






"""simple docstring"""



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

A_    =							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					int=8      )		->str:
						A__						:  int		  =  h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						A__						:  int		  =  w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						int			,    snake_case      :						MultilingualCLIP			,    snake_case      :						XLMRobertaTokenizer			,    snake_case      :						UNetaDConditionModel			,    snake_case      :						Union[DDIMScheduler, DDPMScheduler]			,    snake_case      :						VQModel			,    ):
													'''simple docstring'''





													super().__init__()

													self.register_modules(
													    text_encoder=snake_case			,    tokenizer=snake_case			,    unet=snake_case			,    scheduler=snake_case			,    movq=snake_case			,    )
													A__						:  Optional[int]		  =  2 ** (len(self.movq.config.block_out_channels							) - 1)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Union[str, Any]			,    snake_case      :						Union[str, Any]			,    snake_case      :						Any			,    snake_case      :						List[str]			,    snake_case      :						List[Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if latents is None:
																			A__						:  List[Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=snake_case			,    dtype=snake_case							)
													else:
																			if latents.shape != shape:
																									raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
																			A__						:  Optional[Any]		  =  latents.to(snake_case							)

													A__						:  Dict		  =  latents * scheduler.init_noise_sigma
													return latents


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Dict			,    snake_case      :						int			,    snake_case      :						List[str]			,    snake_case      :						str			,    snake_case      :						Dict=None			,    ):
													'''simple docstring'''





													A__						:  Dict		  =  len(snake_case							) if isinstance(snake_case			,    snake_case							) else 1
													# get prompt text embeddings
													A__						:  Optional[Any]		  =  self.tokenizer(
													    snake_case			,    padding="""max_length"""			,    truncation=snake_case			,    max_length=77			,    return_attention_mask=snake_case			,    add_special_tokens=snake_case			,    return_tensors="""pt"""			,    )

													A__						:  int		  =  text_inputs.input_ids
													A__						:  Optional[Any]		  =  self.tokenizer(snake_case			,    padding="""longest"""			,    return_tensors="""pt"""							).input_ids

													if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case			,    snake_case							):
																			A__						:  str		  =  self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]							)
																			logger.warning(
																			    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																			    F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)

													A__						:  Dict		  =  text_input_ids.to(snake_case							)
													A__						:  Optional[int]		  =  text_inputs.attention_mask.to(snake_case							)

													A__  ,  A__						:  List[str]		  =  self.text_encoder(
													    input_ids=snake_case			,    attention_mask=snake_case							)

													A__						:  Optional[Any]		  =  prompt_embeds.repeat_interleave(snake_case			,    dim=0							)
													A__						:  Any		  =  text_encoder_hidden_states.repeat_interleave(snake_case			,    dim=0							)
													A__						:  List[str]		  =  text_mask.repeat_interleave(snake_case			,    dim=0							)

													if do_classifier_free_guidance:
																			A__						:  List[str]
																			if negative_prompt is None:
																									A__						:  List[str]		  =  [""""""] * batch_size
																			elif type(snake_case							) is not type(snake_case							):
																									raise TypeError(
																									    F'`negative_prompt` should be the same type to `prompt`, but got {type(snake_case							)} !='
																									    F' {type(snake_case							)}.'							)
																			elif isinstance(snake_case			,    snake_case							):
																									A__						:  List[str]		  =  [negative_prompt]
																			elif batch_size != len(snake_case							):
																									raise ValueError(
																									    F'`negative_prompt`: {negative_prompt} has batch size {len(snake_case							)}, but `prompt`:'
																									    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																									    """ the batch size of `prompt`."""							)
																			else:
																									A__						:  int		  =  negative_prompt

																			A__						:  Union[str, Any]		  =  self.tokenizer(
																			    snake_case			,    padding="""max_length"""			,    max_length=77			,    truncation=snake_case			,    return_attention_mask=snake_case			,    add_special_tokens=snake_case			,    return_tensors="""pt"""			,    )
																			A__						:  Any		  =  uncond_input.input_ids.to(snake_case							)
																			A__						:  List[str]		  =  uncond_input.attention_mask.to(snake_case							)

																			A__  ,  A__						:  int		  =  self.text_encoder(
																			    input_ids=snake_case			,    attention_mask=snake_case							)

																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																			A__						:  Optional[int]		  =  negative_prompt_embeds.shape[1]
																			A__						:  Dict		  =  negative_prompt_embeds.repeat(1			,    snake_case							)
																			A__						:  List[str]		  =  negative_prompt_embeds.view(batch_size * num_images_per_prompt			,    snake_case							)

																			A__						:  str		  =  uncond_text_encoder_hidden_states.shape[1]
																			A__						:  Optional[Any]		  =  uncond_text_encoder_hidden_states.repeat(1			,    snake_case			,    1							)
																			A__						:  List[Any]		  =  uncond_text_encoder_hidden_states.view(
																			    batch_size * num_images_per_prompt			,    snake_case			,    -1							)
																			A__						:  Union[str, Any]		  =  uncond_text_mask.repeat_interleave(snake_case			,    dim=0							)

																			# done duplicates

																			# 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
																			A__						:  Any		  =  torch.cat([negative_prompt_embeds, prompt_embeds]							)
																			A__						:  Dict		  =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

																			A__						:  Any		  =  torch.cat([uncond_text_mask, text_mask]							)

													return prompt_embeds, text_encoder_hidden_states, text_mask


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Union[str, Any]=0							):
													'''simple docstring'''





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

													A__						:  List[str]		  =  torch.device(F'cuda:{gpu_id}'							)

													A__						:  Union[str, Any]		  =  [
													    self.unet,
													    self.text_encoder,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																									cpu_offload(snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Tuple=0							):
													'''simple docstring'''





													if is_accelerate_available() and is_accelerate_version(""">="""			,    """0.17.0.dev0"""							):
																			from accelerate import cpu_offload_with_hook
													else:
																			raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""							)

													A__						:  Union[str, Any]		  =  torch.device(F'cuda:{gpu_id}'							)

													if self.device.type != "cpu":
																			self.to("""cpu"""			,    silence_dtype_warnings=snake_case							)
																			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													A__						:  Optional[Any]		  =  None
													for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
																			A__  ,  A__						:  Dict		  =  cpu_offload_with_hook(snake_case			,    snake_case			,    prev_module_hook=snake_case							)

													if self.safety_checker is not None:
																			A__  ,  A__						:  Dict		  =  cpu_offload_with_hook(self.safety_checker			,    snake_case			,    prev_module_hook=snake_case							)

													# We'll offload the last model manually.
													A__						:  List[Any]		  =  hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													if not hasattr(self.unet			,    """_hf_hook"""							):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(snake_case			,    """_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


							@torch.no_grad()
							@replace_example_docstring(snake_case							)
							def __call__(			self      :						Optional[Any]			,    snake_case      :						Union[str, List[str]]			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						Optional[Union[str, List[str]]] = None			,    snake_case      :						int = 512			,    snake_case      :						int = 512			,    snake_case      :						int = 100			,    snake_case      :						float = 4.0			,    snake_case      :						int = 1			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[torch.FloatTensor] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    ):
													'''simple docstring'''





													if isinstance(snake_case			,    snake_case							):
																			A__						:  Dict		  =  1
													elif isinstance(snake_case			,    snake_case							):
																			A__						:  List[Any]		  =  len(snake_case							)
													else:
																			raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(snake_case							)}'							)

													A__						:  Optional[int]		  =  self._execution_device

													A__						:  Optional[Any]		  =  batch_size * num_images_per_prompt
													A__						:  Union[str, Any]		  =  guidance_scale > 1.0

													A__  ,  A__  ,  A__						:  Dict		  =  self._encode_prompt(
													    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case							)

													if isinstance(snake_case			,    snake_case							):
																			A__						:  str		  =  torch.cat(snake_case			,    dim=0							)
													if isinstance(snake_case			,    snake_case							):
																			A__						:  List[Any]		  =  torch.cat(snake_case			,    dim=0							)

													if do_classifier_free_guidance:
																			A__						:  Tuple		  =  image_embeds.repeat_interleave(snake_case			,    dim=0							)
																			A__						:  List[str]		  =  negative_image_embeds.repeat_interleave(snake_case			,    dim=0							)

													A__						:  Tuple		  =  torch.cat([negative_image_embeds, image_embeds]			,    dim=0							).to(
													    dtype=prompt_embeds.dtype			,    device=snake_case							)

													self.scheduler.set_timesteps(snake_case			,    device=snake_case							)
													A__						:  str		  =  self.scheduler.timesteps

													A__						:  List[str]		  =  self.unet.config.in_channels

													A__  ,  A__						:  Dict		  =  get_new_h_w(snake_case			,    snake_case			,    self.movq_scale_factor							)

													# create initial latent
													A__						:  Dict		  =  self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)			,    text_encoder_hidden_states.dtype			,    snake_case			,    snake_case			,    snake_case			,    self.scheduler			,    )

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

																			A__						:  Dict		  =  {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
																			A__						:  Optional[Any]		  =  self.unet(
																			    sample=snake_case			,    timestep=snake_case			,    encoder_hidden_states=snake_case			,    added_cond_kwargs=snake_case			,    return_dict=snake_case			,    )[0]

																			if do_classifier_free_guidance:
																									A__  ,  A__						:  List[str]		  =  noise_pred.split(latents.shape[1]			,    dim=1							)
																									A__  ,  A__						:  List[str]		  =  noise_pred.chunk(2							)
																									A__  ,  A__						:  Optional[int]		  =  variance_pred.chunk(2							)
																									A__						:  str		  =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									A__						:  Tuple		  =  torch.cat([noise_pred, variance_pred_text]			,    dim=1							)

																			if not (
																			    hasattr(self.scheduler.config			,    """variance_type"""							)
																			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																			):
																									A__  ,  A__						:  Tuple		  =  noise_pred.split(latents.shape[1]			,    dim=1							)

																			# compute the previous noisy sample x_t -> x_t-1
																			A__						:  List[Any]		  =  self.scheduler.step(
																			    snake_case			,    snake_case			,    snake_case			,    generator=snake_case			,    ).prev_sample
													# post-processing
													A__						:  Optional[int]		  =  self.movq.decode(snake_case			,    force_not_quantize=snake_case							)["""sample"""]

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

													if output_type in ["np", "pil"]:
																			A__						:  List[Any]		  =  image * 0.5 + 0.5
																			A__						:  str		  =  image.clamp(0			,    1							)
																			A__						:  Optional[Any]		  =  image.cpu().permute(0			,    2			,    3			,    1							).float().numpy()

													if output_type == "pil":
																			A__						:  Optional[int]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)














"""simple docstring"""



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

import requests




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Union[str, Any]=None      )		->Tuple:
						A__						:  Dict		  =  None
						if token is not None:
												A__						:  str		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Dict		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
						A__						:  Any		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Tuple		  =  {}

						try:
												job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )
												A__						:  Optional[Any]		  =  math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

												for i in range(UpperCAmelCase__      ):
																		A__						:  str		  =  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      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					str=None      )		->List[str]:
						A__						:  Optional[Any]		  =  None
						if token is not None:
												A__						:  Any		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  str		  =  f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
						A__						:  Dict		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__      ).json()
						A__						:  Any		  =  {}

						try:
												artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )
												A__						:  Union[str, Any]		  =  math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

												for i in range(UpperCAmelCase__      ):
																		A__						:  Union[str, Any]		  =  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      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Any      )		->Tuple:
						A__						:  Tuple		  =  None
						if token is not None:
												A__						:  List[Any]		  =  {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__						:  Tuple		  =  requests.get(UpperCAmelCase__,						headers=UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  Dict		  =  result.headers["""Location"""]
						A__						:  Union[str, Any]		  =  requests.get(UpperCAmelCase__,						allow_redirects=UpperCAmelCase__      )
						A__						:  int		  =  os.path.join(UpperCAmelCase__,						f'{artifact_name}.zip'      )
						with open(UpperCAmelCase__,						"""wb"""      ) as fp:
												fp.write(response.content      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple=None      )		->Tuple:
						A__						:  int		  =  []
						A__						:  Union[str, Any]		  =  []
						A__						:  Optional[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:
																																										A__						:  int		  =  line.decode("""UTF-8"""      ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						A__						:  List[str]		  =  line[: line.index(""": """      )]
																																																						A__						:  str		  =  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
																																																A__						:  Any		  =  line[len("""FAILED """      ) :]
																																																failed_tests.append(UpperCAmelCase__      )
																																										elif filename == "job_name.txt":
																																																A__						:  Any		  =  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."""      )

						A__						:  List[str]		  =  None
						if job_name and job_links:
												A__						:  Any		  =  job_links.get(UpperCAmelCase__,						UpperCAmelCase__      )

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

						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int=None      )		->str:
						A__						:  List[Any]		  =  []

						A__						:  Dict		  =  [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      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Dict=None      )		->List[Any]:
						A__						:  Dict		  =  Counter()
						counter.update([x[1] for x in logs]      )
						A__						:  str		  =  counter.most_common()
						A__						:  Dict		  =  {}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		A__						:  Optional[int]		  =  {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any]      )		->str:
						A__						:  List[str]		  =  test.split("""::"""      )[0]
						if test.startswith("""tests/models/"""      ):
												A__						:  Union[str, Any]		  =  test.split("""/"""      )[2]
						else:
												A__						:  int		  =  None

						return test




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str=None      )		->Optional[Any]:
						A__						:  Any		  =  [(x[0], x[1], get_model(x[2]      )) for x in logs]
						A__						:  List[Any]		  =  [x for x in logs if x[2] is not None]
						A__						:  Union[str, Any]		  =  {x[2] for x in logs}

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

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->List[Any]:
						A__						:  List[Any]		  =  """| no. | error | status |"""
						A__						:  Union[str, Any]		  =  """|-:|:-|:-|"""
						A__						:  Dict		  =  [header, sep]
						for error in reduced_by_error:
												A__						:  List[Any]		  =  reduced_by_error[error]["""count"""]
												A__						:  List[Any]		  =  f'| {count} | {error[:1_0_0]} |  |'
												lines.append(UpperCAmelCase__      )

						return "\n".join(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						A__						:  str		  =  """| model | no. of errors | major error | count |"""
						A__						:  Optional[int]		  =  """|-:|-:|-:|-:|"""
						A__						:  Tuple		  =  [header, sep]
						for model in reduced_by_model:
												A__						:  Optional[Any]		  =  reduced_by_model[model]["""count"""]
												A__  ,  A__						:  Optional[Any]		  =  list(reduced_by_model[model]["""errors"""].items()      )[0]
												A__						:  Optional[int]		  =  f'| {model} | {count} | {error[:6_0]} | {_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(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)

						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)






