


'''simple docstring'''

import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


lowerCAmelCase      :int    	=			logging.get_logger(__name__)





def lowerCamelCase			(			lowerCAmelCase				:	int		,		lowerCAmelCase				:	List[Any]		):





	"""simple docstring"""







	__magic_name__       :    str     	=     set()
	__magic_name__       :    Tuple     	=     []

	def parse_line(lowerCAmelCase				:	Tuple		):
		for line in fp:
			if isinstance(UpperCamelCase__		,		UpperCamelCase__		):
				__magic_name__       :    Tuple     	=     line.decode('UTF-8'		)
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(' '		):
				# process a single warning and move it to `selected_warnings`.
				if len(UpperCamelCase__		) > 0:
					__magic_name__       :    Any     	=     '''\n'''.join(UpperCamelCase__		)
					# Only keep the warnings specified in `targets`
					if any(f': {x}: ' in warning for x in targets		):
						selected_warnings.add(UpperCamelCase__		)
					buffer.clear()
				continue
			else:
				__magic_name__       :    int     	=     line.strip()
				buffer.append(UpperCamelCase__		)

	if from_gh:
		for filename in os.listdir(UpperCamelCase__		):
			__magic_name__       :    int     	=     os.path.join(UpperCamelCase__		,		UpperCamelCase__		)
			if not os.path.isdir(UpperCamelCase__		):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(UpperCamelCase__		) as fp:
					parse_line(UpperCamelCase__		)
	else:
		try:
			with zipfile.ZipFile(UpperCamelCase__		) as z:
				for filename in z.namelist():
					if not os.path.isdir(UpperCamelCase__		):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(UpperCamelCase__		) as fp:
							parse_line(UpperCamelCase__		)
		except Exception:
			logger.warning(
			    f'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'		)

	return selected_warnings





def lowerCamelCase			(			lowerCAmelCase				:	Union[str, Any]		,		lowerCAmelCase				:	Optional[int]		):





	"""simple docstring"""







	__magic_name__       :    str     	=     set()

	__magic_name__       :    Optional[int]     	=     [os.path.join(UpperCamelCase__		,		UpperCamelCase__		) for p in os.listdir(UpperCamelCase__		) if (p.endswith('.zip'		) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(UpperCamelCase__		,		UpperCamelCase__		)		)

	return selected_warnings


if __name__ == "__main__":





							def lowerCamelCase			(			lowerCAmelCase				:	Optional[Any]		):





								"""simple docstring"""







								return values.split(','		)

							lowerCAmelCase      :Dict    	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
							parser.add_argument(
							    '''--output_dir''',
							    type=str,
							    required=True,
							    help='''Where to store the downloaded artifacts and other result files.''',
							)
							parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
							# optional parameters
							parser.add_argument(
							    '''--targets''',
							    default='''DeprecationWarning,UserWarning,FutureWarning''',
							    type=list_str,
							    help='''Comma-separated list of target warning(s) which we want to extract.''',
							)
							parser.add_argument(
							    '''--from_gh''',
							    action='''store_true''',
							    help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
							)

							lowerCAmelCase      :List[Any]    	=			parser.parse_args()

							lowerCAmelCase      :List[str]    	=			args.from_gh
							if from_gh:
														# The artifacts have to be downloaded using `actions/download-artifact@v3`
														pass
							else:
														os.makedirs(args.output_dir, exist_ok=True)

														# get download links
														lowerCAmelCase      :Union[str, Any]    	=			get_artifacts_links(args.workflow_run_id, token=args.token)
														with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
																					json.dump(artifacts, fp, ensure_ascii=False, indent=4)

														# download artifacts
														for idx, (name, url) in enumerate(artifacts.items()):
																					print(name)
																					print(url)
																					print('''=''' * 8_0)
																					download_artifact(name, url, args.output_dir, args.token)
																					# Be gentle to GitHub
																					time.sleep(1)

														# extract warnings from artifacts
							lowerCAmelCase      :Tuple    	=			extract_warnings(args.output_dir, args.targets)
							lowerCAmelCase      :Tuple    	=			sorted(selected_warnings)
							with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
														json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)




"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


