




import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
       import torch

       lowercase__				:      Any	   = True
except ImportError:
       lowercase__				:      Optional[int]	   = False


try:
       from torch.hub import _get_torch_home

       lowercase__				:      List[Any]	   = _get_torch_home()
except ImportError:
       lowercase__				:      Union[str, Any]	   = os.path.expanduser(
           os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
       )

lowercase__				:      Tuple	   = os.path.join(torch_cache_home, "transformers")

lowercase__				:      Optional[Any]	   = "https://cdn.huggingface.co"
lowercase__				:      Dict	   = "https://s3.amazonaws.com/models.huggingface.co/bert"
lowercase__				:      List[Any]	   = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
lowercase__				:      Any	   = os.path.join(PATH, "config.yaml")
lowercase__				:      Optional[int]	   = os.path.join(PATH, "attributes.txt")
lowercase__				:      Optional[Any]	   = os.path.join(PATH, "objects.txt")
lowercase__				:      str	   = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
lowercase__				:      List[Any]	   = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
lowercase__				:      Dict	   = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
lowercase__				:      List[Any]	   = "pytorch_model.bin"
lowercase__				:      Union[str, Any]	   = "config.yaml"

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase=OBJECTS		, __UpperCamelCase=ATTRIBUTES)      ->					Optional[Any]:
     a     =				[]
     with open(__UpperCamelCase) as f:
          for object in f.readlines():
               vg_classes.append(object.split(",")[0].lower().strip())

     a     =				[]
     with open(__UpperCamelCase) as f:
          for object in f.readlines():
               vg_attrs.append(object.split(",")[0].lower().strip())
     return vg_classes, vg_attrs

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				OrderedDict()
     with open(__UpperCamelCase		, "rb") as f:
          a     =				pkl.load(__UpperCamelCase)["model"]
     for k in copy.deepcopy(list(ckp.keys())):
          a     =				ckp.pop(__UpperCamelCase)
          if isinstance(__UpperCamelCase		, np.ndarray):
               a     =				torch.tensor(__UpperCamelCase)
          else:
               assert isinstance(__UpperCamelCase		, torch.tensor), type(__UpperCamelCase)
          a     =				v
     return r




class       a__	:
     a		:      Optional[Any]		 =     {}







     def __init__(     self  ,		A  ,		A = "root"  ,		A=0			)  ->   List[str]:



          '''simple docstring'''



          a     =				name
          a     =				level
          a     =				{}
          for k, v in dictionary.items():
               if v is None:
                    raise ValueError()
               a     =				copy.deepcopy(A			)
               a     =				copy.deepcopy(A			)
               if isinstance(A  ,		A			):
                    a     =				Config(A  ,		name=A  ,		level=level + 1			)
               a     =				v
               setattr(self  ,		A  ,		A			)

          a     =				d







     def __repr__(     self			)  ->   int:



          '''simple docstring'''



          return str(list((self._pointer.keys())			)			)







     def __setattr__(     self  ,		A  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				val
          a     =				val
          a     =				key.split("."			)
          a     =				len(A			) - 1
          a     =				self._pointer
          if len(A			) > 1:
               for i, l in enumerate(A			):
                    if hasattr(self  ,		A			) and isinstance(getattr(self  ,		A			)  ,		A			):
                         setattr(getattr(self  ,		A			)  ,		".".join(levels[i:]			)  ,		A			)
                    if l == last_level:
                         a     =				val
                    else:
                         a     =				pointer[l]







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return self._pointer







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          with open(F'''{file_name}'''  ,		"w"			) as stream:
               dump(A  ,		A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   int:



          '''simple docstring'''



          with open(F'''{file_name}'''  ,		"w"			) as stream:
               json.dump(A  ,		A			)







     @staticmethod
     def       lowerCAmelCase_    (     A			)  ->   Optional[Any]:



          '''simple docstring'''



          with open(A			) as stream:
               a     =				load(A  ,		Loader=A			)
          return data







     def __str__(     self			)  ->   Any:



          '''simple docstring'''



          a     =				"    "
          if self._name != "root":
               a     =				F'''{t * (self._level-1)}{self._name}:\n'''
          else:
               a     =				""
          a     =				self._level
          for i, (k, v) in enumerate(self._pointer.items()			):
               if isinstance(A  ,		A			):
                    r += F'''{t * (self._level)}{v}\n'''
                    self._level += 1
               else:
                    r += F'''{t * (self._level)}{k}: {v} ({type(A			).__name__})\n'''
               a     =				level
          return r[:-1]







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				cls.get_config_dict(A  ,		**A			)
          return cls(A			)







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				kwargs.pop("cache_dir"  ,		A			)
          a     =				kwargs.pop("force_download"  ,		A			)
          a     =				kwargs.pop("resume_download"  ,		A			)
          a     =				kwargs.pop("proxies"  ,		A			)
          a     =				kwargs.pop("local_files_only"  ,		A			)

          if os.path.isdir(A			):
               a     =				os.path.join(A  ,		A			)
          elif os.path.isfile(A			) or is_remote_url(A			):
               a     =				pretrained_model_name_or_path
          else:
               a     =				hf_bucket_url(A  ,		filename=A  ,		use_cdn=A			)

          try:
               # Load from URL or cache if already cached
               a     =				cached_path(
                   A  ,		cache_dir=A  ,		force_download=A  ,		proxies=A  ,		resume_download=A  ,		local_files_only=A  ,		)
               # Load config dict
               if resolved_config_file is None:
                    raise EnvironmentError

               a     =				Config.load_yaml(A			)

          except EnvironmentError:
               a     =				"Can't load config for"
               raise EnvironmentError(A			)

          if resolved_config_file == config_file:
               print("loading configuration file from path"			)
          else:
               print("loading configuration file cache"			)

          return Config.load_yaml(A			), kwargs







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
     a     =				torch.load("dump.pt"		, map_location=in_tensor.device)
     a     =				in_tensor.numpy()
     a     =				out_tensor.numpy()[0]
     print(na.shape		, na[0, 0, :5])
     print(na.shape		, na[0, 0, :5])
     assert np.allclose(__UpperCamelCase		, __UpperCamelCase		, rtol=0.01		, atol=0.1), (
         f'''{sum([1 for x in np.isclose(__UpperCamelCase		, __UpperCamelCase		, rtol=0.01		, atol=0.1).flatten() if x is False])/len(na.flatten())*1_00:.4f} %'''
         " element-wise mismatch"
     )
     raise Exception("tensors are all good")

     # Hugging face functions below

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Any:
     a     =				urlparse(__UpperCamelCase)
     return parsed.scheme in ("http", "https")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=True)      ->					str:
     a     =				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     a     =				"/" not in model_id
     if legacy_format:
          return f'''{endpoint}/{model_id}-{filename}'''
     else:
          return f'''{endpoint}/{model_id}/{filename}'''

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=0		, __UpperCamelCase=None		, )      ->					Any:
     a     =				"python/{}".format(sys.version.split()[0])
     if _torch_available:
          ua += "; torch/{}".format(torch.__version__)
     if isinstance(__UpperCamelCase		, __UpperCamelCase):
          ua += "; " + "; ".join("{}/{}".format(__UpperCamelCase		, __UpperCamelCase) for k, v in user_agent.items())
     elif isinstance(__UpperCamelCase		, __UpperCamelCase):
          ua += "; " + user_agent
     a     =				{"user-agent": ua}
     if resume_size > 0:
          a     =				"bytes=%d-" % (resume_size,)
     a     =				requests.get(__UpperCamelCase		, stream=__UpperCamelCase		, proxies=__UpperCamelCase		, headers=__UpperCamelCase)
     if response.status_code == 4_16:  # Range not satisfiable
          return
     a     =				response.headers.get("Content-Length")
     a     =				resume_size + int(__UpperCamelCase) if content_length is not None else None
     a     =				tqdm(
         unit="B"		, unit_scale=__UpperCamelCase		, total=__UpperCamelCase		, initial=__UpperCamelCase		, desc="Downloading"		, )
     for chunk in response.iter_content(chunk_size=10_24):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(__UpperCamelCase))
               temp_file.write(__UpperCamelCase)
     progress.close()

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=False		, __UpperCamelCase=None		, __UpperCamelCase=10		, __UpperCamelCase=False		, __UpperCamelCase=None		, __UpperCamelCase=False		, )      ->					List[Any]:
     if cache_dir is None:
          a     =				TRANSFORMERS_CACHE
     if isinstance(__UpperCamelCase		, __UpperCamelCase):
          a     =				str(__UpperCamelCase)

     os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)

     a     =				None
     if not local_files_only:
          try:
               a     =				requests.head(__UpperCamelCase		, allow_redirects=__UpperCamelCase		, proxies=__UpperCamelCase		, timeout=__UpperCamelCase)
               if response.status_code == 2_00:
                    a     =				response.headers.get("ETag")
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     a     =				url_to_filename(__UpperCamelCase		, __UpperCamelCase)

     # get cache path to put the file
     a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)

     # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
     # try to get the last downloaded one
     if etag is None:
          if os.path.exists(__UpperCamelCase):
               return cache_path
          else:
               a     =				[
                   file
                   for file in fnmatch.filter(os.listdir(__UpperCamelCase)		, filename + ".*")
                   if not file.endswith(".json") and not file.endswith(".lock")
               ]
               if len(__UpperCamelCase) > 0:
                    return os.path.join(__UpperCamelCase		, matching_files[-1])
               else:
                    # If files cannot be found and local_files_only=True,
                    # the models might've been found if local_files_only=False
                    # Notify the user about that
                    if local_files_only:
                         raise ValueError(
                             "Cannot find the requested files in the cached path and outgoing traffic has been"
                             " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
                             " to False.")
                    return None

    # From now on, etag is not None.
     if os.path.exists(__UpperCamelCase) and not force_download:
          return cache_path

     # Prevent parallel downloads of the same file with a lock.
     a     =				cache_path + ".lock"
     with FileLock(__UpperCamelCase):
          # If the download just completed while the lock was activated.
          if os.path.exists(__UpperCamelCase) and not force_download:
               # Even if returning early like here, the lock will be released.
               return cache_path

          if resume_download:
               a     =				cache_path + ".incomplete"

               @contextmanager
               def _resumable_file_manager():
                    with open(__UpperCamelCase		, "a+b") as f:
                         yield f

               a     =				_resumable_file_manager
               if os.path.exists(__UpperCamelCase):
                    a     =				os.stat(__UpperCamelCase).st_size
               else:
                    a     =				0
          else:
               a     =				partial(tempfile.NamedTemporaryFile		, dir=__UpperCamelCase		, delete=__UpperCamelCase)
               a     =				0

          # Download to temporary file, then copy to cache dir once finished.
          # Otherwise you get corrupt cache entries if the download gets interrupted.
          with temp_file_manager() as temp_file:
               print(
                   "%s not found in cache or force_download set to True, downloading to %s"		, __UpperCamelCase		, temp_file.name		, )

               http_get(
                   __UpperCamelCase		, __UpperCamelCase		, proxies=__UpperCamelCase		, resume_size=__UpperCamelCase		, user_agent=__UpperCamelCase		, )

          os.replace(temp_file.name		, __UpperCamelCase)

          a     =				{"url": url, "etag": etag}
          a     =				cache_path + ".json"
          with open(__UpperCamelCase		, "w") as meta_file:
               json.dump(__UpperCamelCase		, __UpperCamelCase)

     return cache_path

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=None)      ->					List[str]:
     a     =				url.encode("utf-8")
     a     =				shaaaa(__UpperCamelCase)
     a     =				url_hash.hexdigest()

     if etag:
          a     =				etag.encode("utf-8")
          a     =				shaaaa(__UpperCamelCase)
          filename += "." + etag_hash.hexdigest()

     if url.endswith(".h5"):
          filename += ".h5"

     return filename

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=False		, __UpperCamelCase=None		, __UpperCamelCase=False		, __UpperCamelCase=None		, __UpperCamelCase=False		, __UpperCamelCase=False		, __UpperCamelCase=False		, )      ->					int:
     if cache_dir is None:
          a     =				TRANSFORMERS_CACHE
     if isinstance(__UpperCamelCase		, __UpperCamelCase):
          a     =				str(__UpperCamelCase)
     if isinstance(__UpperCamelCase		, __UpperCamelCase):
          a     =				str(__UpperCamelCase)

     if is_remote_url(__UpperCamelCase):
          # URL, so get it from the cache (downloading if necessary)
          a     =				get_from_cache(
              __UpperCamelCase		, cache_dir=__UpperCamelCase		, force_download=__UpperCamelCase		, proxies=__UpperCamelCase		, resume_download=__UpperCamelCase		, user_agent=__UpperCamelCase		, local_files_only=__UpperCamelCase		, )
     elif os.path.exists(__UpperCamelCase):
          # File, and it exists.
          a     =				url_or_filename
     elif urlparse(__UpperCamelCase).scheme == "":
          # File, but it doesn't exist.
          raise EnvironmentError("file {} not found".format(__UpperCamelCase))
     else:
          # Something unknown
          raise ValueError("unable to parse {} as a URL or as a local path".format(__UpperCamelCase))

     if extract_compressed_file:
          if not is_zipfile(__UpperCamelCase) and not tarfile.is_tarfile(__UpperCamelCase):
               return output_path

          # Path where we extract compressed archives
          # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
          a  ,				a     =				os.path.split(__UpperCamelCase)
          a     =				output_file.replace("."		, "-") + "-extracted"
          a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)

          if os.path.isdir(__UpperCamelCase) and os.listdir(__UpperCamelCase) and not force_extract:
               return output_path_extracted

          # Prevent parallel extractions
          a     =				output_path + ".lock"
          with FileLock(__UpperCamelCase):
               shutil.rmtree(__UpperCamelCase		, ignore_errors=__UpperCamelCase)
               os.makedirs(__UpperCamelCase)
               if is_zipfile(__UpperCamelCase):
                    with ZipFile(__UpperCamelCase		, "r") as zip_file:
                         zip_file.extractall(__UpperCamelCase)
                         zip_file.close()
               elif tarfile.is_tarfile(__UpperCamelCase):
                    a     =				tarfile.open(__UpperCamelCase)
                    tar_file.extractall(__UpperCamelCase)
                    tar_file.close()
               else:
                    raise EnvironmentError("Archive format of {} could not be identified".format(__UpperCamelCase))

          return output_path_extracted

     return output_path

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=",")      ->					List[str]:
     assert isinstance(__UpperCamelCase		, __UpperCamelCase)
     if os.path.isfile(__UpperCamelCase):
          with open(__UpperCamelCase) as f:
               a     =				eval(f.read())
     else:
          a     =				requests.get(__UpperCamelCase)
          try:
               a     =				requests.json()
          except Exception:
               a     =				req.content.decode()
               assert data is not None, "could not connect"
               try:
                    a     =				eval(__UpperCamelCase)
               except Exception:
                    a     =				data.split("\n")
          req.close()
     return data

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Dict:
     a     =				requests.get(__UpperCamelCase)
     a     =				np.array(Image.open(BytesIO(response.content)))
     return img

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					List[str]:
     a     =				url.split("/")[-1]
     if fn not in os.listdir(os.getcwd()):
          wget.download(__UpperCamelCase)
     with open(__UpperCamelCase		, "rb") as stream:
          a     =				pkl.load(__UpperCamelCase)
     a     =				weights.pop("model")
     a     =				{}
     for k, v in model.items():
          a     =				torch.from_numpy(__UpperCamelCase)
          if "running_var" in k:
               a     =				torch.tensor([0])
               a     =				k.replace("running_var"		, "num_batches_tracked")
               a     =				zero
     return new

def  SCREAMING_SNAKE_CASE							(       )      ->					Dict:
     print(f'''{os.path.abspath(os.path.join(__UpperCamelCase		, os.pardir))}/demo.ipynb''')

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase="RGB")      ->					List[str]:
     assert isinstance(__UpperCamelCase		, __UpperCamelCase)
     if os.path.isfile(__UpperCamelCase):
          a     =				cva.imread(__UpperCamelCase)
     else:
          a     =				get_image_from_url(__UpperCamelCase)
          assert img is not None, f'''could not connect to: {im}'''
     a     =				cva.cvtColor(__UpperCamelCase		, cva.COLOR_BGR2RGB)
     if input_format == "RGB":
          a     =				img[:, :, ::-1]
     return img





def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=1)      ->					Optional[int]:
     return (images[i : i + batch] for i in range(0		, len(__UpperCamelCase)		, __UpperCamelCase))







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__				:      Tuple	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}




class       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """resnet"""
     a		:      Tuple		 =     ["""basic""", """bottleneck"""]







     def __init__(     self  ,		A=3  ,		A=64  ,		A=[256, 512, 1024, 2048]  ,		A=[3, 4, 6, 3]  ,		A="bottleneck"  ,		A="relu"  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Any:



          '''simple docstring'''



          super().__init__(**A			)
          if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
          a     =				num_channels
          a     =				embedding_size
          a     =				hidden_sizes
          a     =				depths
          a     =				layer_type
          a     =				hidden_act
          a     =				downsample_in_first_stage
          a     =				["stem"] + [F'''stage{idx}''' for idx in range(1  ,		len(A			) + 1			)]
          a  ,				a     =				get_aligned_output_features_output_indices(
              out_features=A  ,		out_indices=A  ,		stage_names=self.stage_names			)







class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-3

