


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Any=None   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       None
			if token is not None:
						_UpperCAmelCase			=       {'Accept': 'application/vnd.github+json', 'Authorization': F"Bearer {token}"}

			_UpperCAmelCase			=       F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
			_UpperCAmelCase			=       requests.get(_UpperCAmelCase							,       headers=_UpperCAmelCase   ).json()
			_UpperCAmelCase			=       {}

			try:
						job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
						_UpperCAmelCase			=       math.ceil((result['total_count'] - 100) / 100   )

						for i in range(_UpperCAmelCase   ):
									_UpperCAmelCase			=       requests.get(url + F"&page={i + 2}"							,       headers=_UpperCAmelCase   ).json()
									job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )

						return job_links
			except Exception:
						print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"   )

			return {}





def 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Dict=None   )    ->			List[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       None
			if token is not None:
						_UpperCAmelCase			=       {'Accept': 'application/vnd.github+json', 'Authorization': F"Bearer {token}"}

			_UpperCAmelCase			=       F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
			_UpperCAmelCase			=       requests.get(_UpperCAmelCase							,       headers=_UpperCAmelCase   ).json()
			_UpperCAmelCase			=       {}

			try:
						artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
						_UpperCAmelCase			=       math.ceil((result['total_count'] - 100) / 100   )

						for i in range(_UpperCAmelCase   ):
									_UpperCAmelCase			=       requests.get(url + F"&page={i + 2}"							,       headers=_UpperCAmelCase   ).json()
									artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )

						return artifacts
			except Exception:
						print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"   )

			return {}





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       List[str]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       None
			if token is not None:
						_UpperCAmelCase			=       {'Accept': 'application/vnd.github+json', 'Authorization': F"Bearer {token}"}

			_UpperCAmelCase			=       requests.get(_UpperCAmelCase							,       headers=_UpperCAmelCase							,       allow_redirects=_UpperCAmelCase   )
			_UpperCAmelCase			=       result.headers['Location']
			_UpperCAmelCase			=       requests.get(_UpperCAmelCase							,       allow_redirects=_UpperCAmelCase   )
			_UpperCAmelCase			=       os.path.join(_UpperCAmelCase							,       F"{artifact_name}.zip"   )
			with open(_UpperCAmelCase							,       'wb'   ) as fp:
						fp.write(response.content   )





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[Any]=None   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       None

			with zipfile.ZipFile(_UpperCAmelCase   ) as z:
						for filename in z.namelist():
									if not os.path.isdir(_UpperCAmelCase   ):
												# read the file
												if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
															with z.open(_UpperCAmelCase   ) as f:
																		for line in f:
																					_UpperCAmelCase			=       line.decode('UTF-8'   ).strip()
																					if filename == "failures_line.txt":
																								try:
																											# `error_line` is the place where `error` occurs
																											_UpperCAmelCase			=       line[: line.index(': '   )]
																											_UpperCAmelCase			=       line[line.index(': '   ) + len(': '   ) :]
																											errors.append([error_line, error]   )
																								except Exception:
																											# skip un-related lines
																											pass
																					elif filename == "summary_short.txt" and line.startswith('FAILED '   ):
																								# `test` is the test method that failed
																								_UpperCAmelCase			=       line[len('FAILED '   ) :]
																								failed_tests.append(_UpperCAmelCase   )
																					elif filename == "job_name.txt":
																								_UpperCAmelCase			=       line

			if len(_UpperCAmelCase   ) != len(_UpperCAmelCase   ):
						raise ValueError(
						    F"`errors` and `failed_tests` should have the same number of elements. Got {len(_UpperCAmelCase   )} for `errors` "
						    F"and {len(_UpperCAmelCase   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
						    ' problem.'   )

			_UpperCAmelCase			=       None
			if job_name and job_links:
						_UpperCAmelCase			=       job_links.get(_UpperCAmelCase							,       _UpperCAmelCase   )

			# A list with elements of the form (line of error, error, failed test)
			_UpperCAmelCase			=       [x + [y] + [job_link] for x, y in zip(_UpperCAmelCase							,       _UpperCAmelCase   )]

			return result





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Union[str, Any]=None   )    ->			Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       []

			_UpperCAmelCase			=       [os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   ) for p in os.listdir(_UpperCAmelCase   ) if p.endswith('.zip'   )]
			for p in paths:
						errors.extend(get_errors_from_single_artifact(_UpperCAmelCase							,       job_links=_UpperCAmelCase   )   )

			return errors





def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       Tuple=None   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       Counter()
			counter.update([x[1] for x in logs]   )
			_UpperCAmelCase			=       counter.most_common()
			_UpperCAmelCase			=       {}
			for error, count in counts:
						if error_filter is None or error not in error_filter:
									_UpperCAmelCase			=       {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

			_UpperCAmelCase			=       dict(sorted(r.items()							,       key=lambda _UpperCAmelCase   : item[1]["count"]							,       reverse=_UpperCAmelCase   )   )
			return r





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       test.split('::'   )[0]
			if test.startswith('tests/models/'   ):
						_UpperCAmelCase			=       test.split('/'   )[2]
			else:
						_UpperCAmelCase			=       None

			return test





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Union[str, Any]=None   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       [(x[0], x[1], get_model(x[2]   )) for x in logs]
			_UpperCAmelCase			=       [x for x in logs if x[2] is not None]
			_UpperCAmelCase			=       {x[2] for x in logs}

			_UpperCAmelCase			=       {}
			for test in tests:
						_UpperCAmelCase			=       Counter()
						# count by errors in `test`
						counter.update([x[1] for x in logs if x[2] == test]   )
						_UpperCAmelCase			=       counter.most_common()
						_UpperCAmelCase			=       {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
						_UpperCAmelCase			=       sum(error_counts.values()   )
						if n_errors > 0:
									_UpperCAmelCase			=       {'count': n_errors, 'errors': error_counts}

			_UpperCAmelCase			=       dict(sorted(r.items()							,       key=lambda _UpperCAmelCase   : item[1]["count"]							,       reverse=_UpperCAmelCase   )   )
			return r





def 							A     (					_UpperCAmelCase    :       Optional[int]   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       '| no. | error | status |'
			_UpperCAmelCase			=       '|-:|:-|:-|'
			_UpperCAmelCase			=       [header, sep]
			for error in reduced_by_error:
						_UpperCAmelCase			=       reduced_by_error[error]['count']
						_UpperCAmelCase			=       F"| {count} | {error[:100]} |  |"
						lines.append(_UpperCAmelCase   )

			return "\n".join(_UpperCAmelCase   )





def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       '| model | no. of errors | major error | count |'
			_UpperCAmelCase			=       '|-:|-:|-:|-:|'
			_UpperCAmelCase			=       [header, sep]
			for model in reduced_by_model:
						_UpperCAmelCase			=       reduced_by_model[model]['count']
						_UpperCAmelCase	,					_UpperCAmelCase			=       list(reduced_by_model[model]['errors'].items()   )[0]
						_UpperCAmelCase			=       F"| {model} | {count} | {error[:60]} | {_count} |"
						lines.append(_UpperCAmelCase   )

			return "\n".join(_UpperCAmelCase   )


if __name__ == "__main__":
							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.")
							UpperCAmelCase__       =	parser.parse_args()

							os.makedirs(args.output_dir, exist_ok=True)

							UpperCAmelCase__       =	get_job_links(args.workflow_run_id, token=args.token)
							UpperCAmelCase__       =	{}
							# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
							# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
							if _job_links:
														for k, v in _job_links.items():
																					# This is how GitHub actions combine job names.
																					if " / " in k:
																												UpperCAmelCase__       =	k.find(" / ")
																												UpperCAmelCase__       =	k[index + len(" / ") :]
																					UpperCAmelCase__       =	v
							with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
														json.dump(job_links, fp, ensure_ascii=False, indent=4)

							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)

							for idx, (name, url) in enumerate(artifacts.items()):
														download_artifact(name, url, args.output_dir, args.token)
														# Be gentle to GitHub
														time.sleep(1)

							UpperCAmelCase__       =	get_all_errors(args.output_dir, job_links=job_links)

							# `e[1]` is the error
							UpperCAmelCase__       =	Counter()
							counter.update([e[1] for e in errors])

							# print the top 30 most common test errors
							UpperCAmelCase__       =	counter.most_common(30)
							for item in most_common:
														print(item)

							with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
														json.dump(errors, fp, ensure_ascii=False, indent=4)

							UpperCAmelCase__       =	reduce_by_error(errors)
							UpperCAmelCase__       =	reduce_by_model(errors)

							UpperCAmelCase__       =	make_github_table(reduced_by_error)
							UpperCAmelCase__       =	make_github_table_per_model(reduced_by_model)

							with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
														fp.write(sa)
							with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
														fp.write(sa)





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil







class       __lowerCAmelCase   :



			def __init__(						self       :  Any			,		A       :  str = "cpu"			,		A       :  str = "openai/clip-vit-large-patch14")       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       device
						_UpperCAmelCase			=       CLIPTokenizerFast.from_pretrained(A)
						_UpperCAmelCase			=       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
						_UpperCAmelCase			=       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
						_UpperCAmelCase			=       torchvision.transforms.Normalize(self.image_mean			,		self.image_std)
						_UpperCAmelCase			=       torchvision.transforms.Resize(2_24)
						_UpperCAmelCase			=       torchvision.transforms.CenterCrop(2_24)



			def    _lowerCamelCase	(						self       :  str			,		A       :  Any)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.resize(A)
						_UpperCAmelCase			=       self.center_crop(A)
						_UpperCAmelCase			=       self.normalize(A)
						return images



			def __call__(						self       :  Any			,		A       :  Dict=None			,		A       :  Dict=None			,		**A       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(text=A			,		**A)
						_UpperCAmelCase			=       self.preprocess_img(A)
						_UpperCAmelCase			=       {key: value.to(self.device) for (key, value) in encoding.items()}
						return encoding







class       __lowerCAmelCase   (      nn.Module						):



			def __init__(						self       :  List[Any]			,		A       :  Any=10			,		A       :  List[Any]=0.0_1			,		A       :  Optional[int]=None			,		A       :  int=None			,		A       :  Dict=None			,		A       :  Tuple=None			,		A       :  str=None			,		A       :  Dict=None			,		A       :  Union[str, Any]=False			,		A       :  Any=True			,		A       :  Any="image"			,		A       :  Tuple=True			,		A       :  List[Any]=False			,		A       :  int=False			,		A       :  int=False			,		)       ->   None:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       device if device else get_device()
						if vqgan:
									_UpperCAmelCase			=       vqgan
						else:
									_UpperCAmelCase			=       load_vqgan(self.device			,		conf_path=A			,		ckpt_path=A)
						self.vqgan.eval()
						if clip:
									_UpperCAmelCase			=       clip
						else:
									_UpperCAmelCase			=       CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
						self.clip.to(self.device)
						_UpperCAmelCase			=       ProcessorGradientFlow(device=self.device)

						_UpperCAmelCase			=       iterations
						_UpperCAmelCase			=       lr
						_UpperCAmelCase			=       log
						_UpperCAmelCase			=       make_grid
						_UpperCAmelCase			=       return_val
						_UpperCAmelCase			=       quantize
						_UpperCAmelCase			=       self.vqgan.decoder.z_shape



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  int=None			,		A       :  Union[str, Any]=None			,		A       :  Dict=5			,		A       :  Optional[Any]=True)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       []
						if output_path is None:
									_UpperCAmelCase			=       './animation.gif'
						if input_path is None:
									_UpperCAmelCase			=       self.save_path
						_UpperCAmelCase			=       sorted(glob(input_path + '/*'))
						if not len(A):
									raise ValueError(
									    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
									    ' function?)')
						if len(A) == 1:
									print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)')
						_UpperCAmelCase			=       total_duration / len(A)
						_UpperCAmelCase			=       [frame_duration] * len(A)
						if extend_frames:
									_UpperCAmelCase			=       1.5
									_UpperCAmelCase			=       3
						for file_name in paths:
									if file_name.endswith('.png'):
												images.append(imageio.imread(A))
						imageio.mimsave(A			,		A			,		duration=A)
						print(F"gif saved to {output_path}")



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]=None			,		A       :  Optional[int]=None)       ->   int:



						"""simple docstring"""






						if not (path or img):
									raise ValueError('Input either path or tensor')
						if img is not None:
									raise NotImplementedError
						_UpperCAmelCase			=       preprocess(Image.open(A)			,		target_image_size=2_56).to(self.device)
						_UpperCAmelCase			=       preprocess_vqgan(A)
						_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.encode(A)
						return z



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.latent.detach().requires_grad_()
						_UpperCAmelCase			=       base_latent + transform_vector
						if self.quantize:
									_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.quantize(A)
						else:
									_UpperCAmelCase			=       trans_latent
						return self.vqgan.decode(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Dict			,		A       :  Dict			,		A       :  Optional[Any]=None)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.clip_preprocessor(text=A			,		images=A			,		return_tensors='pt'			,		padding=A)
						_UpperCAmelCase			=       self.clip(**A)
						_UpperCAmelCase			=       clip_outputs.logits_per_image
						if weights is not None:
									_UpperCAmelCase			=       similarity_logits * weights
						return similarity_logits.sum()



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Dict			,		A       :  int			,		A       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self._get_clip_similarity(pos_prompts['prompts']			,		A			,		weights=(1 / pos_prompts['weights']))
						if neg_prompts:
									_UpperCAmelCase			=       self._get_clip_similarity(neg_prompts['prompts']			,		A			,		weights=neg_prompts['weights'])
						else:
									_UpperCAmelCase			=       torch.tensor([1]			,		device=self.device)
						_UpperCAmelCase			=       -torch.log(A) + torch.log(A)
						return loss



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch.randn_like(self.latent			,		requires_grad=A			,		device=self.device)
						_UpperCAmelCase			=       torch.optim.Adam([vector]			,		lr=self.lr)

						for i in range(self.iterations):
									optim.zero_grad()
									_UpperCAmelCase			=       self._add_vector(A)
									_UpperCAmelCase			=       loop_post_process(A)
									_UpperCAmelCase			=       self._get_CLIP_loss(A			,		A			,		A)
									print('CLIP loss'			,		A)
									if self.log:
												wandb.log({'CLIP Loss': clip_loss})
									clip_loss.backward(retain_graph=A)
									optim.step()
									if self.return_val == "image":
												yield custom_to_pil(transformed_img[0])
									else:
												yield vector



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Any			,		A       :  Optional[int]			,		A       :  str)       ->   Any:



						"""simple docstring"""






						wandb.init(reinit=A			,		project='face-editor')
						wandb.config.update({'Positive Prompts': positive_prompts})
						wandb.config.update({'Negative Prompts': negative_prompts})
						wandb.config.update({'lr': self.lr, 'iterations': self.iterations})
						if image_path:
									_UpperCAmelCase			=       Image.open(A)
									_UpperCAmelCase			=       image.resize((2_56, 2_56))
									wandb.log('Original Image'			,		wandb.Image(A))



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						if not prompts:
									return []
						_UpperCAmelCase			=       []
						_UpperCAmelCase			=       []
						if isinstance(A			,		A):
									_UpperCAmelCase			=       [prompt.strip() for prompt in prompts.split('|')]
						for prompt in prompts:
									if isinstance(A			,		(tuple, list)):
												_UpperCAmelCase			=       prompt[0]
												_UpperCAmelCase			=       float(prompt[1])
									elif ":" in prompt:
												_UpperCAmelCase	,					_UpperCAmelCase			=       prompt.split(':')
												_UpperCAmelCase			=       float(A)
									else:
												_UpperCAmelCase			=       prompt
												_UpperCAmelCase			=       1.0
									processed_prompts.append(A)
									weights.append(A)
						return {
						    "prompts": processed_prompts,
						    "weights": torch.tensor(A			,		device=self.device),
						}



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any]			,		A       :  Union[str, Any]=None			,		A       :  int=None			,		A       :  Optional[Any]=True			,		A       :  Dict=False			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Any=None			,		)       ->   Dict:



						"""simple docstring"""






						if image_path:
									_UpperCAmelCase			=       self._get_latent(A)
						else:
									_UpperCAmelCase			=       torch.randn(self.latent_dim			,		device=self.device)
						if self.log:
									self._init_logging(A			,		A			,		A)

						assert pos_prompts, "You must provide at least one positive prompt."
						_UpperCAmelCase			=       self.process_prompts(A)
						_UpperCAmelCase			=       self.process_prompts(A)
						if save_final and save_path is None:
									_UpperCAmelCase			=       os.path.join('./outputs/'			,		'_'.join(pos_prompts['prompts']))
						if not os.path.exists(A):
									os.makedirs(A)
						else:
									_UpperCAmelCase			=       save_path + '_' + get_timestamp()
									os.makedirs(A)
						_UpperCAmelCase			=       save_path

						_UpperCAmelCase			=       self.vqgan.decode(self.latent)[0]
						if show_intermediate:
									print('Original Image')
									show_pil(custom_to_pil(A))

						_UpperCAmelCase			=       loop_post_process(A)
						for iter, transformed_img in enumerate(self._optimize_CLIP(A			,		A			,		A)):
									if show_intermediate:
												show_pil(A)
									if save_intermediate:
												transformed_img.save(os.path.join(self.save_path			,		F"iter_{iter:03d}.png"))
									if self.log:
												wandb.log({'Image': wandb.Image(A)})
						if show_final:
									show_pil(A)
						if save_final:
									transformed_img.save(os.path.join(self.save_path			,		F"iter_{iter:03d}_final.png"))
