





"""simple docstring"""





import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


SCREAMING_SNAKE_CASE__					=   WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      lowerCAmelCase  						=       test_results.split(""" """			)

      lowerCAmelCase  						=       0
      lowerCAmelCase  						=       0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      lowerCAmelCase  						=       expressions[-2] if """=""" in expressions[-1] else expressions[-1]

      for i, expression in enumerate(SCREAMING_SNAKE_CASE			):
            if "failed" in expression:
                  failed += int(expressions[i - 1]			)
            if "passed" in expression:
                  success += int(expressions[i - 1]			)

      return failed, success, time_spent


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       {}
      lowerCAmelCase  						=       None
      lowerCAmelCase  						=       False
      for line in failures_short_lines.split("""\n"""			):
            if re.search(R"""_ \[doctest\]"""							,						SCREAMING_SNAKE_CASE			):
                  lowerCAmelCase  						=       True
                  lowerCAmelCase  						=       line.split(""" """			)[2]
            elif in_error and not line.split(""" """			)[0].isdigit():
                  lowerCAmelCase  						=       line
                  lowerCAmelCase  						=       False

      return failures

class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       title

            lowerCAmelCase  						=       doc_test_results["""time_spent"""].split(""",""" )[0]
            lowerCAmelCase  						=       doc_test_results["""success"""]
            lowerCAmelCase  						=       doc_test_results["""failures"""]
            lowerCAmelCase  						=       self.n_success + self.n_failures

            # Failures and success of the modeling tests
            lowerCAmelCase  						=       doc_test_results





      @property
      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       [self._time_spent]
            lowerCAmelCase  						=       0

            for time in time_spent:
                  lowerCAmelCase  						=       time.split(""":""" )

                  # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                  if len(lowercase ) == 1:
                        lowerCAmelCase  						=       [0, 0, time_parts[0]]

                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
                  total_secs += hours * 3_600 + minutes * 60 + seconds

            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
            return f'{int(lowercase )}h{int(lowercase )}m{int(lowercase )}s'





      @property
      def     _snake_case			(       self )		->     Dict:
            return {"type": "header", "text": {"type": "plain_text", "text": self.title}}





      @property
      def     _snake_case			(       self )		->     Dict:
            return {
                "type": "section",
                "text": {
                    "type": "plain_text",
                    "text": f'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
                    "emoji": True,
                },
                "accessory": {
                    "type": "button",
                    "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                    "url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                },
            }





      @property
      def     _snake_case			(       self )		->     Dict:
            return {
                "type": "section",
                "text": {
                    "type": "plain_text",
                    "text": (
                        f'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
                        f' {self.time}.'
                    ),
                    "emoji": True,
                },
                "accessory": {
                    "type": "button",
                    "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                    "url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                },
            }





      @property
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       40
            lowerCAmelCase  						=       {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(lowercase      ,						lowercase )}

            lowerCAmelCase  						=       """"""
            for category, failures in category_failures.items():
                  if len(lowercase ) == 0:
                        continue

                  if report != "":
                        report += "\n\n"

                  report += f'*{category} failures*:'.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
                  report += "`"
                  report += "`\n`".join(lowercase )
                  report += "`"

            return {
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": f'The following examples had failures:\n\n\n{report}\n',
                },
            }





      @property
      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       [self.header]

            if self.n_failures > 0:
                  blocks.append(self.failures )

            if self.n_failures > 0:
                  blocks.extend([self.category_failures] )

            if self.n_failures == 0:
                  blocks.append(self.no_failures )

            return json.dumps(lowercase )





      @staticmethod
      def     _snake_case			(       )		->     int:
            lowerCAmelCase  						=       [
                {
                    """type""": """section""",
                    """text""": {
                        """type""": """plain_text""",
                        """text""": """There was an issue running the tests.""",
                    },
                    """accessory""": {
                        """type""": """button""",
                        """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
                        """url""": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                    },
                }
            ]

            print("""Sending the following payload""" )
            print(json.dumps({"""blocks""": json.loads(lowercase )} ) )

            client.chat_postMessage(
                channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]      ,						text="""There was an issue running the tests."""      ,						blocks=lowercase      ,						)





      def     _snake_case			(       self )		->     Union[str, Any]:
            print("""Sending the following payload""" )
            print(json.dumps({"""blocks""": json.loads(self.payload )} ) )

            lowerCAmelCase  						=       f'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else """All tests passed."""

            lowerCAmelCase  						=       client.chat_postMessage(
                channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]      ,						blocks=self.payload      ,						text=lowercase      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     int:
            lowerCAmelCase  						=       """"""
            for key, value in failures.items():
                  lowerCAmelCase  						=       value[:200] + """ [Truncated]""" if len(lowercase ) > 250 else value
                  failures_text += f'*{key}*\n_{value}_\n\n'

            lowerCAmelCase  						=       job_name
            lowerCAmelCase  						=       {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

            if job_link is not None:
                  lowerCAmelCase  						=       {
                      """type""": """button""",
                      """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
                      """url""": job_link,
                  }

            return [
                {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
                content,
                {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
            ]





      def     _snake_case			(       self )		->     List[str]:
            if self.thread_ts is None:
                  raise ValueError("""Can only post reply if a post has been made.""" )

            lowerCAmelCase  						=       self.doc_test_results.pop("""job_link""" )
            self.doc_test_results.pop("""failures""" )
            self.doc_test_results.pop("""success""" )
            self.doc_test_results.pop("""time_spent""" )

            lowerCAmelCase  						=       sorted(self.doc_test_results.items()      ,						key=lambda lowercase : t[0] )
            for job, job_result in sorted_dict:
                  if len(job_result["""failures"""] ):
                        lowerCAmelCase  						=       f'*Num failures* :{len(job_result["failed"] )} \n'
                        lowerCAmelCase  						=       job_result["""failures"""]
                        lowerCAmelCase  						=       self.get_reply_blocks(lowercase      ,						lowercase      ,						lowercase      ,						text=lowercase )

                        print("""Sending the following reply""" )
                        print(json.dumps({"""blocks""": blocks} ) )

                        client.chat_postMessage(
                            channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]      ,						text=f'Results for {job}'      ,						blocks=lowercase      ,						thread_ts=self.thread_ts["""ts"""]      ,						)

                        time.sleep(1 )







def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       os.environ["""GITHUB_RUN_ID"""]
      lowerCAmelCase  						=       F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
      lowerCAmelCase  						=       requests.get(SCREAMING_SNAKE_CASE			).json()
      lowerCAmelCase  						=       {}

      try:
            jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}			)
            lowerCAmelCase  						=       math.ceil((result["""total_count"""] - 1_00) / 1_00			)

            for i in range(SCREAMING_SNAKE_CASE			):
                  lowerCAmelCase  						=       requests.get(url + F'&page={i + 2}'			).json()
                  jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}			)

            return jobs
      except Exception as e:
            print("""Unknown error, could not fetch links."""							,						SCREAMING_SNAKE_CASE			)

      return {}


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      lowerCAmelCase  						=       {}

      if os.path.exists(SCREAMING_SNAKE_CASE			):
            lowerCAmelCase  						=       os.listdir(SCREAMING_SNAKE_CASE			)
            for file in files:
                  try:
                        with open(os.path.join(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						encoding="""utf-8"""			) as f:
                              lowerCAmelCase  						=       f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(F'Could not open {os.path.join(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)}.'			) from e

      return _artifact


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      class 		lowercase					:





            def __init__(       self      ,						lowercase )		->     Optional[int]:
                  lowerCAmelCase  						=       name
                  lowerCAmelCase  						=       []





            def __str__(       self )		->     Union[str, Any]:
                  return self.name





            def     _snake_case			(       self      ,						lowercase )		->     Optional[Any]:
                  self.paths.append({"""name""": self.name, """path""": path} )







      lowerCAmelCase  						=       {}

      lowerCAmelCase  						=       filter(os.path.isdir							,						os.listdir()			)
      for directory in directories:
            lowerCAmelCase  						=       directory
            if artifact_name not in _available_artifacts:
                  lowerCAmelCase  						=       Artifact(SCREAMING_SNAKE_CASE			)

                  _available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE			)

      return _available_artifacts


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   get_job_links()
       SCREAMING_SNAKE_CASE__					=   retrieve_available_artifacts()

       SCREAMING_SNAKE_CASE__					=   collections.OrderedDict(
           [
               ("*.py", "API Examples"),
               ("*.md", "MD Examples"),
           ]
       )

       # This dict will contain all the information relative to each doc test category:
       # - failed: list of failed tests
       # - failures: dict in the format 'test': 'error_message'
       SCREAMING_SNAKE_CASE__					=   {
           v: {
               "failed": [],
               "failures": {},
           }
           for v in docs.values()
       }

       # Link to the GitHub Action job
       SCREAMING_SNAKE_CASE__					=   github_actions_job_links.get("run_doctests")

       SCREAMING_SNAKE_CASE__					=   available_artifacts["doc_tests_gpu_test_reports"].paths[0]
       SCREAMING_SNAKE_CASE__					=   retrieve_artifact(artifact_path["name"])
       if "stats" in artifact:
              SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__					=   handle_test_results(artifact["stats"])
              SCREAMING_SNAKE_CASE__					=   failed
              SCREAMING_SNAKE_CASE__					=   success
              SCREAMING_SNAKE_CASE__					=   time_spent[1:-1] + ", "

              SCREAMING_SNAKE_CASE__					=   extract_first_line_failure(artifact["failures_short"])
              for line in artifact["summary_short"].split("\n"):
                     if re.search("FAILED", line):
                            SCREAMING_SNAKE_CASE__					=   line.replace("FAILED ", "")
                            SCREAMING_SNAKE_CASE__					=   line.split()[0].replace("\n", "")

                            if "::" in line:
                                   SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__					=   line.split("::")
                            else:
                                   SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__					=   line, line

                            for file_regex in docs.keys():
                                   if fnmatch(file_path, file_regex):
                                          SCREAMING_SNAKE_CASE__					=   docs[file_regex]
                                          doc_test_results[category]["failed"].append(test)

                                          SCREAMING_SNAKE_CASE__					=   all_failures[test] if test in all_failures else "N/A"
                                          SCREAMING_SNAKE_CASE__					=   failure
                                          break

       SCREAMING_SNAKE_CASE__					=   Message("🤗 Results of the doc tests.", doc_test_results)
       message.post()
       message.post_reply()













"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       OrderedDict()
      for key, value in state_dict.items():
            if key.startswith("""module.encoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.encoder"""							,						"""glpn.encoder"""			)
            if key.startswith("""module.decoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.decoder"""							,						"""decoder.stages"""			)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase  						=       key[key.find("""patch_embed"""			) + len("""patch_embed"""			)]
                  lowerCAmelCase  						=       key.replace(F'patch_embed{idx}'							,						F'patch_embeddings.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "norm" in key:
                  lowerCAmelCase  						=       key.replace("""norm"""							,						"""layer_norm"""			)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase  						=       key[key.find("""glpn.encoder.layer_norm"""			) + len("""glpn.encoder.layer_norm"""			)]
                  lowerCAmelCase  						=       key.replace(F'layer_norm{idx}'							,						F'layer_norm.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "layer_norm1" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm1"""							,						"""layer_norm_1"""			)
            if "layer_norm2" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm2"""							,						"""layer_norm_2"""			)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase  						=       key[key.find("""block"""			) + len("""block"""			)]
                  lowerCAmelCase  						=       key.replace(F'block{idx}'							,						F'block.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "attn.q" in key:
                  lowerCAmelCase  						=       key.replace("""attn.q"""							,						"""attention.self.query"""			)
            if "attn.proj" in key:
                  lowerCAmelCase  						=       key.replace("""attn.proj"""							,						"""attention.output.dense"""			)
            if "attn" in key:
                  lowerCAmelCase  						=       key.replace("""attn"""							,						"""attention.self"""			)
            if "fc1" in key:
                  lowerCAmelCase  						=       key.replace("""fc1"""							,						"""dense1"""			)
            if "fc2" in key:
                  lowerCAmelCase  						=       key.replace("""fc2"""							,						"""dense2"""			)
            if "linear_pred" in key:
                  lowerCAmelCase  						=       key.replace("""linear_pred"""							,						"""classifier"""			)
            if "linear_fuse" in key:
                  lowerCAmelCase  						=       key.replace("""linear_fuse.conv"""							,						"""linear_fuse"""			)
                  lowerCAmelCase  						=       key.replace("""linear_fuse.bn"""							,						"""batch_norm"""			)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase  						=       key[key.find("""linear_c"""			) + len("""linear_c"""			)]
                  lowerCAmelCase  						=       key.replace(F'linear_c{idx}'							,						F'linear_c.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "bot_conv" in key:
                  lowerCAmelCase  						=       key.replace("""bot_conv"""							,						"""0.convolution"""			)
            if "skip_conv1" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv1"""							,						"""1.convolution"""			)
            if "skip_conv2" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv2"""							,						"""2.convolution"""			)
            if "fusion1" in key:
                  lowerCAmelCase  						=       key.replace("""fusion1"""							,						"""1.fusion"""			)
            if "fusion2" in key:
                  lowerCAmelCase  						=       key.replace("""fusion2"""							,						"""2.fusion"""			)
            if "fusion3" in key:
                  lowerCAmelCase  						=       key.replace("""fusion3"""							,						"""3.fusion"""			)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase  						=       key.replace("""conv"""							,						"""convolutional_layer"""			)
            if key.startswith("""module.last_layer_depth"""			):
                  lowerCAmelCase  						=       key.replace("""module.last_layer_depth"""							,						"""head.head"""			)
            lowerCAmelCase  						=       value

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      for i in range(config.num_encoder_blocks			):
            for j in range(config.depths[i]			):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'			)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




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

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]							,						decoder_hidden_size=64							,						depths=[3, 8, 27, 3]			)

      # load image processor (only resize + rescale)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

      logger.info("""Converting model..."""			)

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       rename_keys(SCREAMING_SNAKE_CASE			)

      # key and value matrices need special treatment
      read_in_k_v(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # create HuggingFace model and load state dict
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)
            print("""Looks ok!"""			)

      # finally, push to hub if required
      if push_to_hub:
            logger.info("""Pushing model and image processor to the hub..."""			)
            model.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add model"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)
            image_processor.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add image processor"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path",
           default=None,
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
       )
       parser.add_argument(
           "--model_name",
           default="glpn-kitti",
           type=str,
           help="Name of the model in case you're pushing to the hub.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






