




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__UpperCamelCase   :			Optional[Any]          = False

@skip_mps
class 						__lowerCAmelCase      (      SCREAMING_SNAKE_CASE__ ,			SCREAMING_SNAKE_CASE__ ,			SCREAMING_SNAKE_CASE__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    StableDiffusionAttendAndExcitePipeline
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__   	=    TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}					)
							UpperCamelCase__   	=    TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__   	=    TEXT_TO_IMAGE_IMAGE_PARAMS
							@classmethod
							def  lowerCamelCase__				(		cls		:List[str]							):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(_lowercase							)
							@classmethod
							def  lowerCamelCase__				(		cls		:int							):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(_lowercase							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								torch.manual_seed(0							)
								a									=				UNetaDConditionModel(
								    block_out_channels=(32, 64)						, layers_per_block=1						, sample_size=32						, in_channels=4						, out_channels=4						, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						, cross_attention_dim=32						, attention_head_dim=(2, 4)						, use_linear_projection=_lowercase						, )
								a									=				DDIMScheduler(
								    beta_start=0.00085						, beta_end=0.012						, beta_schedule="""scaled_linear"""						, clip_sample=_lowercase						, set_alpha_to_one=_lowercase						, )
								torch.manual_seed(0							)
								a									=				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						, sample_size=128						, )
								torch.manual_seed(0							)
								a									=				CLIPTextConfig(
								    bos_token_id=0						, eos_token_id=2						, hidden_size=32						, intermediate_size=37						, layer_norm_eps=1E-0_5						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=1000						, hidden_act="""gelu"""						, projection_dim=512						, )
								a									=				CLIPTextModel(_lowercase							)
								a									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

								a									=				{
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}

								return components
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Any						, __magic_name__		:Any=0							):







								'''simple docstring'''







								if str(_lowercase							).startswith("""mps"""							):
									a									=				torch.manual_seed(_lowercase							)
								else:
									a									=				torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
								a									=				{
								    "prompt": "a cat and a frog",
								    "token_indices": [2, 5],
								    "generator": generator,
								    "num_inference_steps": 1,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								    "max_iter_to_alter": 2,
								    "thresholds": {0: 0.7},
								}
								return inputs
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				"cpu"

								a									=				self.get_dummy_components()
								a									=				self.pipeline_class(**_lowercase							)
								pipe.to(_lowercase							)
								pipe.set_progress_bar_config(disable=_lowercase							)

								a									=				self.get_dummy_inputs(_lowercase							)
								a									=				pipe(**_lowercase							).images
								a									=				image[0, -3:, -3:, -1]

								self.assertEqual(image.shape						, (1, 64, 64, 3)							)
								a									=				np.array(
								    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]							)
								a									=				np.abs(image_slice.flatten() - expected_slice							).max()
								self.assertLessEqual(_lowercase						, 1E-3							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=5E-4							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								self._test_inference_batch_single_identical(batch_size=2						, expected_max_diff=7E-4							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=5E-4							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=4E-4							)







@require_torch_gpu
@slow
class 						__lowerCAmelCase      (      unittest.TestCase					):
							@classmethod
							def  lowerCamelCase__				(		cls		:Any							):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(_lowercase							)
							@classmethod
							def  lowerCamelCase__				(		cls		:Optional[Any]							):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(_lowercase							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







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





							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				torch.manual_seed(51							)

								a									=				StableDiffusionAttendAndExcitePipeline.from_pretrained(
								    """CompVis/stable-diffusion-v1-4"""						, safety_checker=_lowercase						, torch_dtype=torch.floataa							)
								pipe.to("""cuda"""							)

								a									=				"a painting of an elephant with glasses"
								a									=				[5, 7]

								a									=				pipe(
								    prompt=_lowercase						, token_indices=_lowercase						, guidance_scale=7.5						, generator=_lowercase						, num_inference_steps=5						, max_iter_to_alter=5						, output_type="""numpy"""						, ).images[0]

								a									=				load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""							)
								assert np.abs((expected_image - image).max()							) < 5E-1






"""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


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[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(lowerCamelCase_ ) > 0:
																														snake_case   :			str						    = "\n".join(lowerCamelCase_ )
																														# Only keep the warnings specified in `targets`
																														if any(f''': {x}: ''' in warning for x in targets ):
																																				selected_warnings.add(lowerCamelCase_ )
																														buffer.clear()
																								continue
																		else:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )
																		if not os.path.isdir(lowerCamelCase_ ):
																								# read the file
																								if filename != "warnings.txt":
																														continue
																								with open(lowerCamelCase_ ) as fp:
																														parse_line(lowerCamelCase_ )
						else:
												try:
																		with zipfile.ZipFile(lowerCamelCase_ ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(lowerCamelCase_ ):
																																				# read the file
																																				if filename != "warnings.txt":
																																										continue
																																				with z.open(lowerCamelCase_ ) as fp:
																																										parse_line(lowerCamelCase_ )
												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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			str						    = [os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) for p in os.listdir(lowerCamelCase_ ) if (p.endswith(".zip" ) or from_gh)]
						for p in paths:
												selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase_     ,		lowerCamelCase_ ) )

						return selected_warnings


if __name__ == "__main__":

			def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			A		=						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.',
			)

			A		=						parser.parse_args()

			A		=						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
						A		=						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
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						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)



