"""simple docstring"""



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a					:	Optional[int]							=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a					:	Tuple							=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
 )

 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)


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





    """simple docstring"""

    _UpperCAmelCase          =   state_dict.pop(lowercase__      )
    _UpperCAmelCase          =   val


def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase          =   key.replace("""backbone.0.body"""					,      """backbone.conv_encoder.model"""      )
            _UpperCAmelCase          =   value
        else:
            _UpperCAmelCase          =   value

    return new_state_dict


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





    """simple docstring"""

    _UpperCAmelCase          =   """"""
    if is_panoptic:
        _UpperCAmelCase          =   """conditional_detr."""

    # first: transformer encoder
    for i in range(6      ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase          =   state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
        _UpperCAmelCase          =   state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""      )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase          =   in_proj_weight[:2_5_6, :]
        _UpperCAmelCase          =   in_proj_bias[:2_5_6]
        _UpperCAmelCase          =   in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase          =   in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase          =   in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase          =   in_proj_bias[-2_5_6:]


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    _UpperCAmelCase          =   Image.open(requests.get(lowercase__					,      stream=lowercase__      ).raw      )

    return im


@torch.no_grad()
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        _UpperCAmelCase          =   """resnet101"""
    if "dc5" in model_name:
        _UpperCAmelCase          =   True
    _UpperCAmelCase          =   """panoptic""" in model_name
    if is_panoptic:
        _UpperCAmelCase          =   2_5_0
    else:
        _UpperCAmelCase          =   9_1
        _UpperCAmelCase          =   """huggingface/label-files"""
        _UpperCAmelCase          =   """coco-detection-id2label.json"""
        _UpperCAmelCase          =   json.load(open(hf_hub_download(lowercase__					,      lowercase__					,      repo_type="""dataset"""      )					,      """r"""      )      )
        _UpperCAmelCase          =   {int(lowercase__      ): v for k, v in idalabel.items()}
        _UpperCAmelCase          =   idalabel
        _UpperCAmelCase          =   {v: k for k, v in idalabel.items()}

    # load image processor
    _UpperCAmelCase          =   """coco_panoptic""" if is_panoptic else """coco_detection"""
    _UpperCAmelCase          =   ConditionalDetrImageProcessor(format=lowercase__      )

    # prepare image
    _UpperCAmelCase          =   prepare_img()
    _UpperCAmelCase          =   image_processor(images=lowercase__					,      return_tensors="""pt"""      )
    _UpperCAmelCase          =   encoding["""pixel_values"""]

    logger.info(F"""Converting model {model_name}..."""      )

    # load original model from torch hub
    _UpperCAmelCase          =   torch.hub.load("""DeppMeng/ConditionalDETR"""					,      lowercase__					,      pretrained=lowercase__      ).eval()
    _UpperCAmelCase          =   conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            _UpperCAmelCase          =   """conditional_detr.""" + src
        rename_key(lowercase__					,      lowercase__					,      lowercase__      )
    _UpperCAmelCase          =   rename_backbone_keys(lowercase__      )
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowercase__					,      is_panoptic=lowercase__      )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase          =   """conditional_detr.model.""" if is_panoptic else """model."""
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith("""conditional_detr"""      )
                and not key.startswith("""class_labels_classifier"""      )
                and not key.startswith("""bbox_predictor"""      )
            ):
                _UpperCAmelCase          =   state_dict.pop(lowercase__      )
                _UpperCAmelCase          =   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _UpperCAmelCase          =   state_dict.pop(lowercase__      )
                _UpperCAmelCase          =   val
            elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
                continue
            else:
                _UpperCAmelCase          =   state_dict.pop(lowercase__      )
                _UpperCAmelCase          =   val
        else:
            if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
                _UpperCAmelCase          =   state_dict.pop(lowercase__      )
                _UpperCAmelCase          =   val
    # finally, create HuggingFace model and load state dict
    _UpperCAmelCase          =   ConditionalDetrForSegmentation(lowercase__      ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__      )
    model.load_state_dict(lowercase__      )
    model.eval()
    model.push_to_hub(repo_id=lowercase__					,      organization="""DepuMeng"""					,      commit_message="""Add model"""      )
    # verify our conversion
    _UpperCAmelCase          =   conditional_detr(lowercase__      )
    _UpperCAmelCase          =   model(lowercase__      )
    assert torch.allclose(outputs.logits					,      original_outputs["""pred_logits"""]					,      atol=1e-4      )
    assert torch.allclose(outputs.pred_boxes					,      original_outputs["""pred_boxes"""]					,      atol=1e-4      )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks					,      original_outputs["""pred_masks"""]					,      atol=1e-4      )

    # Save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
    Path(lowercase__      ).mkdir(exist_ok=lowercase__      )
    model.save_pretrained(lowercase__      )
    image_processor.save_pretrained(lowercase__      )


if __name__ == "__main__":
 a					:	int							=		argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''conditional_detr_resnet50''',
     type=str,
     help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 a					:	Dict							=		parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""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)