



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

import packaging.version


__a      =				"examples/"
__a      =				{
    "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      =				{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__a      =				"README.md"
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


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







						'''simple docstring'''


						for folder, directories, fnames in os.walk(_lowercase	):
												# 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(_lowercase,					_lowercase	),					_lowercase,					pattern="""examples"""	)
def    A_  (			_lowercase,					_lowercase=False	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :Any						=			"""🤗 Transformers currently provides the following architectures"""
						snake_case_ :str						=			"""1. Want to contribute a new model?"""
						with open(_lowercase,					"""r""",					encoding="""utf-8""",					newline="""\n"""	) as f:
												snake_case_ :Union[str, Any]						=			f.readlines()

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

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

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







						'''simple docstring'''


						with open(REPLACE_FILES["""init"""],					"""r"""	) as f:
												snake_case_ :str						=			f.read()
						snake_case_ :str						=			REPLACE_PATTERNS["""init"""][0].search(_lowercase	).groups()[0]
						return packaging.version.parse(_lowercase	)
def    A_  (			_lowercase=False	):







						'''simple docstring'''


						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:
												snake_case_ :Optional[int]						=			default_version.base_version
						elif patch:
												snake_case_ :List[str]						=			f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
						else:
												snake_case_ :Union[str, Any]						=			f"""{default_version.major}.{default_version.minor + 1}.0"""

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

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







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			get_version()
						snake_case_ :str						=			f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
						snake_case_ :List[str]						=			current_version.base_version

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

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


if __name__ == "__main__":
						__a      =				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      =				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 unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			str						=     StableDiffusionSAGPipeline
					_A		:			Optional[Any]						=     TEXT_TO_IMAGE_PARAMS
					_A		:			Any						=     TEXT_TO_IMAGE_BATCH_PARAMS
					_A		:			Tuple						=     TEXT_TO_IMAGE_IMAGE_PARAMS
					_A		:			Tuple						=     TEXT_TO_IMAGE_IMAGE_PARAMS
					_A		:			List[str]						=     False






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	str:
											torch.manual_seed(0						)
											snake_case_ :Any						=			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_ :Any						=			DDIMScheduler(
											    beta_start=0.0_0_0_8_5						,     beta_end=0.0_1_2						,     beta_schedule="""scaled_linear"""						,     clip_sample=snake_case						,     set_alpha_to_one=snake_case						,     )
											torch.manual_seed(0						)
											snake_case_ :Optional[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						)
											snake_case_ :Union[str, 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						,     )
											snake_case_ :Tuple						=			CLIPTextModel(snake_case						)
											snake_case_ :str						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

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






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Tuple						,     snake_case:							List[str]=0						)   ->	str:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_ :Tuple						=			torch.manual_seed(snake_case						)
											else:
																	snake_case_ :Optional[int]						=			torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_ :Any						=			{
											    """prompt""": """.""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 1.0,
											    """sag_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	str:
											super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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






					'''simple docstring'''





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






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""						)
											snake_case_ :int						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Union[str, Any]						=			"""."""
											snake_case_ :str						=			torch.manual_seed(0						)
											snake_case_ :str						=			sag_pipe(
											    [prompt]						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						)

											snake_case_ :List[Any]						=			output.images

											snake_case_ :Tuple						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											snake_case_ :List[Any]						=			np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2






					def    lowerCAmelCase_				(       self:							Dict						)   ->	str:
											snake_case_ :Tuple						=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""						)
											snake_case_ :Optional[int]						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Tuple						=			"""."""
											snake_case_ :Union[str, Any]						=			torch.manual_seed(0						)
											snake_case_ :Tuple						=			sag_pipe(
											    [prompt]						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						)

											snake_case_ :Optional[int]						=			output.images

											snake_case_ :Optional[Any]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											snake_case_ :Tuple						=			np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											snake_case_ :Optional[int]						=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""						)
											snake_case_ :int						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Tuple						=			"""."""
											snake_case_ :Optional[int]						=			torch.manual_seed(0						)
											snake_case_ :List[str]						=			sag_pipe(
											    [prompt]						,     width=768						,     height=512						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						,     )

											snake_case_ :Optional[Any]						=			output.images

											assert image.shape == (1, 512, 768, 3)
