


"""simple docstring"""
import argparse
import os
import re

import packaging.version


__SCREAMING_SNAKE_CASE							:      Dict						=      'examples/'
__SCREAMING_SNAKE_CASE							:      Optional[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'),
}
__SCREAMING_SNAKE_CASE							:      str						=      {
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
__SCREAMING_SNAKE_CASE							:      str						=      'README.md'







def        _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							with open(snake_case__			,	"""r"""			,	encoding="""utf-8"""			,	newline="""\n"""			) as f:
														snake_case_          =						f.read()
							snake_case_    ,  snake_case_          =						REPLACE_PATTERNS[pattern]
							snake_case_          =						replace.replace("""VERSION"""			,	snake_case__			)
							snake_case_          =						re_pattern.sub(snake_case__			,	snake_case__			)
							with open(snake_case__			,	"""w"""			,	encoding="""utf-8"""			,	newline="""\n"""			) as f:
														f.write(snake_case__			)







def        _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							for folder, directories, fnames in os.walk(snake_case__			):
														# 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(snake_case__			,	snake_case__			)			,	snake_case__			,	pattern="""examples"""			)







def        _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			Optional[Any]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(snake_case__			,	snake_case__			,	snake_case__			)
							if not patch:
														update_version_in_examples(snake_case__			)







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

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

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

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







def        _a			( )      ->			List[str]:
							with open(REPLACE_FILES["""init"""]			,	"""r"""			) as f:
														snake_case_          =						f.read()
							snake_case_          =						REPLACE_PATTERNS["""init"""][0].search(snake_case__			).groups()[0]
							return packaging.version.parse(snake_case__			)







def        _a			( _SCREAMING_SNAKE_CASE=False			)      ->			Optional[Any]:
							snake_case_          =						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:
														snake_case_          =						default_version.base_version
							elif patch:
														snake_case_          =						f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														snake_case_          =						f"""{default_version.major}.{default_version.minor + 1}.0"""

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

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







def        _a			( )      ->			str:
							snake_case_          =						get_version()
							snake_case_          =						f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							snake_case_          =						current_version.base_version

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      int						=      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.')
				__SCREAMING_SNAKE_CASE							:      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()







"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      CycleDiffusionPipeline
							__lowercase:						int							 =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """negative_prompt""",
							    """height""",
							    """width""",
							    """negative_prompt_embeds""",
							}
							__lowercase:						str							 =      PipelineTesterMixin.required_optional_params - {"""latents"""}
							__lowercase:						Any							 =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS



							def 						lowerCAmelCase    (			self							: Dict    )   ->Any:



														"""simple docstring"""



														torch.manual_seed(0    )
														snake_case_          =						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						,    )
														snake_case_          =						DDIMScheduler(
														    beta_start=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    num_train_timesteps=1_000						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=UpperCAmelCase_						,    )
														torch.manual_seed(0    )
														snake_case_          =						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    )
														snake_case_          =						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						,    )
														snake_case_          =						CLIPTextModel(UpperCAmelCase_    )
														snake_case_          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[str]=0    )   ->str:



														"""simple docstring"""



														snake_case_          =						floats_tensor((1, 3, 32, 32)						,    rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
														snake_case_          =						image / 2 + 0.5
														if str(UpperCAmelCase_    ).startswith("""mps"""    ):
																					snake_case_          =						torch.manual_seed(UpperCAmelCase_    )
														else:
																					snake_case_          =						torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
														snake_case_          =						{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														snake_case_          =						self.get_dummy_components()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

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



							@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(UpperCAmelCase_						,    """half"""    ):
																												snake_case_          =						module.half()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

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



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_save_load_local()



							@unittest.skip("""non-deterministic pipeline"""    )
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_inference_batch_single_identical()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Any    )   ->Tuple:



														"""simple docstring"""



														return super().test_save_load_optional_components()



							@skip_mps
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->str:



														"""simple docstring"""



														return super().test_attention_slicing_forward_pass()





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





							'''simple docstring'''

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



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(
														    UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_						,    torch_dtype=torch.floataa						,    revision="""fp16"""    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image    ).max() < 5E-1



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														assert np.abs(image - expected_image    ).max() < 2E-2



