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

import requests


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      SCREAMING_SNAKE_CASE_          =  None
      if token is not None:
            SCREAMING_SNAKE_CASE_          =  {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

            for i in range(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  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      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      SCREAMING_SNAKE_CASE_          =  None
      if token is not None:
            SCREAMING_SNAKE_CASE_          =  {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

            for i in range(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  None
      if token is not None:
            SCREAMING_SNAKE_CASE_          =  {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  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:
                                          SCREAMING_SNAKE_CASE_          =  line.decode("UTF-8"				).strip()
                                          if filename == "failures_line.txt":
                                                try:
                                                      # `error_line` is the place where `error` occurs
                                                      SCREAMING_SNAKE_CASE_          =  line[: line.index(": "				)]
                                                      SCREAMING_SNAKE_CASE_          =  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
                                                SCREAMING_SNAKE_CASE_          =  line[len("FAILED "				) :]
                                                failed_tests.append(__UpperCamelCase				)
                                          elif filename == "job_name.txt":
                                                SCREAMING_SNAKE_CASE_          =  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."				)

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

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

      return result


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      SCREAMING_SNAKE_CASE_          =  []

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

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


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  test.split("::"				)[0]
      if test.startswith("tests/models/"				):
            SCREAMING_SNAKE_CASE_          =  test.split("/"				)[2]
      else:
            SCREAMING_SNAKE_CASE_          =  None

      return test


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      SCREAMING_SNAKE_CASE_          =  [(x[0], x[1], get_model(x[2]				)) for x in logs]
      SCREAMING_SNAKE_CASE_          =  [x for x in logs if x[2] is not None]
      SCREAMING_SNAKE_CASE_          =  {x[2] for x in logs}

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

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


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  "| no. | error | status |"
      SCREAMING_SNAKE_CASE_          =  "|-:|:-|:-|"
      SCREAMING_SNAKE_CASE_          =  [header, sep]
      for error in reduced_by_error:
            SCREAMING_SNAKE_CASE_          =  reduced_by_error[error]["count"]
            SCREAMING_SNAKE_CASE_          =  F'''| {count} | {error[:1_0_0]} |  |'''
            lines.append(__UpperCamelCase				)

      return "\n".join(__UpperCamelCase				)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  "| model | no. of errors | major error | count |"
      SCREAMING_SNAKE_CASE_          =  "|-:|-:|-:|-:|"
      SCREAMING_SNAKE_CASE_          =  [header, sep]
      for model in reduced_by_model:
            SCREAMING_SNAKE_CASE_          =  reduced_by_model[model]["count"]
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  list(reduced_by_model[model]["errors"].items()				)[0]
            SCREAMING_SNAKE_CASE_          =  F'''| {model} | {count} | {error[:6_0]} | {_count} |'''
            lines.append(__UpperCamelCase				)

      return "\n".join(__UpperCamelCase				)


if __name__ == "__main__":
      A     :  Any					     =       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     :  Optional[int]					     =       parser.parse_args()

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

      A     :  int					     =       get_job_links(args.workflow_run_id, token=args.token)
      A     :  int					     =       {}
      # 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     :  Tuple					     =       k.find(" / ")
                        A     :  str					     =       k[index + len(" / ") :]
                  A     :  Any					     =       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     :  Optional[int]					     =       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     :  Dict					     =       get_all_errors(args.output_dir, job_links=job_links)

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

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

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

      A     :  Optional[Any]					     =       reduce_by_error(errors)
      A     :  Optional[Any]					     =       reduce_by_model(errors)

      A     :  Optional[int]					     =       make_github_table(reduced_by_error)
      A     :  str					     =       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)



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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A     :  Optional[Any]					     =       logging.get_logger(__name__)

A     :  List[Any]					     =       torch.device("cpu")


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      return im


def      a__ (		__UpperCamelCase				):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]				)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]				)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]				)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  dct.pop(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  val


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      for k in state_dict.keys():
            SCREAMING_SNAKE_CASE_          =  k
            if ".pwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".pwconv"     ,      ".point_wise_conv"				)
            if ".dwconv" in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".dwconv"     ,      ".depth_wise_conv"				)
            if ".Proj." in k:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace(".Proj."     ,      ".proj."				)
            if "patch_embed" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.replace("patch_embed"     ,      "swiftformer.patch_embed.patch_embedding"				)
            if "network" in k_new:
                  SCREAMING_SNAKE_CASE_          =  k_new.split("."				)
                  if ls[2].isdigit():
                        SCREAMING_SNAKE_CASE_          =  "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]				)
                  else:
                        SCREAMING_SNAKE_CASE_          =  k_new.replace("network"     ,      "swiftformer.encoder.network"				)
            rename_keys.append((k, k_new)				)
      return rename_keys


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 6, 4]
            SCREAMING_SNAKE_CASE_          =  [4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 9, 6]
            SCREAMING_SNAKE_CASE_          =  [4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            SCREAMING_SNAKE_CASE_          =  [4, 3, 1_0, 5]
            SCREAMING_SNAKE_CASE_          =  [4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE_          =  [4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE_          =  [6_4, 1_2_8, 3_2_0, 5_1_2]

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith("https"				):
                  SCREAMING_SNAKE_CASE_          =  torch.hub.load_state_dict_from_url(__UpperCamelCase     ,      map_location="cpu"     ,      check_hash=__UpperCamelCase				)
            else:
                  SCREAMING_SNAKE_CASE_          =  torch.load(__UpperCamelCase     ,      map_location="cpu"				)
      SCREAMING_SNAKE_CASE_          =  checkpoint

      SCREAMING_SNAKE_CASE_          =  create_rename_keys(__UpperCamelCase				)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_          =  SwiftFormerForImageClassification(__UpperCamelCase				).eval()
      hf_model.load_state_dict(__UpperCamelCase				)

      # prepare test inputs
      SCREAMING_SNAKE_CASE_          =  prepare_img()
      SCREAMING_SNAKE_CASE_          =  ViTImageProcessor.from_pretrained("preprocessor_config"				)
      SCREAMING_SNAKE_CASE_          =  processor(images=__UpperCamelCase     ,      return_tensors="pt"				)

      # compare outputs from both models
      SCREAMING_SNAKE_CASE_          =  get_expected_output(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  hf_model(inputs["pixel_values"]				).logits

      assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
      assert torch.allclose(hf_logits[0, 0:5]     ,      __UpperCamelCase     ,      atol=1E-3				)

      Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
      print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''				)
      hf_model.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
      A     :  Optional[Any]					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swiftformer_name",
          default="swiftformer_xs",
          choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
          type=str,
          help="Name of the SwiftFormer model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="./converted_outputs/",
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

      A     :  List[Any]					     =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


