






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_    =							logging.get_logger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->YolosConfig:
						A__						:  Optional[int]		  =  YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												A__						:  List[str]		  =  1_9_2
												A__						:  List[Any]		  =  7_6_8
												A__						:  List[Any]		  =  1_2
												A__						:  Tuple		  =  3
												A__						:  Optional[Any]		  =  [8_0_0, 1_3_3_3]
												A__						:  str		  =  False
						elif yolos_name == "yolos_s_dWr":
												A__						:  str		  =  3_3_0
												A__						:  str		  =  1_4
												A__						:  Tuple		  =  6
												A__						:  Any		  =  1_3_2_0
						elif "yolos_s" in yolos_name:
												A__						:  int		  =  3_8_4
												A__						:  str		  =  1_5_3_6
												A__						:  Optional[int]		  =  1_2
												A__						:  Optional[int]		  =  6
						elif "yolos_b" in yolos_name:
												A__						:  Union[str, Any]		  =  [8_0_0, 1_3_4_4]

						A__						:  Union[str, Any]		  =  9_1
						A__						:  List[str]		  =  """huggingface/label-files"""
						A__						:  List[str]		  =  """coco-detection-id2label.json"""
						A__						:  Any		  =  json.load(open(hf_hub_download(UpperCAmelCase__,						UpperCAmelCase__,						repo_type="""dataset"""      ),						"""r"""      )      )
						A__						:  Optional[Any]		  =  {int(UpperCAmelCase__      ): v for k, v in idalabel.items()}
						A__						:  Tuple		  =  idalabel
						A__						:  Optional[Any]		  =  {v: k for k, v in idalabel.items()}

						return config




def      _lowerCAmelCase		(							UpperCAmelCase__       :					dict,						UpperCAmelCase__       :					YolosConfig,						UpperCAmelCase__       :					bool = False      )		->Dict:
						for i in range(config.num_hidden_layers      ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												A__						:  Union[str, Any]		  =  state_dict.pop(f'blocks.{i}.attn.qkv.weight'      )
												A__						:  Union[str, Any]		  =  state_dict.pop(f'blocks.{i}.attn.qkv.bias'      )
												# next, add query, keys and values (in that order) to the state dict
												A__						:  List[Any]		  =  in_proj_weight[: config.hidden_size, :]
												A__						:  str		  =  in_proj_bias[: config.hidden_size]
												A__						:  Optional[Any]		  =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												A__						:  Tuple		  =  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												A__						:  Union[str, Any]		  =  in_proj_weight[-config.hidden_size :, :]
												A__						:  str		  =  in_proj_bias[-config.hidden_size :]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->str:
						if "backbone" in name:
												A__						:  Optional[Any]		  =  name.replace("""backbone""",						"""vit"""      )
						if "cls_token" in name:
												A__						:  List[str]		  =  name.replace("""cls_token""",						"""embeddings.cls_token"""      )
						if "det_token" in name:
												A__						:  Dict		  =  name.replace("""det_token""",						"""embeddings.detection_tokens"""      )
						if "mid_pos_embed" in name:
												A__						:  List[str]		  =  name.replace("""mid_pos_embed""",						"""encoder.mid_position_embeddings"""      )
						if "pos_embed" in name:
												A__						:  Union[str, Any]		  =  name.replace("""pos_embed""",						"""embeddings.position_embeddings"""      )
						if "patch_embed.proj" in name:
												A__						:  Union[str, Any]		  =  name.replace("""patch_embed.proj""",						"""embeddings.patch_embeddings.projection"""      )
						if "blocks" in name:
												A__						:  Union[str, Any]		  =  name.replace("""blocks""",						"""encoder.layer"""      )
						if "attn.proj" in name:
												A__						:  Optional[Any]		  =  name.replace("""attn.proj""",						"""attention.output.dense"""      )
						if "attn" in name:
												A__						:  Optional[int]		  =  name.replace("""attn""",						"""attention.self"""      )
						if "norm1" in name:
												A__						:  Any		  =  name.replace("""norm1""",						"""layernorm_before"""      )
						if "norm2" in name:
												A__						:  List[Any]		  =  name.replace("""norm2""",						"""layernorm_after"""      )
						if "mlp.fc1" in name:
												A__						:  List[Any]		  =  name.replace("""mlp.fc1""",						"""intermediate.dense"""      )
						if "mlp.fc2" in name:
												A__						:  Any		  =  name.replace("""mlp.fc2""",						"""output.dense"""      )
						if "class_embed" in name:
												A__						:  Dict		  =  name.replace("""class_embed""",						"""class_labels_classifier"""      )
						if "bbox_embed" in name:
												A__						:  List[str]		  =  name.replace("""bbox_embed""",						"""bbox_predictor"""      )
						if "vit.norm" in name:
												A__						:  Any		  =  name.replace("""vit.norm""",						"""vit.layernorm"""      )

						return name




def      _lowerCAmelCase		(							UpperCAmelCase__       :					dict,						UpperCAmelCase__       :					YolosForObjectDetection      )		->dict:
						for key in orig_state_dict.copy().keys():
												A__						:  List[Any]		  =  orig_state_dict.pop(UpperCAmelCase__      )

												if "qkv" in key:
																		A__						:  int		  =  key.split("""."""      )
																		A__						:  Any		  =  int(key_split[2]      )
																		A__						:  Tuple		  =  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
																		if "weight" in key:
																								A__						:  List[str]		  =  val[:dim, :]
																								A__						:  Tuple		  =  val[
																								    dim : dim * 2, :
																								]
																								A__						:  List[str]		  =  val[-dim:, :]
																		else:
																								A__						:  Dict		  =  val[:dim]
																								A__						:  Optional[Any]		  =  val[dim : dim * 2]
																								A__						:  List[str]		  =  val[-dim:]
												else:
																		A__						:  Optional[Any]		  =  val

						return orig_state_dict




def      _lowerCAmelCase		(							)		->torch.Tensor:
						A__						:  int		  =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__						:  str		  =  Image.open(requests.get(UpperCAmelCase__,						stream=UpperCAmelCase__      ).raw      )
						return im




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					bool = False      )		->List[str]:
						A__						:  Union[str, Any]		  =  get_yolos_config(UpperCAmelCase__      )

						# load original state_dict
						A__						:  Tuple		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )["""model"""]

						# load 🤗 model
						A__						:  Tuple		  =  YolosForObjectDetection(UpperCAmelCase__      )
						model.eval()
						A__						:  Optional[Any]		  =  convert_state_dict(UpperCAmelCase__,						UpperCAmelCase__      )
						model.load_state_dict(UpperCAmelCase__      )

						# Check outputs on an image, prepared by YolosImageProcessor
						A__						:  Dict		  =  8_0_0 if yolos_name != """yolos_ti""" else 5_1_2
						A__						:  Optional[Any]		  =  YolosImageProcessor(format="""coco_detection""",						size=UpperCAmelCase__      )
						A__						:  int		  =  image_processor(images=prepare_img(),						return_tensors="""pt"""      )
						A__						:  Tuple		  =  model(**UpperCAmelCase__      )
						A__						:  List[Any]		  =  outputs.logits, outputs.pred_boxes

						A__						:  Dict		  =  None, None
						if yolos_name == "yolos_ti":
												A__						:  List[Any]		  =  torch.tensor(
												    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]      )
												A__						:  Union[str, Any]		  =  torch.tensor(
												    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]      )
						elif yolos_name == "yolos_s_200_pre":
												A__						:  Any		  =  torch.tensor(
												    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      )
												A__						:  Dict		  =  torch.tensor(
												    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]      )
						elif yolos_name == "yolos_s_300_pre":
												A__						:  List[str]		  =  torch.tensor(
												    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]      )
												A__						:  Optional[int]		  =  torch.tensor(
												    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]      )
						elif yolos_name == "yolos_s_dWr":
												A__						:  Tuple		  =  torch.tensor(
												    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]      )
												A__						:  Tuple		  =  torch.tensor(
												    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]      )
						elif yolos_name == "yolos_base":
												A__						:  Union[str, Any]		  =  torch.tensor(
												    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]      )
												A__						:  Union[str, Any]		  =  torch.tensor(
												    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]      )
						else:
												raise ValueError(f'Unknown yolos_name: {yolos_name}'      )

						assert torch.allclose(logits[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      )
						assert torch.allclose(pred_boxes[0, :3, :3],						UpperCAmelCase__,						atol=1e-4      )

						Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
						print(f'Saving model {yolos_name} to {pytorch_dump_folder_path}'      )
						model.save_pretrained(UpperCAmelCase__      )
						print(f'Saving image processor to {pytorch_dump_folder_path}'      )
						image_processor.save_pretrained(UpperCAmelCase__      )

						if push_to_hub:
												A__						:  int		  =  {
												    """yolos_ti""": """yolos-tiny""",
												    """yolos_s_200_pre""": """yolos-small""",
												    """yolos_s_300_pre""": """yolos-small-300""",
												    """yolos_s_dWr""": """yolos-small-dwr""",
												    """yolos_base""": """yolos-base""",
												}

												print("""Pushing to the hub..."""      )
												A__						:  Any		  =  model_mapping[yolos_name]
												image_processor.push_to_hub(UpperCAmelCase__,						organization="""hustvl"""      )
												model.push_to_hub(UpperCAmelCase__,						organization="""hustvl"""      )


if __name__ == "__main__":
		A_    =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--yolos_name''',
		    default='''yolos_s_200_pre''',
		    type=str,
		    help=(
		        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
		        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
		    ),
		)
		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		A_    =							parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














"""simple docstring"""



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

import requests




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

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

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  str		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).json()
																		job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )

												return job_links
						except Exception:
												print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'      )

						return {}




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

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

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

												for i in range(UpperCAmelCase__      ):
																		A__						:  Union[str, Any]		  =  requests.get(url + f'&page={i + 2}',						headers=UpperCAmelCase__      ).json()
																		artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )

												return artifacts
						except Exception:
												print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}'      )

						return {}




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

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




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

						with zipfile.ZipFile(UpperCAmelCase__      ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(UpperCAmelCase__      ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(UpperCAmelCase__      ) as f:
																																				for line in f:
																																										A__						:  int		  =  line.decode("""UTF-8"""      ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						A__						:  List[str]		  =  line[: line.index(""": """      )]
																																																						A__						:  str		  =  line[line.index(""": """      ) + len(""": """      ) :]
																																																						errors.append([error_line, error]      )
																																																except Exception:
																																																						# skip un-related lines
																																																						pass
																																										elif filename == "summary_short.txt" and line.startswith("""FAILED """      ):
																																																# `test` is the test method that failed
																																																A__						:  Any		  =  line[len("""FAILED """      ) :]
																																																failed_tests.append(UpperCAmelCase__      )
																																										elif filename == "job_name.txt":
																																																A__						:  Any		  =  line

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

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

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

						return result




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

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

						return errors




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

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




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

						return test




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

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

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




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

						return "\n".join(UpperCAmelCase__      )




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

						return "\n".join(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
						parser.add_argument(
						    '''--output_dir''',
						    type=str,
						    required=True,
						    help='''Where to store the downloaded artifacts and other result files.''',
						)
						parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
						A_    =							parser.parse_args()

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

						A_    =							get_job_links(args.workflow_run_id, token=args.token)
						A_    =							{}
						# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
						# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
						if _job_links:
												for k, v in _job_links.items():
																		# This is how GitHub actions combine job names.
																		if " / " in k:
																								A_    =							k.find(''' / ''')
																								A_    =							k[index + len(''' / ''') :]
																		A_    =							v
						with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(job_links, fp, ensure_ascii=False, indent=4)

						A_    =							get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						for idx, (name, url) in enumerate(artifacts.items()):
												download_artifact(name, url, args.output_dir, args.token)
												# Be gentle to GitHub
												time.sleep(1)

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

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

						# print the top 30 most common test errors
						A_    =							counter.most_common(30)
						for item in most_common:
												print(item)

						with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
												json.dump(errors, fp, ensure_ascii=False, indent=4)

						A_    =							reduce_by_error(errors)
						A_    =							reduce_by_model(errors)

						A_    =							make_github_table(reduced_by_error)
						A_    =							make_github_table_per_model(reduced_by_model)

						with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
												fp.write(sa)
						with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
												fp.write(sa)






