"""simple docstring"""



import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 		a_						:







    def __init__(			self			:       int     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Any=13     ,					__UpperCamelCase			:       str=32     ,					__UpperCamelCase			:       Tuple=2     ,					__UpperCamelCase			:       Optional[Any]=3     ,					__UpperCamelCase			:       Optional[int]=16     ,					__UpperCamelCase			:       Union[str, Any]=[32, 64, 1_28]     ,					__UpperCamelCase			:       str=[1, 2, 1]     ,					__UpperCamelCase			:       Union[str, Any]=[2, 2, 4]     ,					__UpperCamelCase			:       List[Any]=2     ,					__UpperCamelCase			:       Dict=2.0     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[int]=0.0     ,					__UpperCamelCase			:       Tuple=0.0     ,					__UpperCamelCase			:       Tuple=0.1     ,					__UpperCamelCase			:       Any="gelu"     ,					__UpperCamelCase			:       str=False     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       Dict=0.0_2     ,					__UpperCamelCase			:       Optional[int]=1e-5     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Any=True     ,					__UpperCamelCase			:       Optional[Any]=10     ,					__UpperCamelCase			:       List[str]=8     ,					__UpperCamelCase			:       Tuple=["stage1", "stage2"]     ,					__UpperCamelCase			:       List[str]=[1, 2]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   patch_size
        _UpperCAmelCase          =   num_channels
        _UpperCAmelCase          =   embed_dim
        _UpperCAmelCase          =   hidden_sizes
        _UpperCAmelCase          =   depths
        _UpperCAmelCase          =   num_heads
        _UpperCAmelCase          =   window_size
        _UpperCAmelCase          =   mlp_ratio
        _UpperCAmelCase          =   qkv_bias
        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   drop_path_rate
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   use_absolute_embeddings
        _UpperCAmelCase          =   patch_norm
        _UpperCAmelCase          =   layer_norm_eps
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   scope
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   type_sequence_label_size
        _UpperCAmelCase          =   encoder_stride
        _UpperCAmelCase          =   out_features
        _UpperCAmelCase          =   out_indices







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






        '''simple docstring'''






        _UpperCAmelCase          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        _UpperCAmelCase          =   None
        if self.use_labels:
            _UpperCAmelCase          =   ids_tensor([self.batch_size]     ,					self.type_sequence_label_size     )

        _UpperCAmelCase          =   self.get_config()

        return config, pixel_values, labels







    def      _snake_case					(			self			:       str     )		->Optional[Any]:






        '''simple docstring'''






        return FocalNetConfig(
            image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					embed_dim=self.embed_dim     ,					hidden_sizes=self.hidden_sizes     ,					depths=self.depths     ,					num_heads=self.num_heads     ,					window_size=self.window_size     ,					mlp_ratio=self.mlp_ratio     ,					qkv_bias=self.qkv_bias     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					drop_path_rate=self.drop_path_rate     ,					hidden_act=self.hidden_act     ,					use_absolute_embeddings=self.use_absolute_embeddings     ,					path_norm=self.patch_norm     ,					layer_norm_eps=self.layer_norm_eps     ,					initializer_range=self.initializer_range     ,					encoder_stride=self.encoder_stride     ,					out_features=self.out_features     ,					out_indices=self.out_indices     ,					)







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )

        _UpperCAmelCase          =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
        _UpperCAmelCase          =   int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

        self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, expected_seq_len, expected_dim)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetBackbone(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps     )     ,					len(config.out_features     )     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )     ,					[self.batch_size, self.image_size, 8, 8]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )     ,					len(config.out_features     )     )
        self.parent.assertListEqual(model.channels     ,					config.hidden_sizes[:-1]     )

        # verify backbone works with out_features=None
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   FocalNetBackbone(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps     )     ,					1     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )     ,					[self.batch_size, self.image_size * 2, 4, 4]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )     ,					1     )
        self.parent.assertListEqual(model.channels     ,					[config.hidden_sizes[-1]]     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetForMaskedImageModeling(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(
            result.reconstruction.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

        # test greyscale images
        _UpperCAmelCase          =   1
        _UpperCAmelCase          =   FocalNetForMaskedImageModeling(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

        _UpperCAmelCase          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(result.reconstruction.shape     ,					(self.batch_size, 1, self.image_size, self.image_size)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.type_sequence_label_size
        _UpperCAmelCase          =   FocalNetForImageClassification(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )

        # test greyscale images
        _UpperCAmelCase          =   1
        _UpperCAmelCase          =   FocalNetForImageClassification(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

        _UpperCAmelCase          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )







    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()

        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   config_and_inputs
        _UpperCAmelCase          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				str     =				(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    a      :				int     =				(
        {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    a      :				Optional[Any]     =				False

    a      :				Dict     =				False
    a      :				List[Any]     =				False
    a      :				int     =				False
    a      :				Optional[Any]     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					embed_dim=37     ,					has_text_modality=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Optional[int]:






        '''simple docstring'''






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







    def      _snake_case					(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        return







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase     )







    @unittest.skip(reason="""FocalNet does not use inputs_embeds"""     )
    def      _snake_case					(			self			:       List[Any]     )		->Any:






        '''simple docstring'''






        pass







    @unittest.skip(reason="""FocalNet does not use feedforward chunking"""     )
    def      _snake_case					(			self			:       Tuple     )		->List[str]:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase          =   model_class(__UpperCamelCase     )
            self.assertIsInstance(model.get_input_embeddings()     ,					(nn.Module)     )
            _UpperCAmelCase          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__UpperCamelCase     ,					nn.Linear     )     )







    def      _snake_case					(			self			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase          =   model_class(__UpperCamelCase     )
            _UpperCAmelCase          =   inspect.signature(model.forward     )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase          =   [*signature.parameters.keys()]

            _UpperCAmelCase          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,					__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   model_class(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()

        with torch.no_grad():
            _UpperCAmelCase          =   model(**self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     )     )

        _UpperCAmelCase          =   outputs.hidden_states

        _UpperCAmelCase          =   getattr(
            self.model_tester     ,					"""expected_num_hidden_layers"""     ,					len(self.model_tester.depths     ) + 1     )
        self.assertEqual(len(__UpperCamelCase     )     ,					__UpperCamelCase     )

        # FocalNet has a different seq_length
        _UpperCAmelCase          =   (
            config.patch_size
            if isinstance(config.patch_size     ,					collections.abc.Iterable     )
            else (config.patch_size, config.patch_size)
        )

        _UpperCAmelCase          =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]     )     ,					[num_patches, self.model_tester.embed_dim]     ,					)

        _UpperCAmelCase          =   outputs.reshaped_hidden_states
        self.assertEqual(len(__UpperCamelCase     )     ,					__UpperCamelCase     )

        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   reshaped_hidden_states[0].shape
        _UpperCAmelCase          =   (
            reshaped_hidden_states[0].view(__UpperCamelCase     ,					__UpperCamelCase     ,					height * width     ).permute(0     ,					2     ,					1     )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]     )     ,					[num_patches, self.model_tester.embed_dim]     ,					)







    def      _snake_case					(			self			:       Tuple     )		->str:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase          =   (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,					collections.abc.Iterable     )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase          =   True
            self.check_hidden_states_output(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase          =   True

            self.check_hidden_states_output(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->str:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase          =   3

        _UpperCAmelCase          =   (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,					collections.abc.Iterable     )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        _UpperCAmelCase          =   (
            config.patch_size
            if isinstance(config.patch_size     ,					collections.abc.Iterable     )
            else (config.patch_size, config.patch_size)
        )

        _UpperCAmelCase          =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        _UpperCAmelCase          =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase          =   True
            self.check_hidden_states_output(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					(padded_height, padded_width)     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase          =   True
            self.check_hidden_states_output(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					(padded_height, padded_width)     )







    @slow
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase          =   FocalNetModel.from_pretrained(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase          =   _config_zero_init(__UpperCamelCase     )
        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   model_class(config=__UpperCamelCase     )
            for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item()     ,					[0.0, 1.0]     ,					msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     ,					)







@require_vision
@require_torch
class 		a_						( unittest.TestCase	):







    @cached_property
    def      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""     ) if is_vision_available() else None







    @slow
    def      _snake_case					(			self			:       Optional[int]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   self.default_image_processor

        _UpperCAmelCase          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
        _UpperCAmelCase          =   image_processor(images=__UpperCamelCase     ,					return_tensors="""pt"""     ).to(__UpperCamelCase     )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase          =   model(**__UpperCamelCase     )

        # verify the logits
        _UpperCAmelCase          =   torch.Size((1, 10_00)     )
        self.assertEqual(outputs.logits.shape     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]     ).to(__UpperCamelCase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					__UpperCamelCase     ,					atol=1e-4     )     )
        self.assertTrue(outputs.logits.argmax(dim=-1     ).item()     ,					2_81     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(FocalNetBackbone,) if is_torch_available() else ()
    a      :				Tuple     =				FocalNetConfig

    a      :				Optional[Any]     =				False







    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   FocalNetModelTester(self     )

"""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)