"""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					:	Union[str, Any]							=		logging.get_logger(__name__)  # pylint: disable=invalid-name

a					:	Union[str, Any]							=		'''\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'''


def  _UpperCamelCase	(						_A					,      _A					,      _A=8      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   h // scale_factor**2
    if h % scale_factor**2 != 0:
        new_h += 1
    _UpperCAmelCase          =   w // scale_factor**2
    if w % scale_factor**2 != 0:
        new_w += 1
    return new_h * scale_factor, new_w * scale_factor







class 		a_						( _UpperCAmelCase	):







    def __init__(			self			:       str     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Any     ,					)		->int:






        '''simple docstring'''






        super().__init__()

        self.register_modules(
            text_encoder=lowercase__     ,					tokenizer=lowercase__     ,					unet=lowercase__     ,					scheduler=lowercase__     ,					movq=lowercase__     ,					)
        _UpperCAmelCase          =   2 ** (len(self.movq.config.block_out_channels     ) - 1)







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        if latents is None:
            _UpperCAmelCase          =   randn_tensor(lowercase__     ,					generator=lowercase__     ,					device=lowercase__     ,					dtype=lowercase__     )
        else:
            if latents.shape != shape:
                raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
            _UpperCAmelCase          =   latents.to(lowercase__     )

        _UpperCAmelCase          =   latents * scheduler.init_noise_sigma
        return latents







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str=None     ,					)		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   len(lowercase__     ) if isinstance(lowercase__     ,					lowercase__     ) else 1
        # get prompt text embeddings
        _UpperCAmelCase          =   self.tokenizer(
            lowercase__     ,					padding="""max_length"""     ,					truncation=lowercase__     ,					max_length=77     ,					return_attention_mask=lowercase__     ,					add_special_tokens=lowercase__     ,					return_tensors="""pt"""     ,					)

        _UpperCAmelCase          =   text_inputs.input_ids
        _UpperCAmelCase          =   self.tokenizer(lowercase__     ,					padding="""longest"""     ,					return_tensors="""pt"""     ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase__     ,					lowercase__     ):
            _UpperCAmelCase          =   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}"""     )

        _UpperCAmelCase          =   text_input_ids.to(lowercase__     )
        _UpperCAmelCase          =   text_inputs.attention_mask.to(lowercase__     )

        _UpperCAmelCase          =   self.text_encoder(
            input_ids=lowercase__     ,					attention_mask=lowercase__     )

        _UpperCAmelCase          =   prompt_embeds.repeat_interleave(lowercase__     ,					dim=0     )
        _UpperCAmelCase          =   text_encoder_hidden_states.repeat_interleave(lowercase__     ,					dim=0     )
        _UpperCAmelCase          =   text_mask.repeat_interleave(lowercase__     ,					dim=0     )

        if do_classifier_free_guidance:
            _UpperCAmelCase          =   42
            if negative_prompt is None:
                _UpperCAmelCase          =   [""] * batch_size
            elif type(lowercase__     ) is not type(lowercase__     ):
                raise TypeError(
                    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowercase__     )} !="""
                    f""" {type(lowercase__     )}."""     )
            elif isinstance(lowercase__     ,					lowercase__     ):
                _UpperCAmelCase          =   [negative_prompt]
            elif batch_size != len(lowercase__     ):
                raise ValueError(
                    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowercase__     )}, but `prompt`:"""
                    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                    """ the batch size of `prompt`."""     )
            else:
                _UpperCAmelCase          =   negative_prompt

            _UpperCAmelCase          =   self.tokenizer(
                lowercase__     ,					padding="""max_length"""     ,					max_length=77     ,					truncation=lowercase__     ,					return_attention_mask=lowercase__     ,					add_special_tokens=lowercase__     ,					return_tensors="""pt"""     ,					)
            _UpperCAmelCase          =   uncond_input.input_ids.to(lowercase__     )
            _UpperCAmelCase          =   uncond_input.attention_mask.to(lowercase__     )

            _UpperCAmelCase          =   self.text_encoder(
                input_ids=lowercase__     ,					attention_mask=lowercase__     )

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

            _UpperCAmelCase          =   negative_prompt_embeds.shape[1]
            _UpperCAmelCase          =   negative_prompt_embeds.repeat(1     ,					lowercase__     )
            _UpperCAmelCase          =   negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,					lowercase__     )

            _UpperCAmelCase          =   uncond_text_encoder_hidden_states.shape[1]
            _UpperCAmelCase          =   uncond_text_encoder_hidden_states.repeat(1     ,					lowercase__     ,					1     )
            _UpperCAmelCase          =   uncond_text_encoder_hidden_states.view(
                batch_size * num_images_per_prompt     ,					lowercase__     ,					-1     )
            _UpperCAmelCase          =   uncond_text_mask.repeat_interleave(lowercase__     ,					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
            _UpperCAmelCase          =   torch.cat([negative_prompt_embeds, prompt_embeds]     )
            _UpperCAmelCase          =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

            _UpperCAmelCase          =   torch.cat([uncond_text_mask, text_mask]     )

        return prompt_embeds, text_encoder_hidden_states, text_mask







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]=0     )		->str:






        '''simple docstring'''






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

        _UpperCAmelCase          =   torch.device(f"""cuda:{gpu_id}"""     )

        _UpperCAmelCase          =   [
            self.unet,
            self.text_encoder,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(lowercase__     ,					lowercase__     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[str]=0     )		->Optional[int]:






        '''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."""     )

        _UpperCAmelCase          =   torch.device(f"""cuda:{gpu_id}"""     )

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

        _UpperCAmelCase          =   None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
            _UpperCAmelCase          =   cpu_offload_with_hook(lowercase__     ,					lowercase__     ,					prev_module_hook=lowercase__     )

        if self.safety_checker is not None:
            _UpperCAmelCase          =   cpu_offload_with_hook(self.safety_checker     ,					lowercase__     ,					prev_module_hook=lowercase__     )

        # We'll offload the last model manually.
        _UpperCAmelCase          =   hook







    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def      _snake_case					(			self			:       Optional[int]     )		->List[str]:






        '''simple docstring'''






        if not hasattr(self.unet     ,					"""_hf_hook"""     ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(lowercase__     ,					"""_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(lowercase__     )
    def __call__(			self			:       str     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any = None     ,					__UpperCamelCase			:       Any = 5_12     ,					__UpperCamelCase			:       Union[str, Any] = 5_12     ,					__UpperCamelCase			:       Union[str, Any] = 1_00     ,					__UpperCamelCase			:       Union[str, Any] = 4.0     ,					__UpperCamelCase			:       Optional[int] = 1     ,					__UpperCamelCase			:       int = None     ,					__UpperCamelCase			:       Dict = None     ,					__UpperCamelCase			:       Any = "pil"     ,					__UpperCamelCase			:       int = True     ,					)		->Optional[Any]:






        '''simple docstring'''






        if isinstance(lowercase__     ,					lowercase__     ):
            _UpperCAmelCase          =   1
        elif isinstance(lowercase__     ,					lowercase__     ):
            _UpperCAmelCase          =   len(lowercase__     )
        else:
            raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase__     )}"""     )

        _UpperCAmelCase          =   self._execution_device

        _UpperCAmelCase          =   batch_size * num_images_per_prompt
        _UpperCAmelCase          =   guidance_scale > 1.0

        _UpperCAmelCase          =   self._encode_prompt(
            lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     )

        if isinstance(lowercase__     ,					lowercase__     ):
            _UpperCAmelCase          =   torch.cat(lowercase__     ,					dim=0     )
        if isinstance(lowercase__     ,					lowercase__     ):
            _UpperCAmelCase          =   torch.cat(lowercase__     ,					dim=0     )

        if do_classifier_free_guidance:
            _UpperCAmelCase          =   image_embeds.repeat_interleave(lowercase__     ,					dim=0     )
            _UpperCAmelCase          =   negative_image_embeds.repeat_interleave(lowercase__     ,					dim=0     )

        _UpperCAmelCase          =   torch.cat([negative_image_embeds, image_embeds]     ,					dim=0     ).to(
            dtype=prompt_embeds.dtype     ,					device=lowercase__     )

        self.scheduler.set_timesteps(lowercase__     ,					device=lowercase__     )
        _UpperCAmelCase          =   self.scheduler.timesteps

        _UpperCAmelCase          =   self.unet.config.in_channels

        _UpperCAmelCase          =   get_new_h_w(lowercase__     ,					lowercase__     ,					self.movq_scale_factor     )

        # create initial latent
        _UpperCAmelCase          =   self.prepare_latents(
            (batch_size, num_channels_latents, height, width)     ,					text_encoder_hidden_states.dtype     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					self.scheduler     ,					)

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

            _UpperCAmelCase          =   {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
            _UpperCAmelCase          =   self.unet(
                sample=lowercase__     ,					timestep=lowercase__     ,					encoder_hidden_states=lowercase__     ,					added_cond_kwargs=lowercase__     ,					return_dict=lowercase__     ,					)[0]

            if do_classifier_free_guidance:
                _UpperCAmelCase          =   noise_pred.split(latents.shape[1]     ,					dim=1     )
                _UpperCAmelCase          =   noise_pred.chunk(2     )
                _UpperCAmelCase          =   variance_pred.chunk(2     )
                _UpperCAmelCase          =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _UpperCAmelCase          =   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"]
            ):
                _UpperCAmelCase          =   noise_pred.split(latents.shape[1]     ,					dim=1     )

            # compute the previous noisy sample x_t -> x_t-1
            _UpperCAmelCase          =   self.scheduler.step(
                lowercase__     ,					lowercase__     ,					lowercase__     ,					generator=lowercase__     ,					).prev_sample
        # post-processing
        _UpperCAmelCase          =   self.movq.decode(lowercase__     ,					force_not_quantize=lowercase__     )["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"]:
            _UpperCAmelCase          =   image * 0.5 + 0.5
            _UpperCAmelCase          =   image.clamp(0     ,					1     )
            _UpperCAmelCase          =   image.cpu().permute(0     ,					2     ,					3     ,					1     ).float().numpy()

        if output_type == "pil":
            _UpperCAmelCase          =   self.numpy_to_pil(lowercase__     )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=lowercase__     )

"""simple docstring"""



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

import requests


def  _UpperCamelCase	(						_A					,      _A=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(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

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

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).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  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """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(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

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

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  List[str]:





    """simple docstring"""

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

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


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   None

    with zipfile.ZipFile(_A      ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A      ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A      ) 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(_A      )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase          =   line

    if len(_A      ) != len(_A      ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A      )} for `errors` """
            F"""and {len(_A      )} 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(_A					,      _A      )

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

    return result


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []

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

    return errors


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[Any]:





    """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 _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

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

    return test


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  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 _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """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[:1_0_0]} |  |"""
        lines.append(_A      )

    return "\n".join(_A      )


def  _UpperCamelCase	(						_A      )    ->  Optional[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[:6_0]} | {_count} |"""
        lines.append(_A      )

    return "\n".join(_A      )


if __name__ == "__main__":
 a					:	Tuple							=		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					:	Dict							=		parser.parse_args()

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

 a					:	Tuple							=		get_job_links(args.workflow_run_id, token=args.token)
 a					:	Tuple							=		{}
 # 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					:	List[Any]							=		k.find(''' / ''')
    a					:	Tuple							=		k[index + len(''' / ''') :]
   a					:	int							=		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					:	Tuple							=		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					:	Optional[int]							=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a					:	Union[str, Any]							=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a					:	Optional[int]							=		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					:	int							=		reduce_by_error(errors)
 a					:	str							=		reduce_by_model(errors)

 a					:	int							=		make_github_table(reduced_by_error)
 a					:	Optional[int]							=		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)