




"""simple docstring"""





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        _UpperCamelCase     (      UpperCamelCase=OBJECTS					,							UpperCamelCase=ATTRIBUTES				)      ->      Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       []
			with open(UpperCamelCase				) as f:
						for object in f.readlines():
									vg_classes.append(object.split(","				)[0].lower().strip()				)

			__UpperCAmelCase						:			Union[str, Any]												=       []
			with open(UpperCamelCase				) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(","				)[0].lower().strip()				)
			return vg_classes, vg_attrs

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			str												=       OrderedDict()
			with open(UpperCamelCase					,							"rb"				) as f:
						__UpperCAmelCase						:			List[Any]												=       pkl.load(UpperCamelCase				)["model"]
			for k in copy.deepcopy(list(ckp.keys()				)				):
						__UpperCAmelCase						:			Dict												=       ckp.pop(UpperCamelCase				)
						if isinstance(UpperCamelCase					,							np.ndarray				):
									__UpperCAmelCase						:			Any												=       torch.tensor(UpperCamelCase				)
						else:
									assert isinstance(UpperCamelCase					,							torch.tensor				), type(UpperCamelCase				)
						__UpperCAmelCase						:			Tuple												=       v
			return r



class   a__					:
			lowercase_		=						{}


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     dict   ,	UpperCamelCase_   :     str = "root"   ,	UpperCamelCase_   :     List[str]=0):


						"""simple docstring"""




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

						__UpperCAmelCase						:			Tuple												=       d


			def __repr__(   self   :     str):


						"""simple docstring"""




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


			def __setattr__(   self   :     Tuple   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




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


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						return self._pointer


			def    a_  (   self   :     str   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




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


			def    a_  (   self   :     Optional[int]   ,	UpperCamelCase_   :     Union[str, Any]   ,	UpperCamelCase_   :     Any):


						"""simple docstring"""




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


			@staticmethod
			def    a_  (   UpperCamelCase_   :     Any):


						"""simple docstring"""




						with open(UpperCamelCase_) as stream:
									__UpperCAmelCase						:			int												=       load(UpperCamelCase_   ,	Loader=UpperCamelCase_)
						return data


			def __str__(   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       "    "
						if self._name != "root":
									__UpperCAmelCase						:			str												=       F"{t * (self._level-1)}{self._name}:\n"
						else:
									__UpperCAmelCase						:			str												=       ""
						__UpperCAmelCase						:			int												=       self._level
						for i, (k, v) in enumerate(self._pointer.items()):
									if isinstance(UpperCamelCase_   ,	UpperCamelCase_):
												r += F"{t * (self._level)}{v}\n"
												self._level += 1
									else:
												r += F"{t * (self._level)}{k}: {v} ({type(UpperCamelCase_).__name__})\n"
									__UpperCAmelCase						:			Tuple												=       level
						return r[:-1]


			@classmethod
			def    a_  (   cls   :     List[Any]   ,	UpperCamelCase_   :     str   ,	**UpperCamelCase_   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       cls.get_config_dict(UpperCamelCase_   ,	**UpperCamelCase_)
						return cls(UpperCamelCase_)


			@classmethod
			def    a_  (   cls   :     Dict   ,	UpperCamelCase_   :     str   ,	**UpperCamelCase_   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       kwargs.pop("cache_dir"   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       kwargs.pop("force_download"   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       kwargs.pop("resume_download"   ,	UpperCamelCase_)
						__UpperCAmelCase						:			int												=       kwargs.pop("proxies"   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       kwargs.pop("local_files_only"   ,	UpperCamelCase_)

						if os.path.isdir(UpperCamelCase_):
									__UpperCAmelCase						:			Tuple												=       os.path.join(UpperCamelCase_   ,	UpperCamelCase_)
						elif os.path.isfile(UpperCamelCase_) or is_remote_url(UpperCamelCase_):
									__UpperCAmelCase						:			Union[str, Any]												=       pretrained_model_name_or_path
						else:
									__UpperCAmelCase						:			Union[str, Any]												=       hf_bucket_url(UpperCamelCase_   ,	filename=UpperCamelCase_   ,	use_cdn=UpperCamelCase_)

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

									__UpperCAmelCase						:			int												=       Config.load_yaml(UpperCamelCase_)

						except EnvironmentError:
									__UpperCAmelCase						:			Tuple												=       "Can't load config for"
									raise EnvironmentError(UpperCamelCase_)

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

						return Config.load_yaml(UpperCamelCase_), kwargs






def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       torch.load("dump.pt"					,							map_location=in_tensor.device				)
			__UpperCAmelCase						:			Dict												=       in_tensor.numpy()
			__UpperCAmelCase						:			List[str]												=       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()				)*100:.4f} %"
			    " element-wise mismatch"
			)
			raise Exception("tensors are all good"				)

			# Hugging face functions below

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       urlparse(UpperCamelCase				)
			return parsed.scheme in ("http", "https")

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=True				)      ->      str:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			__UpperCAmelCase						:			List[Any]												=       "/" not in model_id
			if legacy_format:
						return f"{endpoint}/{model_id}-{filename}"
			else:
						return f"{endpoint}/{model_id}/{filename}"

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=None					,							UpperCamelCase=0					,							UpperCamelCase=None					,							)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       "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
			__UpperCAmelCase						:			Dict												=       {"user-agent": ua}
			if resume_size > 0:
						__UpperCAmelCase						:			str												=       "bytes=%d-" % (resume_size,)
			__UpperCAmelCase						:			Optional[int]												=       requests.get(UpperCamelCase					,							stream=UpperCamelCase					,							proxies=UpperCamelCase					,							headers=UpperCamelCase				)
			if response.status_code == 416:  # Range not satisfiable
						return
			__UpperCAmelCase						:			str												=       response.headers.get("Content-Length"				)
			__UpperCAmelCase						:			List[Any]												=       resume_size + int(UpperCamelCase				) if content_length is not None else None
			__UpperCAmelCase						:			Any												=       tqdm(
			    unit="B"					,							unit_scale=UpperCamelCase					,							total=UpperCamelCase					,							initial=UpperCamelCase					,							desc="Downloading"					,							)
			for chunk in response.iter_content(chunk_size=1024				):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(UpperCamelCase				)				)
									temp_file.write(UpperCamelCase				)
			progress.close()

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=None					,							UpperCamelCase=False					,							UpperCamelCase=None					,							UpperCamelCase=10					,							UpperCamelCase=False					,							UpperCamelCase=None					,							UpperCamelCase=False					,							)      ->      Dict:


			"""simple docstring"""
			if cache_dir is None:
						__UpperCAmelCase						:			Dict												=       TRANSFORMERS_CACHE
			if isinstance(UpperCamelCase					,							UpperCamelCase				):
						__UpperCAmelCase						:			Any												=       str(UpperCamelCase				)

			os.makedirs(UpperCamelCase					,							exist_ok=UpperCamelCase				)

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

			__UpperCAmelCase						:			Any												=       url_to_filename(UpperCamelCase					,							UpperCamelCase				)

			# get cache path to put the file
			__UpperCAmelCase						:			int												=       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:
									__UpperCAmelCase						:			Dict												=       [
									    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.
			__UpperCAmelCase						:			Dict												=       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:
									__UpperCAmelCase						:			List[Any]												=       cache_path + ".incomplete"

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

									__UpperCAmelCase						:			str												=       _resumable_file_manager
									if os.path.exists(UpperCamelCase				):
												__UpperCAmelCase						:			Dict												=       os.stat(UpperCamelCase				).st_size
									else:
												__UpperCAmelCase						:			List[str]												=       0
						else:
									__UpperCAmelCase						:			int												=       partial(tempfile.NamedTemporaryFile					,							dir=UpperCamelCase					,							delete=UpperCamelCase				)
									__UpperCAmelCase						:			List[Any]												=       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				)

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

			return cache_path

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=None				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       url.encode("utf-8"				)
			__UpperCAmelCase						:			List[str]												=       shaaaa(UpperCamelCase				)
			__UpperCAmelCase						:			Dict												=       url_hash.hexdigest()

			if etag:
						__UpperCAmelCase						:			List[Any]												=       etag.encode("utf-8"				)
						__UpperCAmelCase						:			str												=       shaaaa(UpperCamelCase				)
						filename += "." + etag_hash.hexdigest()

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

			return filename

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=None					,							UpperCamelCase=False					,							UpperCamelCase=None					,							UpperCamelCase=False					,							UpperCamelCase=None					,							UpperCamelCase=False					,							UpperCamelCase=False					,							UpperCamelCase=False					,							)      ->      Optional[int]:


			"""simple docstring"""
			if cache_dir is None:
						__UpperCAmelCase						:			Dict												=       TRANSFORMERS_CACHE
			if isinstance(UpperCamelCase					,							UpperCamelCase				):
						__UpperCAmelCase						:			List[Any]												=       str(UpperCamelCase				)
			if isinstance(UpperCamelCase					,							UpperCamelCase				):
						__UpperCAmelCase						:			Any												=       str(UpperCamelCase				)

			if is_remote_url(UpperCamelCase				):
						# URL, so get it from the cache (downloading if necessary)
						__UpperCAmelCase						:			List[Any]												=       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.
						__UpperCAmelCase						:			List[str]												=       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/"
						__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       os.path.split(UpperCamelCase				)
						__UpperCAmelCase						:			Dict												=       output_file.replace("."					,							"-"				) + "-extracted"
						__UpperCAmelCase						:			int												=       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
						__UpperCAmelCase						:			List[Any]												=       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				):
												__UpperCAmelCase						:			Optional[Any]												=       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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=","				)      ->      Union[str, Any]:


			"""simple docstring"""
			assert isinstance(UpperCamelCase					,							UpperCamelCase				)
			if os.path.isfile(UpperCamelCase				):
						with open(UpperCamelCase				) as f:
									__UpperCAmelCase						:			int												=       eval(f.read()				)
			else:
						__UpperCAmelCase						:			str												=       requests.get(UpperCamelCase				)
						try:
									__UpperCAmelCase						:			List[Any]												=       requests.json()
						except Exception:
									__UpperCAmelCase						:			List[Any]												=       req.content.decode()
									assert data is not None, "could not connect"
									try:
												__UpperCAmelCase						:			Dict												=       eval(UpperCamelCase				)
									except Exception:
												__UpperCAmelCase						:			List[str]												=       data.split("\n"				)
						req.close()
			return data

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       requests.get(UpperCamelCase				)
			__UpperCAmelCase						:			List[Any]												=       np.array(Image.open(BytesIO(response.content				)				)				)
			return img

def        _UpperCamelCase     (      UpperCamelCase				)      ->      int:


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

def        _UpperCamelCase     (      )      ->      Optional[int]:


			"""simple docstring"""
			print(f"{os.path.abspath(os.path.join(UpperCamelCase					,							os.pardir				)				)}/demo.ipynb"				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase="RGB"				)      ->      Tuple:


			"""simple docstring"""
			assert isinstance(UpperCamelCase					,							UpperCamelCase				)
			if os.path.isfile(UpperCamelCase				):
						__UpperCAmelCase						:			List[str]												=       cva.imread(UpperCamelCase				)
			else:
						__UpperCAmelCase						:			List[str]												=       get_image_from_url(UpperCamelCase				)
						assert img is not None, f"could not connect to: {im}"
			__UpperCAmelCase						:			Union[str, Any]												=       cva.cvtColor(UpperCamelCase					,							cva.COLOR_BGR2RGB				)
			if input_format == "RGB":
						__UpperCAmelCase						:			Tuple												=       img[:, :, ::-1]
			return img





def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=1				)      ->      Dict:


			"""simple docstring"""
			return (images[i : i + batch] for i in range(0					,							len(UpperCamelCase				)					,							UpperCamelCase				))









import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


_lowercase		:  Union[str, Any]			       =logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__ = 1_60_00						):
		lowerCamelCase_	:     List[str] 						=      int(round(sample_rate * max_length						)						)
		if len(lowerCAmelCase__						) <= sample_length:
				return wav
		lowerCamelCase_	:     int 						=      randint(0							,len(lowerCAmelCase__						) - sample_length - 1						)
		return wav[random_offset : random_offset + sample_length]






@dataclass
class 						UpperCamelCase_  :
					_a      :						Optional[str]						    =					field(default=snake_case__  ,     metadata={'help': 'Name of a dataset from the datasets package'}							)
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'A file containing the training audio paths and labels.'}							)
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'A file containing the validation audio paths and labels.'}							)
					_a      :						str						    =					field(
					    default='train'  ,     metadata={
					        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
					    }  ,     )
					_a      :						str						    =					field(
					    default='validation'  ,     metadata={
					        'help': (
					            'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
					        )
					    }  ,     )
					_a      :						str						    =					field(
					    default='audio'  ,     metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''}  ,     )
					_a      :						str						    =					field(
					    default='label'  ,     metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''}							)
					_a      :						Optional[int]						    =					field(
					    default=snake_case__  ,     metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }  ,     )
					_a      :						Optional[int]						    =					field(
					    default=snake_case__  ,     metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }  ,     )
					_a      :						float						    =					field(
					    default=2_0  ,     metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'}  ,     )






@dataclass
class 						UpperCamelCase_  :
					_a      :						str						    =					field(
					    default='facebook/wav2vec2-base'  ,     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  ,     )
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'}							)
					_a      :						str						    =					field(
					    default='main'  ,     metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}  ,     )
					_a      :						Optional[str]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Name or path of preprocessor config.'}							)
					_a      :						bool						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Whether to freeze the feature encoder layers of the model.'}							)
					_a      :						bool						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Whether to generate an attention mask in the feature extractor.'}							)
					_a      :						bool						    =					field(
					    default=snake_case__  ,     metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }  ,     )
					_a      :						Optional[bool]						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}							)
					_a      :						bool						    =					field(
					    default=snake_case__  ,     metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}  ,     )



					def   __a     (		self     :     Optional[int]						):
							if not self.freeze_feature_extractor and self.freeze_feature_encoder:
									warnings.warn(
									    'The argument `--freeze_feature_extractor` is deprecated and '
									    'will be removed in a future version. Use `--freeze_feature_encoder`'
									    'instead. Setting `freeze_feature_encoder==True`.'       ,      lowerCamelCase       ,      )
							if self.freeze_feature_extractor and not self.freeze_feature_encoder:
									raise ValueError(
									    'The argument `--freeze_feature_extractor` is deprecated and '
									    'should not be used in combination with `--freeze_feature_encoder`.'
									    'Only make use of `--freeze_feature_encoder`.'						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowerCamelCase_	:     Optional[Any] 						=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
		if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Tuple 						=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
		else:
				lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Dict 						=      parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_audio_classification'							,lowerCAmelCase__							,lowerCAmelCase__						)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,datefmt='%m/%d/%Y %H:%M:%S'							,handlers=[logging.StreamHandler(sys.stdout						)]							,)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		lowerCamelCase_	:     List[str] 						=      training_args.get_process_log_level()
		logger.setLevel(lowerCAmelCase__						)
		transformers.utils.logging.set_verbosity(lowerCAmelCase__						)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} "
		    + F"distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}"						)
		logger.info(F"Training/evaluation parameters {training_args}"						)

		# Set seed before initializing model.
		set_seed(training_args.seed						)

		# Detecting last checkpoint.
		lowerCamelCase_	:     Union[str, Any] 						=      None
		if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
				lowerCamelCase_	:     Optional[Any] 						=      get_last_checkpoint(training_args.output_dir						)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
						raise ValueError(
						    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
						    'Use --overwrite_output_dir to train from scratch.'						)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Initialize our dataset and prepare it for the audio classification task.
		lowerCamelCase_	:     Optional[int] 						=      DatasetDict()
		lowerCamelCase_	:     Dict 						=      load_dataset(
		    data_args.dataset_name							,data_args.dataset_config_name							,split=data_args.train_split_name							,use_auth_token=True if model_args.use_auth_token else None							,)
		lowerCamelCase_	:     List[str] 						=      load_dataset(
		    data_args.dataset_name							,data_args.dataset_config_name							,split=data_args.eval_split_name							,use_auth_token=True if model_args.use_auth_token else None							,)

		if data_args.audio_column_name not in raw_datasets["train"].column_names:
				raise ValueError(
				    F"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
				    'Make sure to set `--audio_column_name` to the correct audio column - one of '
				    F"{', '.join(raw_datasets['train'].column_names						)}."						)

		if data_args.label_column_name not in raw_datasets["train"].column_names:
				raise ValueError(
				    F"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
				    'Make sure to set `--label_column_name` to the correct text column - one of '
				    F"{', '.join(raw_datasets['train'].column_names						)}."						)

		# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
		# transformer outputs in the classifier, but it doesn't always lead to better accuracy
		lowerCamelCase_	:     Dict 						=      AutoFeatureExtractor.from_pretrained(
		    model_args.feature_extractor_name or model_args.model_name_or_path							,return_attention_mask=model_args.attention_mask							,cache_dir=model_args.cache_dir							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)

		# `datasets` takes care of automatically loading and resampling the audio,
		# so we just need to set the correct target sampling rate.
		lowerCamelCase_	:     Optional[Any] 						=      raw_datasets.cast_column(
		    data_args.audio_column_name							,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate						)						)

		lowerCamelCase_	:     Optional[int] 						=      feature_extractor.model_input_names[0]

		def train_transforms(lowerCAmelCase__						):
				lowerCamelCase_	:     Optional[int] 						=      []
				for audio in batch[data_args.audio_column_name]:
						lowerCamelCase_	:     Union[str, Any] 						=      random_subsample(
						    audio['array']							,max_length=data_args.max_length_seconds							,sample_rate=feature_extractor.sampling_rate						)
						subsampled_wavs.append(lowerCAmelCase__						)
				lowerCamelCase_	:     int 						=      feature_extractor(lowerCAmelCase__							,sampling_rate=feature_extractor.sampling_rate						)
				lowerCamelCase_	:     Optional[Any] 						=      {model_input_name: inputs.get(lowerCAmelCase__						)}
				lowerCamelCase_	:     Any 						=      list(batch[data_args.label_column_name]						)

				return output_batch

		def val_transforms(lowerCAmelCase__						):
				lowerCamelCase_	:     Dict 						=      [audio['array'] for audio in batch[data_args.audio_column_name]]
				lowerCamelCase_	:     Optional[Any] 						=      feature_extractor(lowerCAmelCase__							,sampling_rate=feature_extractor.sampling_rate						)
				lowerCamelCase_	:     Optional[int] 						=      {model_input_name: inputs.get(lowerCAmelCase__						)}
				lowerCamelCase_	:     Tuple 						=      list(batch[data_args.label_column_name]						)

				return output_batch

		# Prepare label mappings.
		# We'll include these in the model's config to get human readable labels in the Inference API.
		lowerCamelCase_	:     Optional[int] 						=      raw_datasets['train'].features[data_args.label_column_name].names
		lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      {}, {}
		for i, label in enumerate(lowerCAmelCase__						):
				lowerCamelCase_	:     List[Any] 						=      str(lowerCAmelCase__						)
				lowerCamelCase_	:     Union[str, Any] 						=      label

		# Load the accuracy metric from the datasets package
		lowerCamelCase_	:     Tuple 						=      evaluate.load('accuracy'						)

		# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
		# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
		def compute_metrics(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      np.argmax(eval_pred.predictions							,axis=1						)
				return metric.compute(predictions=lowerCAmelCase__							,references=eval_pred.label_ids						)

		lowerCamelCase_	:     Union[str, Any] 						=      AutoConfig.from_pretrained(
		    model_args.config_name or model_args.model_name_or_path							,num_labels=len(lowerCAmelCase__						)							,labelaid=lowerCAmelCase__							,idalabel=lowerCAmelCase__							,finetuning_task='audio-classification'							,cache_dir=model_args.cache_dir							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)
		lowerCamelCase_	:     Optional[int] 						=      AutoModelForAudioClassification.from_pretrained(
		    model_args.model_name_or_path							,from_tf=bool('.ckpt' in model_args.model_name_or_path						)							,config=lowerCAmelCase__							,cache_dir=model_args.cache_dir							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,)

		# freeze the convolutional waveform encoder
		if model_args.freeze_feature_encoder:
				model.freeze_feature_encoder()

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						lowerCamelCase_	:     List[Any] 						=      (
						    raw_datasets['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
						)
				# Set the training transforms
				raw_datasets["train"].set_transform(lowerCAmelCase__							,output_all_columns=lowerCAmelCase__						)

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						lowerCamelCase_	:     List[str] 						=      (
						    raw_datasets['eval'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
						)
				# Set the validation transforms
				raw_datasets["eval"].set_transform(lowerCAmelCase__							,output_all_columns=lowerCAmelCase__						)

		# Initialize our trainer
		lowerCamelCase_	:     str 						=      Trainer(
		    model=lowerCAmelCase__							,args=lowerCAmelCase__							,train_dataset=raw_datasets['train'] if training_args.do_train else None							,eval_dataset=raw_datasets['eval'] if training_args.do_eval else None							,compute_metrics=lowerCAmelCase__							,tokenizer=lowerCAmelCase__							,)

		# Training
		if training_args.do_train:
				lowerCamelCase_	:     List[Any] 						=      None
				if training_args.resume_from_checkpoint is not None:
						lowerCamelCase_	:     List[Any] 						=      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowerCamelCase_	:     Dict 						=      last_checkpoint
				lowerCamelCase_	:     Optional[int] 						=      trainer.train(resume_from_checkpoint=lowerCAmelCase__						)
				trainer.save_model()
				trainer.log_metrics('train'							,train_result.metrics						)
				trainer.save_metrics('train'							,train_result.metrics						)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				lowerCamelCase_	:     str 						=      trainer.evaluate()
				trainer.log_metrics('eval'							,lowerCAmelCase__						)
				trainer.save_metrics('eval'							,lowerCAmelCase__						)

		# Write model card and (optionally) push to hub
		lowerCamelCase_	:     List[Any] 						=      {
		    'finetuned_from': model_args.model_name_or_path,
		    'tasks': 'audio-classification',
		    'dataset': data_args.dataset_name,
		    'tags': ['audio-classification'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**lowerCAmelCase__						)
		else:
				trainer.create_model_card(**lowerCAmelCase__						)


if __name__ == "__main__":
		main()
