



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__A   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

__A   			="\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"

@dataclass
class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       42

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			def __init__(   self							: int       ,       a_							: PriorTransformer       ,       a_							: CLIPVisionModel       ,       a_							: CLIPImageProcessor       ,       a_							: HeunDiscreteScheduler       ,       a_							: ShapERenderer       ,       ):
					'''simple docstring'''





					super().__init__()

					self.register_modules(
					    prior=a_       ,       image_encoder=a_       ,       image_processor=a_       ,       scheduler=a_       ,       renderer=a_       ,       )




			def  snake_case__     (   self							: Optional[int]       ,       a_							: List[Any]       ,       a_							: Optional[int]       ,       a_							: Tuple       ,       a_							: str       ,       a_							: List[Any]       ,       a_							: int ):
					'''simple docstring'''





					if latents is None:
							__UpperCAmelCase    :     Tuple													=	randn_tensor(a_       ,       generator=a_       ,       device=a_       ,       dtype=a_ )
					else:
							if latents.shape != shape:
									raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
							__UpperCAmelCase    :     Tuple													=	latents.to(a_ )

					__UpperCAmelCase    :     Optional[Any]													=	latents * scheduler.init_noise_sigma
					return latents




			def  snake_case__     (   self							: List[str]       ,       a_							: List[Any]=0 ):
					'''simple docstring'''





					if is_accelerate_available():
							from accelerate import cpu_offload
					else:
							raise ImportError('''Please install accelerate via `pip install accelerate`''' )

					__UpperCAmelCase    :     Optional[int]													=	torch.device(F'cuda:{gpu_id}' )

					__UpperCAmelCase    :     Optional[Any]													=	[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(a_       ,       a_ )




			@property
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder       ,       '''_hf_hook''' ):
							return self.device
					for module in self.image_encoder.modules():
							if (
							    hasattr(a_       ,       '''_hf_hook''' )
							    and hasattr(module._hf_hook       ,       '''execution_device''' )
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device )
					return self.device




			def  snake_case__     (   self							: Dict       ,       a_							: int       ,       a_							: int       ,       a_							: str       ,       a_							: int       ,       ):
					'''simple docstring'''





					if isinstance(a_       ,       a_ ) and isinstance(image[0]       ,       torch.Tensor ):
							__UpperCAmelCase    :     str													=	torch.cat(a_       ,       axis=0 ) if image[0].ndim == 4 else torch.stack(a_       ,       axis=0 )

					if not isinstance(a_       ,       torch.Tensor ):
							__UpperCAmelCase    :     List[str]													=	self.image_processor(a_       ,       return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )

					__UpperCAmelCase    :     str													=	image.to(dtype=self.image_encoder.dtype       ,       device=a_ )

					__UpperCAmelCase    :     List[str]													=	self.image_encoder(a_ )['''last_hidden_state''']
					__UpperCAmelCase    :     Union[str, Any]													=	image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					__UpperCAmelCase    :     Union[str, Any]													=	image_embeds.repeat_interleave(a_       ,       dim=0 )

					if do_classifier_free_guidance:
							__UpperCAmelCase    :     List[str]													=	torch.zeros_like(a_ )

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							__UpperCAmelCase    :     Optional[int]													=	torch.cat([negative_image_embeds, image_embeds] )

					return image_embeds




			@torch.no_grad()
			@replace_example_docstring(a_ )
			def __call__(   self							: Optional[Any]       ,       a_							: Union[PIL.Image.Image, List[PIL.Image.Image]]       ,       a_							: int = 1       ,       a_							: int = 25       ,       a_							: Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,       a_							: Optional[torch.FloatTensor] = None       ,       a_							: float = 4.0       ,       a_							: int = 64       ,       a_							: Optional[str] = "pil"       ,       a_							: bool = True       ,       ):
					'''simple docstring'''





					if isinstance(a_       ,       PIL.Image.Image ):
							__UpperCAmelCase    :     Optional[int]													=	1
					elif isinstance(a_       ,       torch.Tensor ):
							__UpperCAmelCase    :     Optional[Any]													=	image.shape[0]
					elif isinstance(a_       ,       a_ ) and isinstance(image[0]       ,       (torch.Tensor, PIL.Image.Image) ):
							__UpperCAmelCase    :     Optional[int]													=	len(a_ )
					else:
							raise ValueError(
							    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(a_ )}' )

					__UpperCAmelCase    :     List[Any]													=	self._execution_device

					__UpperCAmelCase    :     Optional[Any]													=	batch_size * num_images_per_prompt

					__UpperCAmelCase    :     str													=	guidance_scale > 1.0
					__UpperCAmelCase    :     Dict													=	self._encode_image(a_       ,       a_       ,       a_       ,       a_ )

					# prior

					self.scheduler.set_timesteps(a_       ,       device=a_ )
					__UpperCAmelCase    :     Dict													=	self.scheduler.timesteps

					__UpperCAmelCase    :     Any													=	self.prior.config.num_embeddings
					__UpperCAmelCase    :     str													=	self.prior.config.embedding_dim

					__UpperCAmelCase    :     Any													=	self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)       ,       image_embeds.dtype       ,       a_       ,       a_       ,       a_       ,       self.scheduler       ,       )

					# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
					__UpperCAmelCase    :     Union[str, Any]													=	latents.reshape(latents.shape[0]       ,       a_       ,       a_ )

					for i, t in enumerate(self.progress_bar(a_ ) ):
							# expand the latents if we are doing classifier free guidance
							__UpperCAmelCase    :     Tuple													=	torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
							__UpperCAmelCase    :     Any													=	self.scheduler.scale_model_input(a_       ,       a_ )

							__UpperCAmelCase    :     Optional[Any]													=	self.prior(
							    a_       ,       timestep=a_       ,       proj_embedding=a_       ,       ).predicted_image_embedding

							# remove the variance
							__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	noise_pred.split(
							    scaled_model_input.shape[2]       ,       dim=2 )  # batch_size, num_embeddings, embedding_dim

							if do_classifier_free_guidance is not None:
									__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	noise_pred.chunk(2 )
									__UpperCAmelCase    :     List[Any]													=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							__UpperCAmelCase    :     Dict													=	self.scheduler.step(
							    a_       ,       timestep=a_       ,       sample=a_       ,       ).prev_sample

					if output_type == "latent":
							return ShapEPipelineOutput(images=a_ )

					__UpperCAmelCase    :     Optional[Any]													=	[]
					for i, latent in enumerate(a_ ):
							print()
							__UpperCAmelCase    :     List[str]													=	self.renderer.decode(
							    latent[None, :]       ,       a_       ,       size=a_       ,       ray_batch_size=40_96       ,       n_coarse_samples=64       ,       n_fine_samples=1_28       ,       )

							images.append(a_ )

					__UpperCAmelCase    :     Optional[int]													=	torch.stack(a_ )

					if output_type not in ["np", "pil"]:
							raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}' )

					__UpperCAmelCase    :     List[Any]													=	images.cpu().numpy()

					if output_type == "pil":
							__UpperCAmelCase    :     List[str]													=	[self.numpy_to_pil(a_ ) for image in images]

					# Offload last model to CPU
					if hasattr(self       ,       '''final_offload_hook''' ) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (images,)

					return ShapEPipelineOutput(images=a_ )










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

import requests

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Any=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	None
		if token is not None:
				__UpperCAmelCase    :     Optional[Any]													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     List[str]													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		__UpperCAmelCase    :     Union[str, Any]													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     Union[str, Any]													=	{}

		try:
				job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)
				__UpperCAmelCase    :     List[Any]													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Optional[Any]													=	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							:					List[str] ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	None
		if token is not None:
				__UpperCAmelCase    :     Tuple													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

		__UpperCAmelCase    :     int													=	f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		__UpperCAmelCase    :     str													=	requests.get(_UpperCAmelCase ,   headers=_UpperCAmelCase						).json()
		__UpperCAmelCase    :     List[Any]													=	{}

		try:
				artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)
				__UpperCAmelCase    :     str													=	math.ceil((result['''total_count'''] - 1_00) / 1_00						)

				for i in range(_UpperCAmelCase						):
						__UpperCAmelCase    :     Dict													=	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							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	None
		if token is not None:
				__UpperCAmelCase    :     str													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'Bearer {token}'}

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

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[str]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	[]
		__UpperCAmelCase    :     int													=	[]
		__UpperCAmelCase    :     List[Any]													=	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    :     Tuple													=	line.decode('''UTF-8'''						).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		__UpperCAmelCase    :     int													=	line[: line.index(''': '''						)]
																		__UpperCAmelCase    :     Any													=	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    :     str													=	line[len('''FAILED '''						) :]
																failed_tests.append(_UpperCAmelCase						)
														elif filename == "job_name.txt":
																__UpperCAmelCase    :     Tuple													=	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    :     str													=	None
		if job_name and job_links:
				__UpperCAmelCase    :     Any													=	job_links.get(_UpperCAmelCase ,   _UpperCAmelCase						)

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

		return result

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Tuple=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]

		__UpperCAmelCase    :     str													=	[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							:					str ,   _UpperCAmelCase							:					Optional[Any]=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	Counter()
		counter.update([x[1] for x in logs]						)
		__UpperCAmelCase    :     Dict													=	counter.most_common()
		__UpperCAmelCase    :     int													=	{}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						__UpperCAmelCase    :     Tuple													=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

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

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	test.split('''::'''						)[0]
		if test.startswith('''tests/models/'''						):
				__UpperCAmelCase    :     Optional[int]													=	test.split('''/'''						)[2]
		else:
				__UpperCAmelCase    :     Optional[Any]													=	None

		return test

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					int=None						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[(x[0], x[1], get_model(x[2]						)) for x in logs]
		__UpperCAmelCase    :     int													=	[x for x in logs if x[2] is not None]
		__UpperCAmelCase    :     Tuple													=	{x[2] for x in logs}

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

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

def        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	'''| no. | error | status |'''
		__UpperCAmelCase    :     List[str]													=	'''|-:|:-|:-|'''
		__UpperCAmelCase    :     Union[str, Any]													=	[header, sep]
		for error in reduced_by_error:
				__UpperCAmelCase    :     Tuple													=	reduced_by_error[error]['''count''']
				__UpperCAmelCase    :     int													=	f'| {count} | {error[:1_00]} |  |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''| model | no. of errors | major error | count |'''
		__UpperCAmelCase    :     List[Any]													=	'''|-:|-:|-:|-:|'''
		__UpperCAmelCase    :     Tuple													=	[header, sep]
		for model in reduced_by_model:
				__UpperCAmelCase    :     List[Any]													=	reduced_by_model[model]['''count''']
				__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	list(reduced_by_model[model]['''errors'''].items()						)[0]
				__UpperCAmelCase    :     Dict													=	f'| {model} | {count} | {error[:60]} | {_count} |'
				lines.append(_UpperCAmelCase						)

		return "\n".join(_UpperCAmelCase						)


if __name__ == "__main__":
				__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.")
				__A   			=parser.parse_args()

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

				__A   			=get_job_links(args.workflow_run_id, token=args.token)
				__A   			={}
				# 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:
																__A   			=k.find(" / ")
																__A   			=k[index + len(" / ") :]
												__A   			=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)

				__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)

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

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

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

				# print the top 30 most common test errors
				__A   			=counter.most_common(3_0)
				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)

				__A   			=reduce_by_error(errors)
				__A   			=reduce_by_model(errors)

				__A   			=make_github_table(reduced_by_error)
				__A   			=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)





