






'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = StableDiffusionPanoramaPipeline
							a          = TEXT_TO_IMAGE_PARAMS
							a          = TEXT_TO_IMAGE_BATCH_PARAMS
							a          = TEXT_TO_IMAGE_IMAGE_PARAMS
							a          = TEXT_TO_IMAGE_IMAGE_PARAMS




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														torch.manual_seed(0  )
														lowerCamelCase__      :			Any 						= UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,							layers_per_block=1    ,							sample_size=32    ,							in_channels=4    ,							out_channels=4    ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,							cross_attention_dim=32    ,							)
														lowerCamelCase__      :			Dict 						= DDIMScheduler()
														torch.manual_seed(0  )
														lowerCamelCase__      :			int 						= 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    ,							)
														torch.manual_seed(0  )
														lowerCamelCase__      :			Any 						= 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=1_000    ,							)
														lowerCamelCase__      :			Any 						= CLIPTextModel(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

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




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]=0  ):
														lowerCamelCase__      :			List[str] 						= torch.manual_seed(UpperCamelCase__  )
														lowerCamelCase__      :			str 						= {
														    """prompt""": """a photo of the dolomites""",
														    """generator""": generator,
														    # Setting height and width to None to prevent OOMs on CPU.
														    """height""": None,
														    """width""": None,
														    """num_inference_steps""": 1,
														    """guidance_scale""": 6.0,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			int 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			str 						= self.get_dummy_components()
														lowerCamelCase__      :			str 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			int 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			str 						= image[0, -3:, -3:, -1]
														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Tuple 						= np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]  )

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




							def 	lowerCamelCase_       (							self:       Any  ):
														super().test_inference_batch_consistent(batch_sizes=[1, 2]  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														super().test_inference_batch_single_identical(batch_size=2    ,							expected_max_diff=3.25e-3  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Union[str, Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Optional[Any] 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= """french fries"""
														lowerCamelCase__      :			Any 						= sd_pipe(**UpperCamelCase__    ,							negative_prompt=UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= output.images
														lowerCamelCase__      :			Optional[Any] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Optional[Any] 						= np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]  )

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




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			List[Any] 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= sd_pipe(**UpperCamelCase__    ,							view_batch_size=2  )
														lowerCamelCase__      :			Optional[Any] 						= output.images
														lowerCamelCase__      :			str 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Any 						= np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]  )

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




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Tuple 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Dict 						= self.get_dummy_components()
														lowerCamelCase__      :			Union[str, Any] 						= EulerAncestralDiscreteScheduler(
														    beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule="""scaled_linear"""  )
														lowerCamelCase__      :			Dict 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Optional[int] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			Dict 						= np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]  )

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




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__      :			Dict 						= self.get_dummy_components()
														lowerCamelCase__      :			Optional[Any] 						= PNDMScheduler(
														    beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule="""scaled_linear"""    ,							skip_prk_steps=UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= StableDiffusionPanoramaPipeline(**UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= sd_pipe.to(UpperCamelCase__  )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= self.get_dummy_inputs(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= sd_pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Optional[int] 						= image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														lowerCamelCase__      :			str 						= np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]  )

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



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




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Any=0  ):
														lowerCamelCase__      :			List[str] 						= torch.manual_seed(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= {
														    """prompt""": """a photo of the dolomites""",
														    """generator""": generator,
														    """num_inference_steps""": 3,
														    """guidance_scale""": 7.5,
														    """output_type""": """numpy""",
														}
														return inputs




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			str 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			List[Any] 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Union[str, Any] 						= self.get_inputs()
														lowerCamelCase__      :			List[Any] 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Dict 						= image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2_048, 3)

														lowerCamelCase__      :			List[Any] 						= np.array(
														    [
														        0.36_968_392,
														        0.27_025_372,
														        0.32_446_766,
														        0.28_379_387,
														        0.36_363_274,
														        0.30_733_347,
														        0.27_100_027,
														        0.27_054_125,
														        0.25_536_096,
														    ]  )

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




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2-base"""    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			int 						= LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
														pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Union[str, Any] 						= self.get_inputs()
														lowerCamelCase__      :			Optional[Any] 						= pipe(**UpperCamelCase__  ).images
														lowerCamelCase__      :			Any 						= image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2_048, 3)

														lowerCamelCase__      :			str 						= np.array(
														    [
														        [
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														            0.0,
														        ]
														    ]  )

														assert np.abs(expected_slice - image_slice  ).max() < 1e-3




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Union[str, Any] 						= 0

														def callback_fn(UpperCamelCase__:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       torch.FloatTensor  ) -> None:
																					lowerCamelCase__      :			int 						= True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												lowerCamelCase__      :			Any 						= latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												lowerCamelCase__      :			str 						= latents[0, -3:, -3:, -1]

																												lowerCamelCase__      :			Union[str, Any] 						= np.array(
																												    [
																												        0.18_681_869,
																												        0.33_907_816,
																												        0.5_361_276,
																												        0.14_432_865,
																												        -0.02_856_611,
																												        -0.73_941_123,
																												        0.23_397_987,
																												        0.47_322_682,
																												        -0.37_823_164,
																												    ]  )
																												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2
																					elif step == 2:
																												lowerCamelCase__      :			List[Any] 						= latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												lowerCamelCase__      :			Tuple 						= latents[0, -3:, -3:, -1]

																												lowerCamelCase__      :			int 						= np.array(
																												    [
																												        0.18_539_645,
																												        0.33_987_248,
																												        0.5_378_559,
																												        0.14_437_142,
																												        -0.02_455_261,
																												        -0.7_338_317,
																												        0.23_990_755,
																												        0.47_356_272,
																												        -0.3_786_505,
																												    ]  )

																												assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2

														lowerCamelCase__      :			Optional[Any] 						= False

														lowerCamelCase__      :			Any 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			List[Any] 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing()

														lowerCamelCase__      :			Any 						= self.get_inputs()
														pipe(**UpperCamelCase__    ,							callback=UpperCamelCase__    ,							callback_steps=1  )
														assert callback_fn.has_been_called
														assert number_of_steps == 3




							def 	lowerCamelCase_       (							self:       Any  ):
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														lowerCamelCase__      :			str 						= """stabilityai/stable-diffusion-2-base"""
														lowerCamelCase__      :			Optional[Any] 						= DDIMScheduler.from_pretrained(UpperCamelCase__    ,							subfolder="""scheduler"""  )
														lowerCamelCase__      :			Any 						= StableDiffusionPanoramaPipeline.from_pretrained(UpperCamelCase__    ,							scheduler=UpperCamelCase__    ,							safety_checker=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= pipe.to(UpperCamelCase__  )
														pipe.set_progress_bar_config(disable=UpperCamelCase__  )
														pipe.enable_attention_slicing(1  )
														pipe.enable_sequential_cpu_offload()

														lowerCamelCase__      :			int 						= self.get_inputs()
														lowerCamelCase__      :			Optional[Any] 						= pipe(**UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= torch.cuda.max_memory_allocated()
														# make sure that less than 5.2 GB is allocated
														assert mem_bytes < 5.5 * 10**9









'''simple docstring'''







import argparse
import os
import re

import packaging.version


_A			:    List[str]					  ='''examples/'''
_A			:    Any					  ={
    '''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			:    int					  ={
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
_A			:    int					  ='''README.md'''





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> List[Any]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
							if not patch:
														update_version_in_examples(UpperCamelCase   )





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

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

							lowerCamelCase__      :			Optional[Any] 						= start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt   ):
														if lines[index].startswith("""1."""   ):
																					lowerCamelCase__      :			List[Any] 						= 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 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							with open(REPLACE_FILES["""init"""]       ,    """r"""   ) as f:
														lowerCamelCase__      :			int 						= f.read()
							lowerCamelCase__      :			Optional[Any] 						= REPLACE_PATTERNS["""init"""][0].search(UpperCamelCase   ).groups()[0]
							return packaging.version.parse(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase=False   )   -> List[Any]:
							lowerCamelCase__      :			Union[str, 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:
														lowerCamelCase__      :			List[str] 						= default_version.base_version
							elif patch:
														lowerCamelCase__      :			Any 						= f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
							else:
														lowerCamelCase__      :			List[Any] 						= f'''{default_version.major}.{default_version.minor + 1}.0'''

							# Now let's ask nicely if that's the right one.
							lowerCamelCase__      :			Any 						= input(f'''Which version are you releasing? [{default_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Optional[int] 						= 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 				SCREAMING_SNAKE_CASE_ ()   -> List[str]:
							lowerCamelCase__      :			Optional[int] 						= get_version()
							lowerCamelCase__      :			Any 						= f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
							lowerCamelCase__      :			Any 						= current_version.base_version

							# Check with the user we got that right.
							lowerCamelCase__      :			List[Any] 						= input(f'''Which version are we developing now? [{dev_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Dict 						= 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__":
			_A			:    List[Any]					  =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			:    List[str]					  =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()

