
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


lowercase__       =WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 __a :  List[str]						=  test_results.split(''' '''  )

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

 return failures



class 		UpperCamelCase__  :
   def __init__(self :					str					,				snake_case_ :					str					,				snake_case_ :					Dict   ):
    __a :  Union[str, Any]						=  title

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

    # Failures and success of the modeling tests
    __a :  Any						=  doc_test_results
   @property
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Dict						=  [self._time_spent]
    __a :  Dict						=  0

    for time in time_spent:
     __a :  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:
      __a :  Optional[int]						=  [0, 0, time_parts[0]]

     __a				,       __a				,       __a :  Optional[int]						=  int(time_parts[0]   ), int(time_parts[1]   ), float(time_parts[2]   )
     total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

    __a				,       __a				,       __a :  List[str]						=  total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
    return f"{int(snake_case_   )}h{int(snake_case_   )}m{int(snake_case_   )}s"
   @property
   def       lowerCAmelCase			(self :					Any   ):
    return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
   @property
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    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       lowerCAmelCase			(self :					Optional[int]   ):
    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       lowerCAmelCase			(self :					List[Any]   ):
    __a :  List[str]						=  4_0
    __a :  List[str]						=  {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(snake_case_					,				snake_case_   )}

    __a :  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       lowerCAmelCase			(self :					Dict   ):
    __a :  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       lowerCAmelCase			():
    __a :  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       lowerCAmelCase			(self :					str   ):
    print('''Sending the following payload'''   )
    print(json.dumps({'''blocks''': json.loads(self.payload   )}   )   )

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

    __a :  Dict						=  client.chat_postMessage(
        channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']					,				blocks=self.payload					,				text=snake_case_					,				)
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					str					,				snake_case_ :					str					,				snake_case_ :					Dict   ):
    __a :  List[str]						=  ''''''
    for key, value in failures.items():
     __a :  Any						=  value[:2_0_0] + ''' [Truncated]''' if len(snake_case_   ) > 2_5_0 else value
     failures_text += f"*{key}*\n_{value}_\n\n"

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

    if job_link is not None:
     __a :  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       lowerCAmelCase			(self :					Dict   ):
    if self.thread_ts is None:
     raise ValueError('''Can only post reply if a post has been made.'''   )

    __a :  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'''   )

    __a :  str						=  sorted(self.doc_test_results.items()					,				key=lambda snake_case_   : t[0]   )
    for job, job_result in sorted_dict:
     if len(job_result['''failures''']   ):
      __a :  Optional[Any]						=  f"*Num failures* :{len(job_result['failed']   )} \n"
      __a :  List[Any]						=  job_result['''failures''']
      __a :  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    __UpperCamelCase     (				):
 __a :  int						=  os.environ['''GITHUB_RUN_ID''']
 __a :  Union[str, Any]						=  f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
 __a :  Optional[int]						=  requests.get(lowerCAmelCase__  ).json()
 __a :  List[Any]						=  {}

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

  for i in range(lowerCAmelCase__  ):
   __a :  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    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  int						=  {}

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

 return _artifact




def    __UpperCamelCase     (				):



 class 		UpperCamelCase__  :
    def __init__(self :					Optional[Any]					,				snake_case_ :					str   ):
     __a :  Optional[int]						=  name
     __a :  int						=  []
    def __str__(self :					Dict   ):
     return self.name







    def       lowerCAmelCase			(self :					Any					,				snake_case_ :					str   ):
     self.paths.append({'''name''': self.name, '''path''': path}   )





 __a :  Dict[str, Artifact]						=  {}

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

   _available_artifacts[artifact_name].add_path(lowerCAmelCase__  )

 return _available_artifacts


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

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

      # Link to the GitHub Action job
      lowercase__       =github_actions_job_links.get('run_doctests')

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

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

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

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

                                    lowercase__       =all_failures[test] if test in all_failures else 'N/A'
                                    lowercase__       =failure
                                    break

      lowercase__       =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)
