






"""simple docstring"""



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__	:    List[str]			 = 'src/diffusers'

# Matches is_xxx_available()
A__	:    Dict			 = re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
A__	:    str			 = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


A__	:    List[str]			 = '\n{0} = None\n'

A__	:    str			 = '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


A__	:    Optional[Any]			 = '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'

def 			_snake_case    (					lowerCamelCase__     :   List[str]  )    ->     Tuple:
							lowerCamelCase_	:							Any									=_re_backend.findall(lowerCamelCase__  )
							if len(lowerCamelCase__  ) == 0:
														return None

							return "_and_".join(lowerCamelCase__  )

def 			_snake_case    (					)    ->     str:
							with open(os.path.join(lowerCamelCase__    ,						"__init__.py"  )    ,						"r"    ,						encoding="utf-8"    ,						newline="\n"  ) as f:
														lowerCamelCase_	:							Optional[Any]									=f.readlines()

							# Get to the point we do the actual imports for type checking
							lowerCamelCase_	:							List[str]									=0
							lowerCamelCase_	:							Optional[Any]									={}
							# Go through the end of the file
							while line_index < len(lowerCamelCase__  ):
														# If the line contains is_backend_available, we grab all objects associated with the `else` block
														lowerCamelCase_	:							Dict									=find_backend(lines[line_index]  )
														if backend is not None:
																					while not lines[line_index].startswith("else:"  ):
																												line_index += 1
																					line_index += 1
																					lowerCamelCase_	:							Dict									=[]
																					# Until we unindent, add backend objects to the list
																					while line_index < len(lowerCamelCase__  ) and len(lines[line_index]  ) > 1:
																												lowerCamelCase_	:							Tuple									=lines[line_index]
																												lowerCamelCase_	:							List[Any]									=_re_single_line_import.search(lowerCamelCase__  )
																												if single_line_import_search is not None:
																																			objects.extend(single_line_import_search.groups()[0].split(", "  )  )
																												elif line.startswith(" " * 8  ):
																																			objects.append(line[8:-2]  )
																												line_index += 1

																					if len(lowerCamelCase__  ) > 0:
																												lowerCamelCase_	:							Dict									=objects
														else:
																					line_index += 1

							return backend_specific_objects

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Dict  )    ->     int:
							if name.isupper():
														return DUMMY_CONSTANT.format(lowerCamelCase__  )
							elif name.islower():
														return DUMMY_FUNCTION.format(lowerCamelCase__    ,						lowerCamelCase__  )
							else:
														return DUMMY_CLASS.format(lowerCamelCase__    ,						lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   Dict=None  )    ->     List[str]:
							if backend_specific_objects is None:
														lowerCamelCase_	:							Optional[int]									=read_init()
							# For special correspondence backend to module name as used in the function requires_modulename
							lowerCamelCase_	:							Optional[int]									={}

							for backend, objects in backend_specific_objects.items():
														lowerCamelCase_	:							Tuple									="[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"  )  ) + "]"
														lowerCamelCase_	:							str									="# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
														dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
														dummy_file += "\n".join([create_dummy_object(lowerCamelCase__    ,						lowerCamelCase__  ) for o in objects]  )
														lowerCamelCase_	:							Any									=dummy_file

							return dummy_files

def 			_snake_case    (					lowerCamelCase__     :   int=False  )    ->     List[str]:
							lowerCamelCase_	:							int									=create_dummy_files()
							# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
							lowerCamelCase_	:							Tuple									={"torch": "pt"}

							# Locate actual dummy modules and read their content.
							lowerCamelCase_	:							List[Any]									=os.path.join(lowerCamelCase__    ,						"utils"  )
							lowerCamelCase_	:							Dict									={
							    backend: os.path.join(lowerCamelCase__    ,						F"""dummy_{short_names.get(lowerCamelCase__    ,						lowerCamelCase__  )}_objects.py"""  )
							    for backend in dummy_files.keys()
							}

							lowerCamelCase_	:							Union[str, Any]									={}
							for backend, file_path in dummy_file_paths.items():
														if os.path.isfile(lowerCamelCase__  ):
																					with open(lowerCamelCase__    ,						"r"    ,						encoding="utf-8"    ,						newline="\n"  ) as f:
																												lowerCamelCase_	:							List[Any]									=f.read()
														else:
																					lowerCamelCase_	:							List[Any]									=""

							for backend in dummy_files.keys():
														if dummy_files[backend] != actual_dummies[backend]:
																					if overwrite:
																												print(
																												    F"""Updating diffusers.utils.dummy_{short_names.get(lowerCamelCase__    ,						lowerCamelCase__  )}_objects.py as the main """
																												    "__init__ has new objects."  )
																												with open(dummy_file_paths[backend]    ,						"w"    ,						encoding="utf-8"    ,						newline="\n"  ) as f:
																																			f.write(dummy_files[backend]  )
																					else:
																												raise ValueError(
																												    "The main __init__ has objects that are not present in "
																												    F"""diffusers.utils.dummy_{short_names.get(lowerCamelCase__    ,						lowerCamelCase__  )}_objects.py. Run `make fix-copies` """
																												    "to fix this."  )


if __name__ == "__main__":
						A__	:    Optional[int]			 = argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						A__	:    Union[str, Any]			 = parser.parse_args()

						check_dummies(args.fix_and_overwrite)











"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        lowercase__      (							unittest.TestCase       ):


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


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=1
										lowerCamelCase_	:							Union[str, Any]									=3
										lowerCamelCase_	:							Dict									=(32, 32)

										lowerCamelCase_	:							List[Any]									=floats_tensor((batch_size, num_channels) + sizes       ,	rng=random.Random(0 ) ).to(snake_case__ )
										return image


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Dict									=UNetaDConditionModel(
										    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,	cross_attention_dim=32       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							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       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Any									=RobertaSeriesConfig(
										    hidden_size=32       ,	project_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=5006       ,	)
										return RobertaSeriesModelWithTransformation(snake_case__ )


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										def extract(*snake_case__					:  Dict       ,	**snake_case__					:  int ):







																	class        lowercase__      :


																				def __init__(				self					:  Optional[Any] ):
																											lowerCamelCase_	:							Union[str, Any]									=torch.ones([0] )


																				def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Any ):
																											self.pixel_values.to(snake_case__ )
																											return self


																	return Out()

										return extract


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Dict									="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							List[str]									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							List[Any]									=self.dummy_vae
										lowerCamelCase_	:							Any									=self.dummy_text_encoder
										lowerCamelCase_	:							List[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							List[str]									=77

										lowerCamelCase_	:							Optional[int]									=self.dummy_image.to(snake_case__ )
										lowerCamelCase_	:							Any									=init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							List[Any]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							int									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							Optional[int]									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Any									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Union[str, Any]									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							str									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	)

										lowerCamelCase_	:							List[Any]									=output.images

										lowerCamelCase_	:							str									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	return_dict=snake_case__       ,	)[0]

										lowerCamelCase_	:							Tuple									=image[0, -3:, -3:, -1]
										lowerCamelCase_	:							Union[str, Any]									=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_	:							List[str]									=np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							Any									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							Tuple									=self.dummy_vae
										lowerCamelCase_	:							Union[str, Any]									=self.dummy_text_encoder
										lowerCamelCase_	:							Optional[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							Tuple									=77

										lowerCamelCase_	:							str									=self.dummy_image.to(snake_case__ )

										# put models in fp16
										lowerCamelCase_	:							Optional[Any]									=unet.half()
										lowerCamelCase_	:							Dict									=vae.half()
										lowerCamelCase_	:							str									=bert.half()

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							Optional[int]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							List[Any]									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							int									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Tuple									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Tuple									=torch.manual_seed(0 )
										lowerCamelCase_	:							List[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	).images

										assert image.shape == (1, 32, 32, 3)


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							int									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowerCamelCase_	:							Any									=init_image.resize((760, 504) )

										lowerCamelCase_	:							List[str]									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[int]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							List[Any]									=torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[int]									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							Optional[int]									=output.images[0]

										lowerCamelCase_	:							Tuple									=image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										lowerCamelCase_	:							Optional[int]									=np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] )

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







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


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


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										lowerCamelCase_	:							Any									=init_image.resize((768, 512) )
										lowerCamelCase_	:							Optional[int]									=load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )

										lowerCamelCase_	:							Dict									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[Any]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							Dict									=torch.manual_seed(0 )
										lowerCamelCase_	:							int									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							int									=output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image ).max() < 1E-2



