'''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


UpperCamelCase_      	=	logging.get_logger(__name__)


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  Optional[Any]		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Tuple						    = set()
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = []

							def parse_line(UpperCAmelCase__				:  Any		):
														for line in fp:
																					if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
																												SCREAMING_SNAKE_CASE__     :List[Any]						    = 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:
																																			SCREAMING_SNAKE_CASE__     :Union[str, 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:
																												SCREAMING_SNAKE_CASE__     :List[str]						    = line.strip()
																												buffer.append(UpperCAmelCase__		)

							if from_gh:
														for filename in os.listdir(UpperCAmelCase__		):
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = 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       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  Union[str, Any]		)			->	Tuple:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = set()

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [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       (			UpperCAmelCase__				:  Dict		)			->	Optional[int]:


									'''simple docstring'''






									return values.split(','		)

		UpperCamelCase_      	=	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.''',
		)

		UpperCamelCase_      	=	parser.parse_args()

		UpperCamelCase_      	=	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
				UpperCamelCase_      	=	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('''=''' * 80)
						download_artifact(name, url, args.output_dir, args.token)
						# Be gentle to GitHub
						time.sleep(1)

    # extract warnings from artifacts
		UpperCamelCase_      	=	extract_warnings(args.output_dir, args.targets)
		UpperCamelCase_      	=	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 unittest

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

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

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Any     		= LDMTextToImagePipeline
						A_			:							Dict     		= TEXT_TO_IMAGE_PARAMS - {
						    'negative_prompt',
						    'negative_prompt_embeds',
						    'cross_attention_kwargs',
						    'prompt_embeds',
						}
						A_			:							Optional[Any]     		= PipelineTesterMixin.required_optional_params - {
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						A_			:							Any     		= TEXT_TO_IMAGE_BATCH_PARAMS
						A_			:							Optional[int]     		= False




						def      __lowerCamelCase       (    self :       str      )     ->				List[str]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = 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							,							)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = DDIMScheduler(
													    beta_start=0.0_0085							,							beta_end=0.012							,							beta_schedule='scaled_linear'							,							clip_sample=UpperCamelCase_							,							set_alpha_to_one=UpperCamelCase_							,							)
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = 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      )
													SCREAMING_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=10_00							,							)
													SCREAMING_SNAKE_CASE__     :List[str]						    = CLIPTextModel(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vqvae': vae,
													    'bert': text_encoder,
													    'tokenizer': tokenizer,
													}
													return components




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=0      )     ->				List[Any]:
													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :int						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :Tuple						    = LDMTextToImagePipeline(**UpperCamelCase_      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :int						    = image[0, -3:, -3:, -1]

													assert image.shape == (1, 16, 16, 3)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




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




						def      __lowerCamelCase       (    self :       str      )     ->				Union[str, Any]:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]=torch.floataa							,							UpperCamelCase_ :       int=0      )     ->				str:
													SCREAMING_SNAKE_CASE__     :str						    = torch.manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.random.RandomState(UpperCamelCase_      ).standard_normal((1, 4, 32, 32)      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_							,							dtype=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 2_56, 2_56, 3)
													SCREAMING_SNAKE_CASE__     :str						    = np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.abs(expected_slice - image_slice      ).max()
													assert max_diff < 1e-3




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




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[Any]:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Any=torch.floataa							,							UpperCamelCase_ :       List[str]=0      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.random.RandomState(UpperCamelCase_      ).standard_normal((1, 4, 32, 32)      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_							,							dtype=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 50,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :str						    = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe(**UpperCamelCase_      ).images[0]

													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_numpy(
													    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
													SCREAMING_SNAKE_CASE__     :str						    = np.abs(expected_image - image      ).max()
													assert max_diff < 1e-3






