

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

			a_      =     True
except ImportError:
			a_      =     False


try:
			from torch.hub import _get_torch_home

			a_      =     _get_torch_home()
except ImportError:
			a_      =     os.path.expanduser(
			    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
			)

a_      =     os.path.join(torch_cache_home, 'transformers')

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

def 			lowerCamelCase__			(       _a=OBJECTS				,      _a=ATTRIBUTES):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							[]
	with open(lowercase_) as f:
		for object in f.readlines():
			vg_classes.append(object.split(",")[0].lower().strip())

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

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							OrderedDict()
	with open(lowercase_				,      "rb") as f:
		SCREAMING_SNAKE_CASE :   int					  =							pkl.load(lowercase_)["""model"""]
	for k in copy.deepcopy(list(ckp.keys())):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							ckp.pop(lowercase_)
		if isinstance(lowercase_				,      np.ndarray):
			SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor(lowercase_)
		else:
			assert isinstance(lowercase_				,      torch.tensor), type(lowercase_)
		SCREAMING_SNAKE_CASE :   Tuple					  =							v
	return r






class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     ={}
			def __init__(      self				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			List[Any] = "root"   ,   a				:			Union[str, Any]=0						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							name
				SCREAMING_SNAKE_CASE :   List[Any]					  =							level
				SCREAMING_SNAKE_CASE :   Tuple					  =							{}
				for k, v in dictionary.items():
					if v is None:
						raise ValueError()
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							copy.deepcopy(SCREAMING_SNAKE_CASE_						)
					SCREAMING_SNAKE_CASE :   str					  =							copy.deepcopy(SCREAMING_SNAKE_CASE_						)
					if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						):
						SCREAMING_SNAKE_CASE :   Any					  =							Config(SCREAMING_SNAKE_CASE_   ,   name=SCREAMING_SNAKE_CASE_   ,   level=level + 1						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							v
					setattr(self   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						)

				SCREAMING_SNAKE_CASE :   Dict					  =							d
			def __repr__(      self				:			Tuple						)				->							Dict:

				"""simple docstring"""

				return str(list((self._pointer.keys())						)						)
			def __setattr__(      self				:			Tuple   ,   a				:			str   ,   a				:			str						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							val
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							val
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							key.split("."						)
				SCREAMING_SNAKE_CASE :   Dict					  =							len(SCREAMING_SNAKE_CASE_						) - 1
				SCREAMING_SNAKE_CASE :   Tuple					  =							self._pointer
				if len(SCREAMING_SNAKE_CASE_						) > 1:
					for i, l in enumerate(SCREAMING_SNAKE_CASE_						):
						if hasattr(self   ,   SCREAMING_SNAKE_CASE_						) and isinstance(getattr(self   ,   SCREAMING_SNAKE_CASE_						)   ,   SCREAMING_SNAKE_CASE_						):
							setattr(getattr(self   ,   SCREAMING_SNAKE_CASE_						)   ,   ".".join(levels[i:]						)   ,   SCREAMING_SNAKE_CASE_						)
						if l == last_level:
							SCREAMING_SNAKE_CASE :   Dict					  =							val
						else:
							SCREAMING_SNAKE_CASE :   List[str]					  =							pointer[l]
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				return self._pointer
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			Any   ,   a				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				with open(F"{file_name}"   ,   "w"						) as stream:
					dump(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Any   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				with open(F"{file_name}"   ,   "w"						) as stream:
					json.dump(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						)
			@staticmethod
			def 				__UpperCamelCase			(      a				:			Tuple						)				->							int:

				"""simple docstring"""

				with open(SCREAMING_SNAKE_CASE_						) as stream:
					SCREAMING_SNAKE_CASE :   int					  =							load(SCREAMING_SNAKE_CASE_   ,   Loader=SCREAMING_SNAKE_CASE_						)
				return data
			def __str__(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"""    """
				if self._name != "root":
					SCREAMING_SNAKE_CASE :   List[Any]					  =							F"{t * (self._level-1)}{self._name}:\n"
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							""""""
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._level
				for i, (k, v) in enumerate(self._pointer.items()						):
					if isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						):
						r += F"{t * (self._level)}{v}\n"
						self._level += 1
					else:
						r += F"{t * (self._level)}{k}: {v} ({type(SCREAMING_SNAKE_CASE_						).__name__})\n"
					SCREAMING_SNAKE_CASE :   Dict					  =							level
				return r[:-1]
			@classmethod
			def 				__UpperCamelCase			(      cls				:			List[str]   ,   a				:			List[str]   ,   **a				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							cls.get_config_dict(SCREAMING_SNAKE_CASE_   ,   **SCREAMING_SNAKE_CASE_						)
				return cls(SCREAMING_SNAKE_CASE_						)






			@classmethod
			def 				__UpperCamelCase			(      cls				:			List[str]   ,   a				:			Tuple   ,   **a				:			Optional[int]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							kwargs.pop("cache_dir"   ,   SCREAMING_SNAKE_CASE_						)
				SCREAMING_SNAKE_CASE :   Any					  =							kwargs.pop("force_download"   ,   SCREAMING_SNAKE_CASE_						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("resume_download"   ,   SCREAMING_SNAKE_CASE_						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("proxies"   ,   SCREAMING_SNAKE_CASE_						)
				SCREAMING_SNAKE_CASE :   Dict					  =							kwargs.pop("local_files_only"   ,   SCREAMING_SNAKE_CASE_						)

				if os.path.isdir(SCREAMING_SNAKE_CASE_						):
					SCREAMING_SNAKE_CASE :   Tuple					  =							os.path.join(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_						)
				elif os.path.isfile(SCREAMING_SNAKE_CASE_						) or is_remote_url(SCREAMING_SNAKE_CASE_						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							pretrained_model_name_or_path
				else:
					SCREAMING_SNAKE_CASE :   int					  =							hf_bucket_url(SCREAMING_SNAKE_CASE_   ,   filename=SCREAMING_SNAKE_CASE_   ,   use_cdn=SCREAMING_SNAKE_CASE_						)

				try:
					# Load from URL or cache if already cached
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							cached_path(
					    SCREAMING_SNAKE_CASE_   ,   cache_dir=SCREAMING_SNAKE_CASE_   ,   force_download=SCREAMING_SNAKE_CASE_   ,   proxies=SCREAMING_SNAKE_CASE_   ,   resume_download=SCREAMING_SNAKE_CASE_   ,   local_files_only=SCREAMING_SNAKE_CASE_   ,   )
					# Load config dict
					if resolved_config_file is None:
						raise EnvironmentError

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							Config.load_yaml(SCREAMING_SNAKE_CASE_						)

				except EnvironmentError:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							"""Can't load config for"""
					raise EnvironmentError(SCREAMING_SNAKE_CASE_						)

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

				return Config.load_yaml(SCREAMING_SNAKE_CASE_						), kwargs

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   str					  =							torch.load("dump.pt"				,      map_location=in_tensor.device)
	SCREAMING_SNAKE_CASE :   int					  =							in_tensor.numpy()
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							out_tensor.numpy()[0]
	print(na.shape				,      na[0, 0, :5])
	print(na.shape				,      na[0, 0, :5])
	assert np.allclose(lowercase_				,      lowercase_				,      rtol=0.01				,      atol=0.1), (
	    f"{sum([1 for x in np.isclose(lowercase_				,      lowercase_				,      rtol=0.01				,      atol=0.1).flatten() if x is False])/len(na.flatten())*100:.4f} %"
	    " element-wise mismatch"
	)
	raise Exception("tensors are all good")

	# Hugging face functions below

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Any					  =							urlparse(lowercase_)
	return parsed.scheme in ("http", "https")

def 			lowerCamelCase__			(       _a				,      _a				,      _a=True):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
	SCREAMING_SNAKE_CASE :   Dict					  =							"""/""" not in model_id
	if legacy_format:
		return f"{endpoint}/{model_id}-{filename}"
	else:
		return f"{endpoint}/{model_id}/{filename}"

def 			lowerCamelCase__			(       _a				,      _a				,      _a=None				,      _a=0				,      _a=None				,      ):
	SCREAMING_SNAKE_CASE :   Dict					  =							"""python/{}""".format(sys.version.split()[0])
	if _torch_available:
		ua += "; torch/{}".format(torch.__version__)
	if isinstance(lowercase_				,      lowercase_):
		ua += "; " + "; ".join("{}/{}".format(lowercase_				,      lowercase_) for k, v in user_agent.items())
	elif isinstance(lowercase_				,      lowercase_):
		ua += "; " + user_agent
	SCREAMING_SNAKE_CASE :   Any					  =							{"""user-agent""": ua}
	if resume_size > 0:
		SCREAMING_SNAKE_CASE :   Tuple					  =							"""bytes=%d-""" % (resume_size,)
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							requests.get(lowercase_				,      stream=lowercase_				,      proxies=lowercase_				,      headers=lowercase_)
	if response.status_code == 416:  # Range not satisfiable
		return
	SCREAMING_SNAKE_CASE :   Tuple					  =							response.headers.get("Content-Length")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							resume_size + int(lowercase_) if content_length is not None else None
	SCREAMING_SNAKE_CASE :   Dict					  =							tqdm(
	    unit="B"				,      unit_scale=lowercase_				,      total=lowercase_				,      initial=lowercase_				,      desc="Downloading"				,      )
	for chunk in response.iter_content(chunk_size=1024):
		if chunk:  # filter out keep-alive new chunks
			progress.update(len(lowercase_))
			temp_file.write(lowercase_)
	progress.close()

def 			lowerCamelCase__			(       _a				,      _a=None				,      _a=False				,      _a=None				,      _a=10				,      _a=False				,      _a=None				,      _a=False				,      ):






	if cache_dir is None:
		SCREAMING_SNAKE_CASE :   int					  =							TRANSFORMERS_CACHE
	if isinstance(lowercase_				,      lowercase_):
		SCREAMING_SNAKE_CASE :   str					  =							str(lowercase_)

	os.makedirs(lowercase_				,      exist_ok=lowercase_)

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
	if not local_files_only:
		try:
			SCREAMING_SNAKE_CASE :   List[Any]					  =							requests.head(lowercase_				,      allow_redirects=lowercase_				,      proxies=lowercase_				,      timeout=lowercase_)
			if response.status_code == 200:
				SCREAMING_SNAKE_CASE :   List[Any]					  =							response.headers.get("ETag")
		except (EnvironmentError, requests.exceptions.Timeout):
			# etag is already None
			pass

	SCREAMING_SNAKE_CASE :   List[Any]					  =							url_to_filename(lowercase_				,      lowercase_)

	# get cache path to put the file
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							os.path.join(lowercase_				,      lowercase_)

	# 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(lowercase_):
			return cache_path
		else:
			SCREAMING_SNAKE_CASE :   Tuple					  =							[
			    file
			    for file in fnmatch.filter(os.listdir(lowercase_)				,      filename + ".*")
			    if not file.endswith(".json") and not file.endswith(".lock")
			]
			if len(lowercase_) > 0:
				return os.path.join(lowercase_				,      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(lowercase_) and not force_download:
		return cache_path

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

		if resume_download:
			SCREAMING_SNAKE_CASE :   Any					  =							cache_path + """.incomplete"""

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

			SCREAMING_SNAKE_CASE :   List[Any]					  =							_resumable_file_manager
			if os.path.exists(lowercase_):
				SCREAMING_SNAKE_CASE :   str					  =							os.stat(lowercase_).st_size
			else:
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							0
		else:
			SCREAMING_SNAKE_CASE :   int					  =							partial(tempfile.NamedTemporaryFile				,      dir=lowercase_				,      delete=lowercase_)
			SCREAMING_SNAKE_CASE :   Dict					  =							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"				,      lowercase_				,      temp_file.name				,      )

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

		os.replace(temp_file.name				,      lowercase_)

		SCREAMING_SNAKE_CASE :   Any					  =							{"""url""": url, """etag""": etag}
		SCREAMING_SNAKE_CASE :   List[Any]					  =							cache_path + """.json"""
		with open(lowercase_				,      "w") as meta_file:
			json.dump(lowercase_				,      lowercase_)

	return cache_path

def 			lowerCamelCase__			(       _a				,      _a=None):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							url.encode("utf-8")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							shaaaa(lowercase_)
	SCREAMING_SNAKE_CASE :   int					  =							url_hash.hexdigest()

	if etag:
		SCREAMING_SNAKE_CASE :   Any					  =							etag.encode("utf-8")
		SCREAMING_SNAKE_CASE :   Any					  =							shaaaa(lowercase_)
		filename += "." + etag_hash.hexdigest()

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

	return filename

def 			lowerCamelCase__			(       _a				,      _a=None				,      _a=False				,      _a=None				,      _a=False				,      _a=None				,      _a=False				,      _a=False				,      _a=False				,      ):






	if cache_dir is None:
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							TRANSFORMERS_CACHE
	if isinstance(lowercase_				,      lowercase_):
		SCREAMING_SNAKE_CASE :   int					  =							str(lowercase_)
	if isinstance(lowercase_				,      lowercase_):
		SCREAMING_SNAKE_CASE :   int					  =							str(lowercase_)

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

	if extract_compressed_file:
		if not is_zipfile(lowercase_) and not tarfile.is_tarfile(lowercase_):
			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/"
		SCREAMING_SNAKE_CASE :   int					  =							os.path.split(lowercase_)
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							output_file.replace("."				,      "-") + """-extracted"""
		SCREAMING_SNAKE_CASE :   Dict					  =							os.path.join(lowercase_				,      lowercase_)

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

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

		return output_path_extracted

	return output_path

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

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   str					  =							requests.get(lowercase_)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							np.array(Image.open(BytesIO(response.content)))
	return img

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

def 			lowerCamelCase__			(       ):
	print(f"{os.path.abspath(os.path.join(lowercase_				,      os.pardir))}/demo.ipynb")

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


def 			lowerCamelCase__			(       _a				,      _a=1):
	return (images[i : i + batch] for i in range(0				,      len(lowercase_)				,      lowercase_))

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     :		Any        = """Create a default config file for Accelerate with only a few flags set."""

def 		UpperCamelCase						(   lowercase_="no"			,			lowercase_ = default_json_config_file			,			lowercase_ = False )		->						Any:


  '''simple docstring'''






  lowercase__	: Any     		=   Path(lowercase_ )
  path.parent.mkdir(parents=lowercase_			,			exist_ok=lowercase_ )
  if path.exists():
    print(
        F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.' )
    return False
  lowercase__	: int     		=   mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}' )
  lowercase__	: Dict     		=   {
      """compute_environment""": """LOCAL_MACHINE""",
      """mixed_precision""": mixed_precision,
  }
  if torch.cuda.is_available():
    lowercase__	: Any     		=   torch.cuda.device_count()
    lowercase__	: Any     		=   num_gpus
    lowercase__	: Optional[int]     		=   False
    if num_gpus > 1:
      lowercase__	: Tuple     		=   """MULTI_GPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_xpu_available() and use_xpu:
    lowercase__	: Union[str, Any]     		=   torch.xpu.device_count()
    lowercase__	: str     		=   num_xpus
    lowercase__	: List[Any]     		=   False
    if num_xpus > 1:
      lowercase__	: str     		=   """MULTI_XPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_npu_available():
    lowercase__	: Tuple     		=   torch.npu.device_count()
    lowercase__	: Union[str, Any]     		=   num_npus
    lowercase__	: Union[str, Any]     		=   False
    if num_npus > 1:
      lowercase__	: List[Any]     		=   """MULTI_NPU"""
    else:
      lowercase__	: int     		=   """NO"""
  else:
    lowercase__	: Union[str, Any]     		=   0
    lowercase__	: str     		=   True
    lowercase__	: Union[str, Any]     		=   1
    lowercase__	: int     		=   """NO"""
  lowercase__	: Tuple     		=   ClusterConfig(**lowercase_ )
  config.to_json_file(lowercase_ )
  return path

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   parser.add_parser("""default"""			,			parents=lowercase_			,			help=lowercase_			,			formatter_class=lowercase_ )
  parser.add_argument(
      """--config_file"""			,			default=lowercase_			,			help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )			,			dest="""save_location"""			,			)

  parser.add_argument(
      """--mixed_precision"""			,			choices=["""no""", """fp16""", """bf16"""]			,			type=lowercase_			,			help="""Whether or not to use mixed precision training. """
      """Choose between FP16 and BF16 (bfloat16) training. """
      """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""			,			default="""no"""			,			)
  parser.set_defaults(func=lowercase_ )
  return parser



def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   write_basic_config(args.mixed_precision			,			args.save_location )
  if config_file:
    print(F'accelerate configuration saved at {config_file}' )






