





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase :str     =    logging.get_logger(__name__)







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[str]				       =   "huggingface/label-files"
					_snake_case       :					Optional[int]				       =   "imagenet-1k-id2label.json"
					_snake_case       :					Tuple				       =   json.load(open(hf_hub_download(UpperCAmelCase  ,  UpperCAmelCase  ,  repo_type="dataset"				)  ,  "r"				)				)
					_snake_case       :					Tuple				       =   {int(UpperCAmelCase				): v for k, v in idalabel.items()}
					_snake_case       :					Optional[int]				       =   {v: k for k, v in idalabel.items()}

					_snake_case       :					List[str]				       =   "std_conv" if "bit" in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					_snake_case       :					Optional[int]				       =   BitConfig(
					    conv_layer=UpperCAmelCase  ,  num_labels=1_000  ,  idalabel=UpperCAmelCase  ,  labelaid=UpperCAmelCase  ,  )

					return config







def    A				(							UpperCAmelCase				):
					if "stem.conv" in name:
										_snake_case       :					str				       =   name.replace("stem.conv"  ,  "bit.embedder.convolution"				)
					if "blocks" in name:
										_snake_case       :					List[str]				       =   name.replace("blocks"  ,  "layers"				)
					if "head.fc" in name:
										_snake_case       :					Tuple				       =   name.replace("head.fc"  ,  "classifier.1"				)
					if name.startswith("norm"				):
										_snake_case       :					List[Any]				       =   "bit." + name
					if "bit" not in name and "classifier" not in name:
										_snake_case       :					Union[str, Any]				       =   "bit.encoder." + name

					return name







def    A				(							):
					_snake_case       :					Optional[int]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					int				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)
					return im







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					_snake_case       :					Union[str, Any]				       =   get_config(UpperCAmelCase				)

					# load original model from timm
					_snake_case       :					List[str]				       =   create_model(UpperCAmelCase  ,  pretrained=UpperCAmelCase				)
					timm_model.eval()

					# load state_dict of original model
					_snake_case       :					Any				       =   timm_model.state_dict()
					for key in state_dict.copy().keys():
										_snake_case       :					Tuple				       =   state_dict.pop(UpperCAmelCase				)
										_snake_case       :					List[Any]				       =   val.squeeze() if "head" in key else val

					# load HuggingFace model
					_snake_case       :					List[str]				       =   BitForImageClassification(UpperCAmelCase				)
					model.eval()
					model.load_state_dict(UpperCAmelCase				)

					# create image processor
					_snake_case       :					Union[str, Any]				       =   create_transform(**resolve_data_config({}  ,  model=UpperCAmelCase				)				)
					_snake_case       :					int				       =   transform.transforms

					_snake_case       :					Dict				       =   {
					    "bilinear": PILImageResampling.BILINEAR,
					    "bicubic": PILImageResampling.BICUBIC,
					    "nearest": PILImageResampling.NEAREST,
					}

					_snake_case       :					Any				       =   BitImageProcessor(
					    do_resize=UpperCAmelCase  ,  size={"shortest_edge": timm_transforms[0].size}  ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,  do_center_crop=UpperCAmelCase  ,  crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}  ,  do_normalize=UpperCAmelCase  ,  image_mean=timm_transforms[-1].mean.tolist()  ,  image_std=timm_transforms[-1].std.tolist()  ,  )

					_snake_case       :					Any				       =   prepare_img()
					_snake_case       :					Any				       =   transform(UpperCAmelCase				).unsqueeze(0				)
					_snake_case       :					int				       =   processor(UpperCAmelCase  ,  return_tensors="pt"				).pixel_values

					# verify pixel values
					assert torch.allclose(UpperCAmelCase  ,  UpperCAmelCase				)

					# verify logits
					with torch.no_grad():
										_snake_case       :					Dict				       =   model(UpperCAmelCase				)
										_snake_case       :					Dict				       =   outputs.logits

					print("Logits:"  ,  logits[0, :3]				)
					print("Predicted class:"  ,  model.config.idalabel[logits.argmax(-1				).item()]				)
					_snake_case       :					Tuple				       =   timm_model(UpperCAmelCase				)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(UpperCAmelCase  ,  outputs.logits  ,  atol=1e-3				)
					print("Looks ok!"				)

					if pytorch_dump_folder_path is not None:
										Path(UpperCAmelCase				).mkdir(exist_ok=UpperCAmelCase				)
										print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""				)
										model.save_pretrained(UpperCAmelCase				)
										processor.save_pretrained(UpperCAmelCase				)

					if push_to_hub:
										print(F"""Pushing model {model_name} and processor to the hub"""				)
										model.push_to_hub(F"""ybelkada/{model_name}"""				)
										processor.push_to_hub(F"""ybelkada/{model_name}"""				)


if __name__ == "__main__":
		__lowerCAmelCase :List[Any]     =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='resnetv2_50x1_bitm',
		    type=str,
		    help='Name of the BiT timm model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		    help='Whether to push the model to the hub.',
		)

		__lowerCAmelCase :List[str]     =    parser.parse_args()
		convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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


__lowerCAmelCase :List[Any]     =    WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[str]				       =   test_results.split(" "				)

					_snake_case       :					Optional[int]				       =   0
					_snake_case       :					int				       =   0

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

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

					return failed, success, time_spent







def    A				(							UpperCAmelCase				):
					_snake_case       :					Union[str, Any]				       =   {}
					_snake_case       :					Any				       =   None
					_snake_case       :					str				       =   False
					for line in failures_short_lines.split("\n"				):
										if re.search(R"_ \[doctest\]"  ,  UpperCAmelCase				):
															_snake_case       :					Union[str, Any]				       =   True
															_snake_case       :					Tuple				       =   line.split(" "				)[2]
										elif in_error and not line.split(" "				)[0].isdigit():
															_snake_case       :					Optional[Any]				       =   line
															_snake_case       :					Dict				       =   False

					return failures






class        _a:







		def __init__(    self ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Dict				       =   title

							_snake_case       :					Optional[Any]				       =   doc_test_results["time_spent"].split(","				)[0]
							_snake_case       :					Dict				       =   doc_test_results["success"]
							_snake_case       :					Optional[Any]				       =   doc_test_results["failures"]
							_snake_case       :					Tuple				       =   self.n_success + self.n_failures

							# Failures and success of the modeling tests
							_snake_case       :					Union[str, Any]				       =   doc_test_results







		@property
		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   [self._time_spent]
							_snake_case       :					Tuple				       =   0

							for time in time_spent:
												_snake_case       :					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:
																	_snake_case       :					List[Any]				       =   [0, 0, time_parts[0]]

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

							_snake_case       , _snake_case       , _snake_case       :					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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							return {"type": "header", "text": {"type": "plain_text", "text": self.title}}







		@property
		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							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        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   4_0
							_snake_case       :					Any				       =   {k: v["failed"] for k, v in doc_test_results.items() if isinstance(__snake_case ,						__snake_case				)}

							_snake_case       :					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        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[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        lowercase       (    )      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Dict				       =   [
							    {
							        "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        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

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

							_snake_case       :					List[str]				       =   f"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else "All tests passed."

							_snake_case       :					Tuple				       =   client.chat_postMessage(
							    channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"] ,						blocks=self.payload ,						text=__snake_case ,						)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					str				       =   ""
							for key, value in failures.items():
												_snake_case       :					Any				       =   value[:2_0_0] + " [Truncated]" if len(__snake_case				) > 2_5_0 else value
												failures_text += f"""*{key}*\n_{value}_\n\n"""

							_snake_case       :					str				       =   job_name
							_snake_case       :					List[str]				       =   {"type": "section", "text": {"type": "mrkdwn", "text": text}}

							if job_link is not None:
												_snake_case       :					Union[str, Any]				       =   {
												    "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        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							if self.thread_ts is None:
												raise ValueError("Can only post reply if a post has been made."				)

							_snake_case       :					Optional[int]				       =   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"				)

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

					try:
										jobs.update({job["name"]: job["html_url"] for job in result["jobs"]}				)
										_snake_case       :					Union[str, Any]				       =   math.ceil((result["total_count"] - 100) / 100				)

										for i in range(UpperCAmelCase				):
															_snake_case       :					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."  ,  UpperCAmelCase				)

					return {}







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   {}

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

					return _artifact







def    A				(							):






					class        _a:







							def __init__(    self ,						__snake_case				)      ->      List[Any]:


												'''simple docstring'''

												_snake_case       :					Any				       =   name
												_snake_case       :					Any				       =   []







							def __str__(    self				)      ->      Tuple:


												'''simple docstring'''

												return self.name







							def        lowercase       (    self ,						__snake_case				)      ->      List[Any]:


												'''simple docstring'''

												self.paths.append({"name": self.name, "path": path}				)






					_snake_case       :					Dict[str, Artifact]				       =   {}

					_snake_case       :					Any				       =   filter(os.path.isdir  ,  os.listdir()				)
					for directory in directories:
										_snake_case       :					Optional[int]				       =   directory
										if artifact_name not in _available_artifacts:
															_snake_case       :					Optional[Any]				       =   Artifact(UpperCAmelCase				)

															_available_artifacts[artifact_name].add_path(UpperCAmelCase				)

					return _available_artifacts


if __name__ == "__main__":
							__lowerCAmelCase :str     =    get_job_links()
							__lowerCAmelCase :Optional[int]     =    retrieve_available_artifacts()

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

							# Link to the GitHub Action job
							__lowerCAmelCase :str     =    github_actions_job_links.get('run_doctests')

							__lowerCAmelCase :List[Any]     =    available_artifacts['doc_tests_gpu_test_reports'].paths[0]
							__lowerCAmelCase :Optional[Any]     =    retrieve_artifact(artifact_path['name'])
							if "stats" in artifact:
														__lowerCAmelCase   ,   __lowerCAmelCase   ,   __lowerCAmelCase :Dict     =    handle_test_results(artifact['stats'])
														__lowerCAmelCase :List[Any]     =    failed
														__lowerCAmelCase :Optional[int]     =    success
														__lowerCAmelCase :str     =    time_spent[1:-1] + ', '

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

																												if "::" in line:
																																			__lowerCAmelCase   ,   __lowerCAmelCase :List[str]     =    line.split('::')
																												else:
																																			__lowerCAmelCase   ,   __lowerCAmelCase :int     =    line, line

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

																																										__lowerCAmelCase :Union[str, Any]     =    all_failures[test] if test in all_failures else 'N/A'
																																										__lowerCAmelCase :Optional[Any]     =    failure
																																										break

							__lowerCAmelCase :Optional[int]     =    Message('🤗 Results of the doc tests.', doc_test_results)
							message.post()
							message.post_reply()