



"""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

		UpperCAmelCase:  str			     =				True
except ImportError:
		UpperCAmelCase:  str			     =				False


try:
		from torch.hub import _get_torch_home

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

UpperCAmelCase:  Any			     =				os.path.join(torch_cache_home, """transformers""")

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


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase=OBJECTS   ,				__UpperCAmelCase=ATTRIBUTES      ):
		_lowercase				: Union[str, Any]     =   []
		with open(__UpperCAmelCase      ) as f:
				for object in f.readlines():
						vg_classes.append(object.split(""","""      )[0].lower().strip()      )

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





class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  {}






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_ = "root"				,UpperCAmelCase_=0   ):
			_lowercase				: Optional[Any]     =   name
			_lowercase				: int     =   level
			_lowercase				: Union[str, Any]     =   {}
			for k, v in dictionary.items():
					if v is None:
							raise ValueError()
					_lowercase				: List[Any]     =   copy.deepcopy(UpperCAmelCase_   )
					_lowercase				: List[Any]     =   copy.deepcopy(UpperCAmelCase_   )
					if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
							_lowercase				: Optional[Any]     =   Config(UpperCAmelCase_				,name=UpperCAmelCase_				,level=level + 1   )
					_lowercase				: List[str]     =   v
					setattr(self				,UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   d






	def __repr__(     self   ):
			return str(list((self._pointer.keys())   )   )






	def __setattr__(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   val
			_lowercase				: Optional[Any]     =   val
			_lowercase				: Optional[int]     =   key.split("""."""   )
			_lowercase				: int     =   len(UpperCAmelCase_   ) - 1
			_lowercase				: List[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:
									_lowercase				: Tuple     =   val
							else:
									_lowercase				: List[Any]     =   pointer[l]






	def    lowerCamelCase__					(     self   ):
			return self._pointer






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			with open(f"""{file_name}"""				,"""w"""   ) as stream:
					dump(UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			with open(f"""{file_name}"""				,"""w"""   ) as stream:
					json.dump(UpperCAmelCase_				,UpperCAmelCase_   )






	@staticmethod
	def    lowerCamelCase__					(     UpperCAmelCase_   ):
			with open(UpperCAmelCase_   ) as stream:
					_lowercase				: Union[str, Any]     =   load(UpperCAmelCase_				,Loader=UpperCAmelCase_   )
			return data






	def __str__(     self   ):
			_lowercase				: str     =   """    """
			if self._name != "root":
					_lowercase				: List[Any]     =   f"""{t * (self._level-1)}{self._name}:\n"""
			else:
					_lowercase				: Any     =   """"""
			_lowercase				: List[Any]     =   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"""
					_lowercase				: Optional[Any]     =   level
			return r[:-1]






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			_lowercase     ,  _lowercase				: Union[str, Any]     =   cls.get_config_dict(UpperCAmelCase_				,**UpperCAmelCase_   )
			return cls(UpperCAmelCase_   )






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			_lowercase				: Optional[Any]     =   kwargs.pop("""cache_dir"""				,UpperCAmelCase_   )
			_lowercase				: int     =   kwargs.pop("""force_download"""				,UpperCAmelCase_   )
			_lowercase				: List[str]     =   kwargs.pop("""resume_download"""				,UpperCAmelCase_   )
			_lowercase				: Any     =   kwargs.pop("""proxies"""				,UpperCAmelCase_   )
			_lowercase				: List[Any]     =   kwargs.pop("""local_files_only"""				,UpperCAmelCase_   )

			if os.path.isdir(UpperCAmelCase_   ):
					_lowercase				: List[Any]     =   os.path.join(UpperCAmelCase_				,UpperCAmelCase_   )
			elif os.path.isfile(UpperCAmelCase_   ) or is_remote_url(UpperCAmelCase_   ):
					_lowercase				: Optional[Any]     =   pretrained_model_name_or_path
			else:
					_lowercase				: List[Any]     =   hf_bucket_url(UpperCAmelCase_				,filename=UpperCAmelCase_				,use_cdn=UpperCAmelCase_   )

			try:
					# Load from URL or cache if already cached
					_lowercase				: int     =   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

					_lowercase				: Union[str, Any]     =   Config.load_yaml(UpperCAmelCase_   )

			except EnvironmentError:
					_lowercase				: Optional[int]     =   """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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Optional[Any]     =   torch.load("""dump.pt"""   ,				map_location=in_tensor.device      )
		_lowercase				: Optional[int]     =   in_tensor.numpy()
		_lowercase				: Dict     =   out_tensor.numpy()[0]
		print(na.shape   ,				na[0, 0, :5]      )
		print(na.shape   ,				na[0, 0, :5]      )
		assert np.allclose(__UpperCAmelCase   ,				__UpperCAmelCase   ,				rtol=0.0_1   ,				atol=0.1      ), (
		    F"""{sum([1 for x in np.isclose(__UpperCAmelCase   ,				__UpperCAmelCase   ,				rtol=0.0_1   ,				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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: int     =   urlparse(__UpperCAmelCase      )
		return parsed.scheme in ("http", "https")


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=True      ):
		_lowercase				: str     =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		_lowercase				: Any     =   """/""" 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   ,				):
		_lowercase				: str     =   """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
		_lowercase				: str     =   {"""user-agent""": ua}
		if resume_size > 0:
				_lowercase				: Any     =   """bytes=%d-""" % (resume_size,)
		_lowercase				: Tuple     =   requests.get(__UpperCAmelCase   ,				stream=__UpperCAmelCase   ,				proxies=__UpperCAmelCase   ,				headers=__UpperCAmelCase      )
		if response.status_code == 416:  # Range not satisfiable
				return
		_lowercase				: Any     =   response.headers.get("""Content-Length"""      )
		_lowercase				: Optional[Any]     =   resume_size + int(__UpperCAmelCase      ) if content_length is not None else None
		_lowercase				: Tuple     =   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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=10   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=None   ,				__UpperCAmelCase=False   ,				):
		if cache_dir is None:
				_lowercase				: Tuple     =   TRANSFORMERS_CACHE
		if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase      ):
				_lowercase				: List[Any]     =   str(__UpperCAmelCase      )

		os.makedirs(__UpperCAmelCase   ,				exist_ok=__UpperCAmelCase      )

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

		_lowercase				: str     =   url_to_filename(__UpperCAmelCase   ,				__UpperCAmelCase      )

		# get cache path to put the file
		_lowercase				: Union[str, Any]     =   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:
						_lowercase				: List[str]     =   [
						    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.
		_lowercase				: int     =   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:
						_lowercase				: Optional[Any]     =   cache_path + """.incomplete"""

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

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

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

		return cache_path


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=None      ):
		_lowercase				: List[str]     =   url.encode("""utf-8"""      )
		_lowercase				: Dict     =   shaaaa(__UpperCAmelCase      )
		_lowercase				: List[str]     =   url_hash.hexdigest()

		if etag:
				_lowercase				: Dict     =   etag.encode("""utf-8"""      )
				_lowercase				: Optional[int]     =   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   ,				):
		if cache_dir is None:
				_lowercase				: Optional[Any]     =   TRANSFORMERS_CACHE
		if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase      ):
				_lowercase				: Optional[Any]     =   str(__UpperCAmelCase      )
		if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase      ):
				_lowercase				: Optional[Any]     =   str(__UpperCAmelCase      )

		if is_remote_url(__UpperCAmelCase      ):
				# URL, so get it from the cache (downloading if necessary)
				_lowercase				: 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.
				_lowercase				: 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/"
				_lowercase     ,  _lowercase				: int     =   os.path.split(__UpperCAmelCase      )
				_lowercase				: Dict     =   output_file.replace("""."""   ,				"""-"""      ) + """-extracted"""
				_lowercase				: Union[str, Any]     =   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
				_lowercase				: List[str]     =   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      ):
								_lowercase				: Union[str, 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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=","      ):
		assert isinstance(__UpperCAmelCase   ,				__UpperCAmelCase      )
		if os.path.isfile(__UpperCAmelCase      ):
				with open(__UpperCAmelCase      ) as f:
						_lowercase				: Any     =   eval(f.read()      )
		else:
				_lowercase				: int     =   requests.get(__UpperCAmelCase      )
				try:
						_lowercase				: Optional[int]     =   requests.json()
				except Exception:
						_lowercase				: Dict     =   req.content.decode()
						assert data is not None, "could not connect"
						try:
								_lowercase				: str     =   eval(__UpperCAmelCase      )
						except Exception:
								_lowercase				: Optional[Any]     =   data.split("""\n"""      )
				req.close()
		return data


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   requests.get(__UpperCAmelCase      )
		_lowercase				: Tuple     =   np.array(Image.open(BytesIO(response.content      )      )      )
		return img


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


def  __SCREAMING_SNAKE_CASE (  ):
		print(F"""{os.path.abspath(os.path.join(__UpperCAmelCase   ,				os.pardir      )      )}/demo.ipynb"""      )


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


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=1      ):
		return (images[i : i + batch] for i in range(0   ,				len(__UpperCAmelCase      )   ,				__UpperCAmelCase      ))






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       BatchFeature:





        '''simple docstring'''







        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                    F' {self.sampling_rate} and not {sampling_rate}.'						)
        else:
            logger.warning(
                'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                'Failing to do so can result in silent errors that might be hard to debug.'						)

        A__        =  isinstance(lowercase_,np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
            raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        A__        =  is_batched_numpy or (
            isinstance(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            A__        =  raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
            A__        =  [raw_speech]

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

