




from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


_lowerCamelCase   :	Union[str, Any]       =    [
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
_lowerCamelCase   :	Optional[Any]       =    "UperNetConfig"
class 							__snake_case	(nn.Module		):



				def __init__(   self   :		Dict		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		Union[int, Tuple[int, int]]		,						_UpperCAmelCase   :		Union[int, Tuple[int, int], str] = 0		,						_UpperCAmelCase   :		bool = False		,						_UpperCAmelCase   :		Union[int, Tuple[int, int]] = 1		,						)	->     List[Any]:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase     :     Any              =   nn.Convad(
								    in_channels=_UpperCAmelCase		,						out_channels=_UpperCAmelCase		,						kernel_size=_UpperCAmelCase		,						padding=_UpperCAmelCase		,						bias=_UpperCAmelCase		,						dilation=_UpperCAmelCase		,						)
								_lowerCAmelCase     :     str              =   nn.BatchNormad(_UpperCAmelCase   )
								_lowerCAmelCase     :     Dict              =   nn.ReLU()



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						_UpperCAmelCase   :		torch.Tensor   )	->     Any:





								'''simple docstring'''




								_lowerCAmelCase     :     int              =   self.conv(_UpperCAmelCase   )
								_lowerCAmelCase     :     Tuple              =   self.batch_norm(_UpperCAmelCase   )
								_lowerCAmelCase     :     List[str]              =   self.activation(_UpperCAmelCase   )

								return output






class 							__snake_case	(nn.Module		):



				def __init__(   self   :		Any		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		int   )	->     Any:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase     :     Any              =   [
								    nn.AdaptiveAvgPoolad(_UpperCAmelCase   ),
								    UperNetConvModule(_UpperCAmelCase		,						_UpperCAmelCase		,						kernel_size=1   ),
								]
								for i, layer in enumerate(self.layers   ):
												self.add_module(str(_UpperCAmelCase   )		,						_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						_UpperCAmelCase   :		torch.Tensor   )	->     Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     List[Any]              =   input
								for layer in self.layers:
												_lowerCAmelCase     :     Any              =   layer(_UpperCAmelCase   )
								return hidden_state






class 							__snake_case	(nn.Module		):



				def __init__(   self   :		List[Any]		,						_UpperCAmelCase   :		Tuple[int, ...]		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		int		,						_UpperCAmelCase   :		bool   )	->     Tuple:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase     :     Union[str, Any]              =   pool_scales
								_lowerCAmelCase     :     Tuple              =   align_corners
								_lowerCAmelCase     :     Optional[Any]              =   in_channels
								_lowerCAmelCase     :     str              =   channels
								_lowerCAmelCase     :     Any              =   []
								for i, pool_scale in enumerate(_UpperCAmelCase   ):
												_lowerCAmelCase     :     Tuple              =   UperNetPyramidPoolingBlock(pool_scale=_UpperCAmelCase		,						in_channels=_UpperCAmelCase		,						channels=_UpperCAmelCase   )
												self.blocks.append(_UpperCAmelCase   )
												self.add_module(str(_UpperCAmelCase   )		,						_UpperCAmelCase   )



				def    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		torch.Tensor   )	->     Dict:





								'''simple docstring'''




								_lowerCAmelCase     :     int              =   []
								for ppm in self.blocks:
												_lowerCAmelCase     :     str              =   ppm(_UpperCAmelCase   )
												_lowerCAmelCase     :     str              =   nn.functional.interpolate(
												    _UpperCAmelCase		,						size=x.size()[2:]		,						mode="""bilinear"""		,						align_corners=self.align_corners   )
												ppm_outs.append(_UpperCAmelCase   )
								return ppm_outs






class 							__snake_case	(nn.Module		):



				def __init__(   self   :		Tuple		,						_UpperCAmelCase   :		str		,						_UpperCAmelCase   :		int   )	->     Tuple:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase     :     Tuple              =   config
								_lowerCAmelCase     :     List[str]              =   config.pool_scales  # e.g. (1, 2, 3, 6)
								_lowerCAmelCase     :     Any              =   in_channels
								_lowerCAmelCase     :     List[str]              =   config.hidden_size
								_lowerCAmelCase     :     int              =   False
								_lowerCAmelCase     :     int              =   nn.Convad(self.channels		,						config.num_labels		,						kernel_size=1   )

								# PSP Module
								_lowerCAmelCase     :     Optional[int]              =   UperNetPyramidPoolingModule(
								    self.pool_scales		,						self.in_channels[-1]		,						self.channels		,						align_corners=self.align_corners		,						)
								_lowerCAmelCase     :     Any              =   UperNetConvModule(
								    self.in_channels[-1] + len(self.pool_scales   ) * self.channels		,						self.channels		,						kernel_size=3		,						padding=1		,						)
								# FPN Module
								_lowerCAmelCase     :     Optional[int]              =   nn.ModuleList()
								_lowerCAmelCase     :     int              =   nn.ModuleList()
								for in_channels in self.in_channels[:-1]:  # skip the top layer
												_lowerCAmelCase     :     str              =   UperNetConvModule(_UpperCAmelCase		,						self.channels		,						kernel_size=1   )
												_lowerCAmelCase     :     List[str]              =   UperNetConvModule(self.channels		,						self.channels		,						kernel_size=3		,						padding=1   )
												self.lateral_convs.append(_UpperCAmelCase   )
												self.fpn_convs.append(_UpperCAmelCase   )

								_lowerCAmelCase     :     Union[str, Any]              =   UperNetConvModule(
								    len(self.in_channels   ) * self.channels		,						self.channels		,						kernel_size=3		,						padding=1		,						)



				def    SCREAMING_SNAKE_CASE							(   self   :		Optional[Any]   )	->     Optional[Any]:





								'''simple docstring'''




								self.apply(self._init_weights   )



				def    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		Dict   )	->     Optional[Any]:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,						nn.Convad   ):
												module.weight.data.normal_(mean=0.0		,						std=self.config.initializer_range   )
												if module.bias is not None:
																module.bias.data.zero_()



				def    SCREAMING_SNAKE_CASE							(   self   :		str		,						_UpperCAmelCase   :		Dict   )	->     List[Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     Optional[Any]              =   inputs[-1]
								_lowerCAmelCase     :     Optional[int]              =   [x]
								psp_outs.extend(self.psp_modules(_UpperCAmelCase   )   )
								_lowerCAmelCase     :     List[str]              =   torch.cat(_UpperCAmelCase		,						dim=1   )
								_lowerCAmelCase     :     Union[str, Any]              =   self.bottleneck(_UpperCAmelCase   )

								return output



				def    SCREAMING_SNAKE_CASE							(   self   :		int		,						_UpperCAmelCase   :		torch.Tensor   )	->     Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase     :     Optional[Any]              =   [lateral_conv(encoder_hidden_states[i]   ) for i, lateral_conv in enumerate(self.lateral_convs   )]

								laterals.append(self.psp_forward(_UpperCAmelCase   )   )

								# build top-down path
								_lowerCAmelCase     :     Tuple              =   len(_UpperCAmelCase   )
								for i in range(used_backbone_levels - 1		,						0		,						-1   ):
												_lowerCAmelCase     :     List[str]              =   laterals[i - 1].shape[2:]
												_lowerCAmelCase     :     str              =   laterals[i - 1] + nn.functional.interpolate(
												    laterals[i]		,						size=_UpperCAmelCase		,						mode="""bilinear"""		,						align_corners=self.align_corners   )

								# build outputs
								_lowerCAmelCase     :     Optional[int]              =   [self.fpn_convs[i](laterals[i]   ) for i in range(used_backbone_levels - 1   )]
								# append psp feature
								fpn_outs.append(laterals[-1]   )

								for i in range(used_backbone_levels - 1		,						0		,						-1   ):
												_lowerCAmelCase     :     int              =   nn.functional.interpolate(
												    fpn_outs[i]		,						size=fpn_outs[0].shape[2:]		,						mode="""bilinear"""		,						align_corners=self.align_corners   )
								_lowerCAmelCase     :     str              =   torch.cat(_UpperCAmelCase		,						dim=1   )
								_lowerCAmelCase     :     Optional[int]              =   self.fpn_bottleneck(_UpperCAmelCase   )
								_lowerCAmelCase     :     List[Any]              =   self.classifier(_UpperCAmelCase   )

								return output






class 							__snake_case	(nn.Module		):



				def __init__(   self   :		Tuple		,						_UpperCAmelCase   :		Union[str, Any]		,						_UpperCAmelCase   :		int = 2		,						_UpperCAmelCase   :		int = 3		,						_UpperCAmelCase   :		Union[int, Tuple[int, int]] = 1   )	->     Union[str, Any]:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase     :     Dict              =   config
								_lowerCAmelCase     :     Optional[int]              =   config.auxiliary_in_channels
								_lowerCAmelCase     :     List[Any]              =   config.auxiliary_channels
								_lowerCAmelCase     :     List[str]              =   config.auxiliary_num_convs
								_lowerCAmelCase     :     Optional[int]              =   config.auxiliary_concat_input
								_lowerCAmelCase     :     Tuple              =   in_index

								_lowerCAmelCase     :     Union[str, Any]              =   (kernel_size // 2) * dilation
								_lowerCAmelCase     :     Optional[Any]              =   []
								convs.append(
								    UperNetConvModule(
								        self.in_channels		,						self.channels		,						kernel_size=_UpperCAmelCase		,						padding=_UpperCAmelCase		,						dilation=_UpperCAmelCase   )   )
								for i in range(self.num_convs - 1   ):
												convs.append(
												    UperNetConvModule(
												        self.channels		,						self.channels		,						kernel_size=_UpperCAmelCase		,						padding=_UpperCAmelCase		,						dilation=_UpperCAmelCase   )   )
								if self.num_convs == 0:
												_lowerCAmelCase     :     List[Any]              =   nn.Identity()
								else:
												_lowerCAmelCase     :     Optional[int]              =   nn.Sequential(*_UpperCAmelCase   )
								if self.concat_input:
												_lowerCAmelCase     :     Optional[Any]              =   UperNetConvModule(
												    self.in_channels + self.channels		,						self.channels		,						kernel_size=_UpperCAmelCase		,						padding=kernel_size // 2   )

								_lowerCAmelCase     :     List[str]              =   nn.Convad(self.channels		,						config.num_labels		,						kernel_size=1   )



				def    SCREAMING_SNAKE_CASE							(   self   :		str   )	->     Optional[int]:





								'''simple docstring'''




								self.apply(self._init_weights   )



				def    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		List[str]   )	->     int:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,						nn.Convad   ):
												module.weight.data.normal_(mean=0.0		,						std=self.config.initializer_range   )
												if module.bias is not None:
																module.bias.data.zero_()



				def    SCREAMING_SNAKE_CASE							(   self   :		Union[str, Any]		,						_UpperCAmelCase   :		torch.Tensor   )	->     Dict:





								'''simple docstring'''




								_lowerCAmelCase     :     int              =   encoder_hidden_states[self.in_index]
								_lowerCAmelCase     :     int              =   self.convs(_UpperCAmelCase   )
								if self.concat_input:
												_lowerCAmelCase     :     int              =   self.conv_cat(torch.cat([hidden_states, output]		,						dim=1   )   )
								_lowerCAmelCase     :     Any              =   self.classifier(_UpperCAmelCase   )
								return output






class 							__snake_case	(_a		):
				lowerCAmelCase__      						=  UperNetConfig
				lowerCAmelCase__      						=  "pixel_values"
				lowerCAmelCase__      						=  True



				def    SCREAMING_SNAKE_CASE							(   self   :		Tuple		,						_UpperCAmelCase   :		Optional[int]   )	->     Tuple:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,						_UpperCAmelCase   ):
												module.backbone.init_weights()
												module.decode_head.init_weights()
												module.auxiliary_head.init_weights()



				def    SCREAMING_SNAKE_CASE							(   self   :		int   )	->     List[Any]:





								'''simple docstring'''




								self.backbone.init_weights()
								self.decode_head.init_weights()
								self.auxiliary_head.init_weights()



				def    SCREAMING_SNAKE_CASE							(   self   :		Any		,						_UpperCAmelCase   :		Any		,						_UpperCAmelCase   :		int=False   )	->     str:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,						_UpperCAmelCase   ):
												_lowerCAmelCase     :     Optional[int]              =   value






_lowerCamelCase   :	List[Any]       =    R"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_lowerCamelCase   :	Optional[Any]       =    R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."    ,						_a    ,						)
class 							__snake_case	(_a		):



				def __init__(   self   :		str		,						_UpperCAmelCase   :		List[Any]   )	->     str:





								'''simple docstring'''




								super().__init__(_UpperCAmelCase   )

								_lowerCAmelCase     :     Union[str, Any]              =   AutoBackbone.from_config(config.backbone_config   )

								# Semantic segmentation head(s)
								_lowerCAmelCase     :     List[str]              =   UperNetHead(_UpperCAmelCase		,						in_channels=self.backbone.channels   )
								_lowerCAmelCase     :     Dict              =   UperNetFCNHead(_UpperCAmelCase   ) if config.use_auxiliary_head else None

								# Initialize weights and apply final processing
								self.post_init()



				@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("""batch_size, sequence_length"""   )   )
				@replace_return_docstrings(output_type=_UpperCAmelCase		,						config_class=_CONFIG_FOR_DOC   )
				def    SCREAMING_SNAKE_CASE							(   self   :		List[str]		,						_UpperCAmelCase   :		Optional[torch.Tensor] = None		,						_UpperCAmelCase   :		Optional[bool] = None		,						_UpperCAmelCase   :		Optional[bool] = None		,						_UpperCAmelCase   :		Optional[torch.Tensor] = None		,						_UpperCAmelCase   :		Optional[bool] = None		,						)	->     Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase     :     Optional[Any]              =   return_dict if return_dict is not None else self.config.use_return_dict
								_lowerCAmelCase     :     Tuple              =   (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								_lowerCAmelCase     :     str              =   output_attentions if output_attentions is not None else self.config.output_attentions

								_lowerCAmelCase     :     Union[str, Any]              =   self.backbone.forward_with_filtered_kwargs(
								    _UpperCAmelCase		,						output_hidden_states=_UpperCAmelCase		,						output_attentions=_UpperCAmelCase   )
								_lowerCAmelCase     :     Any              =   outputs.feature_maps

								_lowerCAmelCase     :     Optional[int]              =   self.decode_head(_UpperCAmelCase   )
								_lowerCAmelCase     :     Any              =   nn.functional.interpolate(_UpperCAmelCase		,						size=pixel_values.shape[2:]		,						mode="""bilinear"""		,						align_corners=_UpperCAmelCase   )

								_lowerCAmelCase     :     int              =   None
								if self.auxiliary_head is not None:
												_lowerCAmelCase     :     List[str]              =   self.auxiliary_head(_UpperCAmelCase   )
												_lowerCAmelCase     :     Optional[Any]              =   nn.functional.interpolate(
												    _UpperCAmelCase		,						size=pixel_values.shape[2:]		,						mode="""bilinear"""		,						align_corners=_UpperCAmelCase   )

								_lowerCAmelCase     :     str              =   None
								if labels is not None:
												if self.config.num_labels == 1:
																raise ValueError("""The number of labels should be greater than one"""   )
												else:
																# compute weighted loss
																_lowerCAmelCase     :     int              =   CrossEntropyLoss(ignore_index=self.config.loss_ignore_index   )
																_lowerCAmelCase     :     Tuple              =   loss_fct(_UpperCAmelCase		,						_UpperCAmelCase   )
																_lowerCAmelCase     :     Any              =   loss_fct(_UpperCAmelCase		,						_UpperCAmelCase   )
																_lowerCAmelCase     :     List[Any]              =   main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

								if not return_dict:
												if output_hidden_states:
																_lowerCAmelCase     :     List[str]              =   (logits,) + outputs[1:]
												else:
																_lowerCAmelCase     :     List[Any]              =   (logits,) + outputs[2:]
												return ((loss,) + output) if loss is not None else output

								return SemanticSegmenterOutput(
								    loss=_UpperCAmelCase		,						logits=_UpperCAmelCase		,						hidden_states=outputs.hidden_states		,						attentions=outputs.attentions		,						)







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

import requests





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				[]
	a									=				[]
	a									=				None

	with zipfile.ZipFile(__lowerCamelCase       ) as z:
		for filename in z.namelist():
			if not os.path.isdir(__lowerCamelCase       ):
				# read the file
				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
					with z.open(__lowerCamelCase       ) as f:
						for line in f:
							a									=				line.decode("""UTF-8"""       ).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									a									=				line[: line.index(""": """       )]
									a									=				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									=				line[len("""FAILED """       ) :]
								failed_tests.append(__lowerCamelCase       )
							elif filename == "job_name.txt":
								a									=				line

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

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

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

	return result





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				[]

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

	return errors





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				Counter()
	counter.update([x[1] for x in logs]       )
	a									=				counter.most_common()
	a									=				{}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			a									=				{"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

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





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				test.split("""::"""       )[0]
	if test.startswith("""tests/models/"""       ):
		a									=				test.split("""/"""       )[2]
	else:
		a									=				None

	return test





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Any:
	a									=				[(x[0], x[1], get_model(x[2]       )) for x in logs]
	a									=				[x for x in logs if x[2] is not None]
	a									=				{x[2] for x in logs}

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

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





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""| no. | error | status |"""
	a									=				"""|-:|:-|:-|"""
	a									=				[header, sep]
	for error in reduced_by_error:
		a									=				reduced_by_error[error]["""count"""]
		a									=				f'| {count} | {error[:100]} |  |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				"""| model | no. of errors | major error | count |"""
	a									=				"""|-:|-:|-:|-:|"""
	a									=				[header, sep]
	for model in reduced_by_model:
		a									=				reduced_by_model[model]["""count"""]
		a							,					a									=				list(reduced_by_model[model]["""errors"""].items()       )[0]
		a									=				f'| {model} | {count} | {error[:60]} | {_count} |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Dict          = 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   :			Any          = parser.parse_args()

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

		__UpperCamelCase   :			Optional[Any]          = get_job_links(args.workflow_run_id, token=args.token)
		__UpperCamelCase   :			str          = {}
		# 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   :			List[str]          = k.find(" / ")
								__UpperCamelCase   :			List[Any]          = k[index + len(" / ") :]
						__UpperCamelCase   :			List[str]          = 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   :			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)

		__UpperCamelCase   :			int          = get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__UpperCamelCase   :			Optional[Any]          = Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__UpperCamelCase   :			Any          = 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   :			Union[str, Any]          = reduce_by_error(errors)
		__UpperCamelCase   :			Dict          = reduce_by_model(errors)

		__UpperCamelCase   :			Union[str, Any]          = make_github_table(reduced_by_error)
		__UpperCamelCase   :			Union[str, Any]          = 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)

