler=__UpperCamelCase     ,					torch_dtype=torch.floataa     ,					)
        pipe.to(__UpperCamelCase     )
        pipe.set_progress_bar_config(disable=__UpperCamelCase     )
        pipe.enable_attention_slicing(1     )
        pipe.enable_sequential_cpu_offload()

        _UpperCAmelCase          =   """Face of a yellow cat, high resolution, sitting on a park bench"""

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   pipe(
            prompt=__UpperCamelCase     ,					image=__UpperCamelCase     ,					mask_image=__UpperCamelCase     ,					generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""np"""     ,					)

        _UpperCAmelCase          =   torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.6_5 * 10**9

"""simple docstring"""



import argparse
import os
import re

import packaging.version


a					:	str							=		'''examples/'''
a					:	List[str]							=		{
    '''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'''),
}
a					:	Tuple							=		{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
a					:	List[str]							=		'''README.md'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase    ,_UpperCAmelCase          =   REPLACE_PATTERNS[pattern]
    _UpperCAmelCase          =   replace.replace("""VERSION"""					,      _A      )
    _UpperCAmelCase          =   re_pattern.sub(_A					,      _A      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.write(_A      )


def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    for folder, directories, fnames in os.walk(_A      ):
        # 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(_A					,      _A      )					,      _A					,      pattern="""examples"""      )


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  int:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_A					,      _A					,      _A      )
    if not patch:
        update_version_in_examples(_A      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """🤗 Transformers currently provides the following architectures"""
    _UpperCAmelCase          =   """1. Want to contribute a new model?"""
    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.readlines()

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

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

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


def  _UpperCamelCase	(						)    ->  Optional[int]:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]					,      """r"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase          =   REPLACE_PATTERNS["""init"""][0].search(_A      ).groups()[0]
    return packaging.version.parse(_A      )


def  _UpperCamelCase	(						_A=False      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   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:
        _UpperCAmelCase          =   default_version.base_version
    elif patch:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor + 1}.0"""

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

    print(F"""Updating version to {version}."""      )
    global_version_update(_A					,      patch=_A      )


def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    _UpperCAmelCase          =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _UpperCAmelCase          =   current_version.base_version

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

    print(F"""Updating version to {version}."""      )
    global_version_update(_A      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a					:	Dict							=		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.''')
 a					:	Tuple							=		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()