"""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


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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   lowercase__:   List[str]					=      test_results.split(''' ''' )

   lowercase__:   Dict					=      0
   lowercase__:   str					=      0

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

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

   return failed, success, time_spent



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[str]:
   lowercase__:   int					=      {}
   lowercase__:   Optional[Any]					=      None
   lowercase__:   Any					=      False
   for line in failures_short_lines.split('''\n''' ):
      if re.search(R'''_ \[doctest\]'''       ,    lowerCAmelCase__ ):
         lowercase__:   Union[str, Any]					=      True
         lowercase__:   Union[str, Any]					=      line.split(''' ''' )[2]
      elif in_error and not line.split(''' ''' )[0].isdigit():
         lowercase__:   Optional[Any]					=      line
         lowercase__:   List[Any]					=      False

   return failures


class     UpperCAmelCase						:



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Union[str, Any]					=      title

       lowercase__:   Optional[Any]					=      doc_test_results['''time_spent'''].split(''','''							)[0]
       lowercase__:   Tuple					=      doc_test_results['''success''']
       lowercase__:   List[str]					=      doc_test_results['''failures''']
       lowercase__:   str					=      self.n_success + self.n_failures

       # Failures and success of the modeling tests
       lowercase__:   Any					=      doc_test_results






    @property
    def _snake_case			(   self							):
       lowercase__:   Dict					=      [self._time_spent]
       lowercase__:   Dict					=      0

       for time in time_spent:
          lowercase__:   List[str]					=      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(snake_case_							) == 1:
             lowercase__:   Optional[int]					=      [0, 0, time_parts[0]]

          lowercase__:   Optional[int]					=      int(time_parts[0]							), int(time_parts[1]							), float(time_parts[2]							)
          total_secs += hours * 3600 + minutes * 60 + seconds

       lowercase__:   List[str]					=      total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
       return F"""{int(snake_case_							)}h{int(snake_case_							)}m{int(snake_case_							)}s"""






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






    @property
    def _snake_case			(   self							):
       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							):
       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							):
       lowercase__:   List[str]					=      40
       lowercase__:   List[str]					=      {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(snake_case_    ,			snake_case_							)}

       lowercase__:   int					=      ''''''
       for category, failures in category_failures.items():
          if len(snake_case_							) == 0:
             continue

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

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

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






    @property
    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      [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(snake_case_							)






    @staticmethod
    def _snake_case			(   ):
       lowercase__:   Optional[int]					=      [
           {
               '''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(snake_case_							)}							)							)

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






    def _snake_case			(   self							):
       print('''Sending the following payload'''							)
       print(json.dumps({'''blocks''': json.loads(self.payload							)}							)							)

       lowercase__:   Any					=      F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else '''All tests passed.'''

       lowercase__:   Dict					=      client.chat_postMessage(
           channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']    ,			blocks=self.payload    ,			text=snake_case_    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      ''''''
       for key, value in failures.items():
          lowercase__:   Any					=      value[:200] + ''' [Truncated]''' if len(snake_case_							) > 250 else value
          failures_text += F"""*{key}*\n_{value}_\n\n"""

       lowercase__:   Dict					=      job_name
       lowercase__:   Dict					=      {'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

       if job_link is not None:
          lowercase__:   List[str]					=      {
              '''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							):
       if self.thread_ts is None:
          raise ValueError('''Can only post reply if a post has been made.'''							)

       lowercase__:   Any					=      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'''							)

       lowercase__:   str					=      sorted(self.doc_test_results.items()    ,			key=lambda _UpperCAmelCase							: t[0]							)
       for job, job_result in sorted_dict:
          if len(job_result['''failures''']							):
             lowercase__:   Optional[Any]					=      F"""*Num failures* :{len(job_result['failed']							)} \n"""
             lowercase__:   List[Any]					=      job_result['''failures''']
             lowercase__:   int					=      self.get_reply_blocks(snake_case_    ,			snake_case_    ,			snake_case_    ,			text=snake_case_							)

             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=snake_case_    ,			thread_ts=self.thread_ts['''ts''']    ,			)

             time.sleep(1							)



def SCREAMING_SNAKE_CASE__			(   )  ->  Optional[Any]:
   lowercase__:   int					=      os.environ['''GITHUB_RUN_ID''']
   lowercase__:   Union[str, Any]					=      F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
   lowercase__:   Optional[int]					=      requests.get(lowerCAmelCase__ ).json()
   lowercase__:   List[Any]					=      {}

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

      for i in range(lowerCAmelCase__ ):
         lowercase__:   Any					=      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.'''       ,    lowerCAmelCase__ )

   return {}



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Dict:
   lowercase__:   int					=      {}

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

   return _artifact



def SCREAMING_SNAKE_CASE__			(   )  ->  Optional[Any]:


   class     UpperCAmelCase						:



       """simple docstring"""

       def __init__(   self    ,			_UpperCAmelCase							):
          lowercase__:   Optional[int]					=      name
          lowercase__:   int					=      []






       def __str__(   self							):
          return self.name






       def _snake_case			(   self    ,			_UpperCAmelCase							):
          self.paths.append({'''name''': self.name, '''path''': path}							)



   lowercase__:   Dict[str, Artifact]					=      {}

   lowercase__:   Tuple					=      filter(os.path.isdir       ,    os.listdir() )
   for directory in directories:
      lowercase__:   Tuple					=      directory
      if artifact_name not in _available_artifacts:
         lowercase__:   str					=      Artifact(lowerCAmelCase__ )

         _available_artifacts[artifact_name].add_path(lowerCAmelCase__ )

   return _available_artifacts


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

       __A			   = 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'
       __A			   = {
           v: {
               "failed": [],
               "failures": {},
           }
           for v in docs.values()
       }

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

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

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

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

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

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

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


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 # vision encoder
 if "img_encoder.pos_embed" in name:
  __a :  Any						=  name.replace('''img_encoder.pos_embed'''  ,   '''vision_model.embeddings.position_embeddings'''  )
 if "img_encoder.patch_embed.proj" in name:
  __a :  str						=  name.replace('''img_encoder.patch_embed.proj'''  ,   '''vision_model.embeddings.patch_embeddings.projection'''  )
 if "img_encoder.patch_embed.norm" in name:
  __a :  int						=  name.replace('''img_encoder.patch_embed.norm'''  ,   '''vision_model.embeddings.layernorm'''  )
 if "img_encoder.layers" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.layers'''  ,   '''vision_model.encoder.stages'''  )
 if "blocks" in name and "res" not in name:
  __a :  List[Any]						=  name.replace('''blocks'''  ,   '''layers'''  )
 if "attn" in name and "pre_assign" not in name:
  __a :  Tuple						=  name.replace('''attn'''  ,   '''self_attn'''  )
 if "proj" in name and "self_attn" in name and "text" not in name:
  __a :  List[Any]						=  name.replace('''proj'''  ,   '''out_proj'''  )
 if "pre_assign_attn.attn.proj" in name:
  __a :  Any						=  name.replace('''pre_assign_attn.attn.proj'''  ,   '''pre_assign_attn.attn.out_proj'''  )
 if "norm1" in name:
  __a :  Union[str, Any]						=  name.replace('''norm1'''  ,   '''layer_norm1'''  )
 if "norm2" in name and "pre_assign" not in name:
  __a :  Optional[int]						=  name.replace('''norm2'''  ,   '''layer_norm2'''  )
 if "img_encoder.norm" in name:
  __a :  Union[str, Any]						=  name.replace('''img_encoder.norm'''  ,   '''vision_model.layernorm'''  )
 # text encoder
 if "text_encoder.token_embedding" in name:
  __a :  List[Any]						=  name.replace('''text_encoder.token_embedding'''  ,   '''text_model.embeddings.token_embedding'''  )
 if "text_encoder.positional_embedding" in name:
  __a :  Any						=  name.replace('''text_encoder.positional_embedding'''  ,   '''text_model.embeddings.position_embedding.weight'''  )
 if "text_encoder.transformer.resblocks." in name:
  __a :  Any						=  name.replace('''text_encoder.transformer.resblocks.'''  ,   '''text_model.encoder.layers.'''  )
 if "ln_1" in name:
  __a :  str						=  name.replace('''ln_1'''  ,   '''layer_norm1'''  )
 if "ln_2" in name:
  __a :  Union[str, Any]						=  name.replace('''ln_2'''  ,   '''layer_norm2'''  )
 if "c_fc" in name:
  __a :  Union[str, Any]						=  name.replace('''c_fc'''  ,   '''fc1'''  )
 if "c_proj" in name:
  __a :  Union[str, Any]						=  name.replace('''c_proj'''  ,   '''fc2'''  )
 if "text_encoder" in name:
  __a :  Optional[int]						=  name.replace('''text_encoder'''  ,   '''text_model'''  )
 if "ln_final" in name:
  __a :  str						=  name.replace('''ln_final'''  ,   '''final_layer_norm'''  )
 # projection layers
 if "img_projector.linear_hidden." in name:
  __a :  List[str]						=  name.replace('''img_projector.linear_hidden.'''  ,   '''visual_projection.'''  )
 if "img_projector.linear_out." in name:
  __a :  str						=  name.replace('''img_projector.linear_out.'''  ,   '''visual_projection.3.'''  )
 if "text_projector.linear_hidden" in name:
  __a :  int						=  name.replace('''text_projector.linear_hidden'''  ,   '''text_projection'''  )
 if "text_projector.linear_out" in name:
  __a :  List[str]						=  name.replace('''text_projector.linear_out'''  ,   '''text_projection.3'''  )

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Tuple  ):
 for key in orig_state_dict.copy().keys():
  __a :  List[Any]						=  orig_state_dict.pop(lowerCAmelCase__  )

  if "qkv" in key:
   # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  Tuple						=  key.split('''.'''  )
   __a				,       __a :  List[Any]						=  int(key_split[2]  ), int(key_split[4]  )
   __a :  List[Any]						=  config.vision_config.hidden_size
   if "weight" in key:
    __a :  int						=  val[:dim, :]
    __a :  List[str]						=  val[dim : dim * 2, :]
    __a :  List[Any]						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  int						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  elif "in_proj" in key:
   # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   __a :  int						=  key.split('''.'''  )
   __a :  str						=  int(key_split[3]  )
   __a :  List[Any]						=  config.text_config.hidden_size
   if "weight" in key:
    __a :  List[str]						=  val[:dim, :]
    __a :  Any						=  val[
        dim : dim * 2, :
    ]
    __a :  Dict						=  val[-dim:, :]
   else:
    __a :  List[str]						=  val[:dim]
    __a :  Any						=  val[dim : dim * 2]
    __a :  Any						=  val[-dim:]
  else:
   __a :  Union[str, Any]						=  rename_key(lowerCAmelCase__  )
   # squeeze if necessary
   if (
       "text_projection.0" in new_name
       or "text_projection.3" in new_name
       or "visual_projection.0" in new_name
       or "visual_projection.3" in new_name
   ):
    __a :  List[Any]						=  val.squeeze_()
   else:
    __a :  Dict						=  val

 return orig_state_dict




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  str						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[Any]="groupvit-gcc-yfcc"  ,   lowerCAmelCase__							:				int=False  ):
 __a :  Union[str, Any]						=  GroupViTConfig()
 __a :  int						=  GroupViTModel(lowerCAmelCase__  ).eval()

 __a :  Any						=  torch.load(lowerCAmelCase__  ,   map_location='''cpu'''  )['''model''']
 __a :  Optional[Any]						=  convert_state_dict(lowerCAmelCase__  ,   lowerCAmelCase__  )
 __a				,       __a :  Dict						=  model.load_state_dict(lowerCAmelCase__  ,   strict=lowerCAmelCase__  )
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__  ) == 0)

 # verify result
 __a :  Any						=  CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''  )
 __a :  Optional[Any]						=  prepare_img()
 __a :  Optional[int]						=  processor(text=['''a photo of a cat''', '''a photo of a dog''']  ,   images=lowerCAmelCase__  ,   padding=lowerCAmelCase__  ,   return_tensors='''pt'''  )

 with torch.no_grad():
  __a :  Tuple						=  model(**lowerCAmelCase__  )

 if model_name == "groupvit-gcc-yfcc":
  __a :  List[str]						=  torch.tensor([[13.35_23, 6.36_29]]  )
 elif model_name == "groupvit-gcc-redcaps":
  __a :  List[str]						=  torch.tensor([[16.18_73, 8.62_30]]  )
 else:
  raise ValueError(f"Model name {model_name} not supported."  )
 assert torch.allclose(outputs.logits_per_image  ,   lowerCAmelCase__  ,   atol=1e-3  )

 processor.save_pretrained(lowerCAmelCase__  )
 model.save_pretrained(lowerCAmelCase__  )
 print('''Successfully saved processor and model to'''  ,   lowerCAmelCase__  )

 if push_to_hub:
  print('''Pushing to the hub...'''  )
  processor.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )
  model.push_to_hub(lowerCAmelCase__  ,   organization='''nielsr'''  )


if __name__ == "__main__":
      lowercase__       =argparse.ArgumentParser()
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
      parser.add_argument(
          '--model_name',
          default='groupvit-gccy-fcc',
          type=str,
          help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
      )
      lowercase__       =parser.parse_args()

      convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
