
import argparse
import os
import re

import packaging.version


_UpperCAmelCase					: Any      =       "examples/"
_UpperCAmelCase					: Tuple      =       {
    "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"),
}
_UpperCAmelCase					: List[Any]      =       {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
_UpperCAmelCase					: Optional[Any]      =       "README.md"


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

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


def       A       (					lowercase )   ->  Any:
		'''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 )   ->  str:
		'''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       (					)   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     '🤗 Transformers currently provides the following architectures'
		UpperCamelCase		=     '1. Want to contribute a new model?'
		with open(lowercase    ,       '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/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       (					)   ->  List[Any]:
		'''simple docstring'''

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


def       A       (					lowercase=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(lowercase ) == 0:
				UpperCamelCase		=     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       (					)   ->  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(lowercase ) == 0:
				UpperCamelCase		=     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__":
					_UpperCAmelCase					: Optional[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.")
					_UpperCAmelCase					: 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()


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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