





'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=0.2      ,  _lowerCAmelCase=0.2		)     ->				List[Any]:
     _lowerCAmelCase						      =   bp_numa
     _lowerCAmelCase						      =   bp_numa
     _lowerCAmelCase						      =   bp_numa
     _lowerCAmelCase						      =   conva_get[:2]
     _lowerCAmelCase						      =   conva_get[2]
     _lowerCAmelCase						      =   size_pa
     _lowerCAmelCase						      =   rate_w
     _lowerCAmelCase						      =   rate_t
     _lowerCAmelCase						      =   [
         np.mat(-1 * np.random.rand(self.conva[0]      ,  self.conva[0]		) + 0.5		)
         for i in range(self.conva[1]		)
     ]
     _lowerCAmelCase						      =   np.mat(-1 * np.random.rand(self.num_bpa      ,  self.num_bpa		) + 0.5		)
     _lowerCAmelCase						      =   np.mat(-1 * np.random.rand(self.num_bpa      ,  self.num_bpa		) + 0.5		)
     _lowerCAmelCase						      =   -2 * np.random.rand(self.conva[1]		) + 1
     _lowerCAmelCase						      =   -2 * np.random.rand(self.num_bpa		) + 1
     _lowerCAmelCase						      =   -2 * np.random.rand(self.num_bpa		) + 1




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     # save model dict with pickle
     _lowerCAmelCase						      =   {
         "num_bp1": self.num_bpa,
         "num_bp2": self.num_bpa,
         "num_bp3": self.num_bpa,
         "conv1": self.conva,
         "step_conv1": self.step_conva,
         "size_pooling1": self.size_poolinga,
         "rate_weight": self.rate_weight,
         "rate_thre": self.rate_thre,
         "w_conv1": self.w_conva,
         "wkj": self.wkj,
         "vji": self.vji,
         "thre_conv1": self.thre_conva,
         "thre_bp2": self.thre_bpa,
         "thre_bp3": self.thre_bpa,
     }
     with open(_lowerCAmelCase      ,  "wb"		) as f:
         pickle.dump(_lowerCAmelCase      ,  _lowerCAmelCase		)

     print(f'''Model saved： {save_path}'''		)




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase		)     ->				Any:
     # read saved model
     with open(_lowerCAmelCase      ,  "rb"		) as f:
         _lowerCAmelCase						      =   pickle.load(_lowerCAmelCase		)  # noqa: S301

     _lowerCAmelCase						      =   model_dic.get("conv1"		)
     conv_get.append(model_dic.get("step_conv1"		)		)
     _lowerCAmelCase						      =   model_dic.get("size_pooling1"		)
     _lowerCAmelCase						      =   model_dic.get("num_bp1"		)
     _lowerCAmelCase						      =   model_dic.get("num_bp2"		)
     _lowerCAmelCase						      =   model_dic.get("num_bp3"		)
     _lowerCAmelCase						      =   model_dic.get("rate_weight"		)
     _lowerCAmelCase						      =   model_dic.get("rate_thre"		)
     # create model instance
     _lowerCAmelCase						      =   CNN(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     # modify model parameter
     _lowerCAmelCase						      =   model_dic.get("w_conv1"		)
     _lowerCAmelCase						      =   model_dic.get("wkj"		)
     _lowerCAmelCase						      =   model_dic.get("vji"		)
     _lowerCAmelCase						      =   model_dic.get("thre_conv1"		)
     _lowerCAmelCase						      =   model_dic.get("thre_bp2"		)
     _lowerCAmelCase						      =   model_dic.get("thre_bp3"		)
     return conv_ins




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     return 1 / (1 + np.exp(-1 * x		))




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     return round(_lowerCAmelCase      ,  3		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     # convolution process
     _lowerCAmelCase						      =   convs[0]
     _lowerCAmelCase						      =   convs[1]
     _lowerCAmelCase						      =   np.shape(_lowerCAmelCase		)[0]
     # get the data slice of original image data, data_focus
     _lowerCAmelCase						      =   []
     for i_focus in range(0      ,  size_data - size_conv + 1      ,  _lowerCAmelCase		):
         for j_focus in range(0      ,  size_data - size_conv + 1      ,  _lowerCAmelCase		):
             _lowerCAmelCase						      =   data[
                 i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
             ]
             data_focus.append(_lowerCAmelCase		)
        # calculate the feature map of every single kernel, and saved as list of matrix
     _lowerCAmelCase						      =   []
     _lowerCAmelCase						      =   int((size_data - size_conv) / conv_step + 1		)
     for i_map in range(_lowerCAmelCase		):
         _lowerCAmelCase						      =   []
         for i_focus in range(len(_lowerCAmelCase		)		):
             _lowerCAmelCase						      =   (
                 np.sum(np.multiply(data_focus[i_focus]      ,  w_convs[i_map]		)		)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(_lowerCAmelCase		)		)
         _lowerCAmelCase						      =   np.asmatrix(_lowerCAmelCase		).reshape(
             _lowerCAmelCase      ,  _lowerCAmelCase		)
         data_featuremap.append(_lowerCAmelCase		)

     # expanding the data slice to One dimenssion
     _lowerCAmelCase						      =   []
     for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
     return focus_list, data_featuremap




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase="average_pool"		)     ->				Any:
     # pooling process
     _lowerCAmelCase						      =   len(featuremaps[0]		)
     _lowerCAmelCase						      =   int(size_map / size_pooling		)
     _lowerCAmelCase						      =   []
     for i_map in range(len(_lowerCAmelCase		)		):
         _lowerCAmelCase						      =   featuremaps[i_map]
         _lowerCAmelCase						      =   []
         for i_focus in range(0      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
             for j_focus in range(0      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
                 _lowerCAmelCase						      =   feature_map[
                     i_focus : i_focus + size_pooling,
                     j_focus : j_focus + size_pooling,
                 ]
                 if pooling_type == "average_pool":
                     # average pooling
                     map_pooled.append(np.average(_lowerCAmelCase		)		)
                 elif pooling_type == "max_pooling":
                     # max pooling
                     map_pooled.append(np.max(_lowerCAmelCase		)		)
         _lowerCAmelCase						      =   np.asmatrix(_lowerCAmelCase		).reshape(_lowerCAmelCase      ,  _lowerCAmelCase		)
         featuremap_pooled.append(_lowerCAmelCase		)
     return featuremap_pooled




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     # expanding three dimension data to one dimension list
     _lowerCAmelCase						      =   []
     for i in range(len(_lowerCAmelCase		)		):
         _lowerCAmelCase						      =   np.shape(data[i]		)
         _lowerCAmelCase						      =   data[i].reshape(1      ,  shapes[0] * shapes[1]		)
         _lowerCAmelCase						      =   data_listed.getA().tolist()[0]
         data_expanded.extend(_lowerCAmelCase		)
     _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
     return data_expanded




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     # expanding matrix to one dimension list
     _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
     _lowerCAmelCase						      =   np.shape(_lowerCAmelCase		)
     _lowerCAmelCase						      =   data_mat.reshape(1      ,  shapes[0] * shapes[1]		)
     return data_expanded




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   []
     _lowerCAmelCase						      =   0
     for i_map in range(_lowerCAmelCase		):
         _lowerCAmelCase						      =   np.ones((size_map, size_map)		)
         for i in range(0      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
             for j in range(0      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
                 _lowerCAmelCase						      =   pd_pool[
                     i_pool
                 ]
                 _lowerCAmelCase						      =   i_pool + 1
         _lowerCAmelCase						      =   np.multiply(
             _lowerCAmelCase      ,  np.multiply(out_map[i_map]      ,  (1 - out_map[i_map])		)		)
         pd_all.append(_lowerCAmelCase		)
     return pd_all




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=bool		)     ->				Tuple:
     # model traning
     print("----------------------Start Training-------------------------"		)
     print((" - - Shape: Train_Data  ", np.shape(_lowerCAmelCase		))		)
     print((" - - Shape: Teach_Data  ", np.shape(_lowerCAmelCase		))		)
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   []
     _lowerCAmelCase						      =   10000
     while rp < n_repeat and mse >= error_accuracy:
         _lowerCAmelCase						      =   0
         print(f'''-------------Learning Time {rp}--------------'''		)
         for p in range(len(_lowerCAmelCase		)		):
             # print('------------Learning Image: %d--------------'%p)
             _lowerCAmelCase						      =   np.asmatrix(datas_train[p]		)
             _lowerCAmelCase						      =   np.asarray(datas_teach[p]		)
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.convolute(
                 _lowerCAmelCase      ,  self.conva      ,  self.w_conva      ,  self.thre_conva      ,  conv_step=self.step_conva      ,  )
             _lowerCAmelCase						      =   self.pooling(_lowerCAmelCase      ,  self.size_poolinga		)
             _lowerCAmelCase						      =   np.shape(_lowerCAmelCase		)
             _lowerCAmelCase						      =   self._expand(_lowerCAmelCase		)
             _lowerCAmelCase						      =   data_bp_input

             _lowerCAmelCase						      =   np.dot(_lowerCAmelCase      ,  self.vji.T		) - self.thre_bpa
             _lowerCAmelCase						      =   self.sig(_lowerCAmelCase		)
             _lowerCAmelCase						      =   np.dot(_lowerCAmelCase      ,  self.wkj.T		) - self.thre_bpa
             _lowerCAmelCase						      =   self.sig(_lowerCAmelCase		)

             # --------------Model Leaning ------------------------
             # calculate error and gradient---------------
             _lowerCAmelCase						      =   np.multiply(
                 (data_teach - bp_outa)      ,  np.multiply(_lowerCAmelCase      ,  (1 - bp_outa)		)		)
             _lowerCAmelCase						      =   np.multiply(
                 np.dot(_lowerCAmelCase      ,  self.wkj		)      ,  np.multiply(_lowerCAmelCase      ,  (1 - bp_outa)		)		)
             _lowerCAmelCase						      =   np.dot(_lowerCAmelCase      ,  self.vji		)

             _lowerCAmelCase						      =   pd_i_all / (self.size_poolinga * self.size_poolinga)
             _lowerCAmelCase						      =   pd_conva_pooled.T.getA().tolist()
             _lowerCAmelCase						      =   self._calculate_gradient_from_pool(
                 _lowerCAmelCase      ,  _lowerCAmelCase      ,  shape_featuremapa[0]      ,  shape_featuremapa[1]      ,  self.size_poolinga      ,  )
             # weight and threshold learning process---------
             # convolution layer
             for k_conv in range(self.conva[1]		):
                 _lowerCAmelCase						      =   self._expand_mat(pd_conva_all[k_conv]		)
                 _lowerCAmelCase						      =   self.rate_weight * np.dot(_lowerCAmelCase      ,  _lowerCAmelCase		)

                 _lowerCAmelCase						      =   self.w_conva[k_conv] + delta_w.reshape(
                     (self.conva[0], self.conva[0])		)

                 _lowerCAmelCase						      =   (
                     self.thre_conva[k_conv]
                     - np.sum(pd_conva_all[k_conv]		) * self.rate_thre
                 )
             # all connected layer
             _lowerCAmelCase						      =   self.wkj + pd_k_all.T * bp_outa * self.rate_weight
             _lowerCAmelCase						      =   self.vji + pd_j_all.T * bp_outa * self.rate_weight
             _lowerCAmelCase						      =   self.thre_bpa - pd_k_all * self.rate_thre
             _lowerCAmelCase						      =   self.thre_bpa - pd_j_all * self.rate_thre
             # calculate the sum error of all single image
             _lowerCAmelCase						      =   np.sum(abs(data_teach - bp_outa		)		)
             error_count += errors
             # print('   ----Teach      ',data_teach)
             # print('   ----BP_output  ',bp_out3)
         _lowerCAmelCase						      =   rp + 1
         _lowerCAmelCase						      =   error_count / patterns
         all_mse.append(_lowerCAmelCase		)

     def draw_error():
         _lowerCAmelCase						      =   [error_accuracy for i in range(int(n_repeat * 1.2		)		)]
         plt.plot(_lowerCAmelCase      ,  "+-"		)
         plt.plot(_lowerCAmelCase      ,  "r--"		)
         plt.xlabel("Learning Times"		)
         plt.ylabel("All_mse"		)
         plt.grid(_lowerCAmelCase      ,  alpha=0.5		)
         plt.show()

     print("------------------Training Complished---------------------"		)
     print((" - - Training epoch: ", rp, f'''     - - Mse: {mse:.6f}''')		)
     if draw_e:
         draw_error()
     return mse




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     # model predict
     _lowerCAmelCase						      =   []
     print("-------------------Start Testing-------------------------"		)
     print((" - - Shape: Test_Data  ", np.shape(_lowerCAmelCase		))		)
     for p in range(len(_lowerCAmelCase		)		):
         _lowerCAmelCase						      =   np.asmatrix(datas_test[p]		)
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.convolute(
             _lowerCAmelCase      ,  self.conva      ,  self.w_conva      ,  self.thre_conva      ,  conv_step=self.step_conva      ,  )
         _lowerCAmelCase						      =   self.pooling(_lowerCAmelCase      ,  self.size_poolinga		)
         _lowerCAmelCase						      =   self._expand(_lowerCAmelCase		)

         _lowerCAmelCase						      =   data_bp_input
         _lowerCAmelCase						      =   bp_outa * self.vji.T - self.thre_bpa
         _lowerCAmelCase						      =   self.sig(_lowerCAmelCase		)
         _lowerCAmelCase						      =   bp_outa * self.wkj.T - self.thre_bpa
         _lowerCAmelCase						      =   self.sig(_lowerCAmelCase		)
         produce_out.extend(bp_outa.getA().tolist()		)
     _lowerCAmelCase						      =   [list(map(self.do_round      ,  _lowerCAmelCase		)		) for each in produce_out]
     return np.asarray(_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Tuple:
     # return the data of image after convoluting process so we can check it out
     _lowerCAmelCase						      =   np.asmatrix(_lowerCAmelCase		)
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.convolute(
         _lowerCAmelCase      ,  self.conva      ,  self.w_conva      ,  self.thre_conva      ,  conv_step=self.step_conva      ,  )
     _lowerCAmelCase						      =   self.pooling(_lowerCAmelCase      ,  self.size_poolinga		)

     return data_conveda, data_pooleda


if __name__ == "__main__":
       pass









'''simple docstring'''


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

import requests


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





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

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict=None     ):



    '''simple docstring'''





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

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   None

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

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

    _lowerCAmelCase						      =   None
    if job_name and job_links:
        _lowerCAmelCase						      =   job_links.get(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

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

    return result


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []

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

    return errors


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       str=None     ):



    '''simple docstring'''





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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





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

    return test


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   [(x[0], x[1], get_model(x[2]     )) for x in logs]
    _lowerCAmelCase						      =   [x for x in logs if x[2] is not None]
    _lowerCAmelCase						      =   {x[2] for x in logs}

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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| no. | error | status |"
    _lowerCAmelCase						      =   "|-:|:-|:-|"
    _lowerCAmelCase						      =   [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase						      =   reduced_by_error[error]["count"]
        _lowerCAmelCase						      =   F'''| {count} | {error[:100]} |  |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| model | no. of errors | major error | count |"
    _lowerCAmelCase						      =   "|-:|-:|-:|-:|"
    _lowerCAmelCase						      =   [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase						      =   reduced_by_model[model]["count"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   list(reduced_by_model[model]["errors"].items()     )[0]
        _lowerCAmelCase						      =   F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				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.")
       _SCREAMING_SNAKE_CASE									=				parser.parse_args()

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

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

       _SCREAMING_SNAKE_CASE									=				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)

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

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

       # print the top 30 most common test errors
       _SCREAMING_SNAKE_CASE									=				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)

       _SCREAMING_SNAKE_CASE									=				reduce_by_error(errors)
       _SCREAMING_SNAKE_CASE									=				reduce_by_model(errors)

       _SCREAMING_SNAKE_CASE									=				make_github_table(reduced_by_error)
       _SCREAMING_SNAKE_CASE									=				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)


