



'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_UpperCamelCase											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Dict     ,      _a :      int     ,      _a :      int     ,      _a :      float     ,      **_a :      Optional[int]  )   ->   List[Any]:
    __lowerCamelCase			:			List[Any]       			=					feature_size
    __lowerCamelCase			:			Union[str, Any]       			=					sampling_rate
    __lowerCamelCase			:			Dict       			=					padding_value

    __lowerCamelCase			:			Optional[int]       			=					kwargs.pop('padding_side'     ,      'right'  )
    __lowerCamelCase			:			Dict       			=					kwargs.pop('return_attention_mask'     ,      _a  )

    super().__init__(**_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]     ,      _a :      Union[bool, str, PaddingStrategy] = True     ,      _a :      Optional[int] = None     ,      _a :      bool = False     ,      _a :      Optional[int] = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[Union[str, TensorType]] = None     ,      )   ->   BatchFeature:
    # If we have a list of dicts, let's convert it in a dict of lists
    # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
    if isinstance(_a     ,      (list, tuple)  ) and isinstance(processed_features[0]     ,      (dict, BatchFeature)  ):
      __lowerCamelCase			:			Union[str, Any]       			=					{
          key: [example[key] for example in processed_features] for key in processed_features[0].keys()
      }

    # The model's main input name, usually `input_values`, has be passed for padding
    if self.model_input_names[0] not in processed_features:
      raise ValueError(
          'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
          f' to this method that includes {self.model_input_names[0]}, but you provided'
          f' {list(processed_features.keys()  )}'  )

    __lowerCamelCase			:			int       			=					processed_features[self.model_input_names[0]]
    __lowerCamelCase			:			Tuple       			=					(
        return_attention_mask if return_attention_mask is not None else self.return_attention_mask
    )

    if len(_a  ) == 0:
      if return_attention_mask:
        __lowerCamelCase			:			Union[str, Any]       			=					[]
      return processed_features

    # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
    # and rebuild them afterwards if no return_tensors is specified
    # Note that we lose the specific device the tensor may be on for PyTorch

    __lowerCamelCase			:			List[str]       			=					required_input[0]
    if isinstance(_a     ,      (list, tuple)  ):
      # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
      __lowerCamelCase			:			int       			=					0
      while len(required_input[index]  ) == 0:
        index += 1
      if index < len(_a  ):
        __lowerCamelCase			:			int       			=					required_input[index][0]

    if return_tensors is None:
      if is_tf_tensor(_a  ):
        __lowerCamelCase			:			Any       			=					'tf'
      elif is_torch_tensor(_a  ):
        __lowerCamelCase			:			int       			=					'pt'
      elif isinstance(_a     ,      (int, float, list, tuple, np.ndarray)  ):
        __lowerCamelCase			:			Optional[Any]       			=					'np'
      else:
        raise ValueError(
            f'type of {first_element} unknown: {type(_a  )}. '
            'Should be one of a python, numpy, pytorch or tensorflow object.'  )

    for key, value in processed_features.items():
      if isinstance(value[0]     ,      (int, float)  ):
        __lowerCamelCase			:			Tuple       			=					to_numpy(_a  )
      else:
        __lowerCamelCase			:			List[Any]       			=					[to_numpy(_a  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
    __lowerCamelCase			:			List[Any]       			=					self._get_padding_strategies(padding=_a     ,      max_length=_a  )

    __lowerCamelCase			:			Any       			=					processed_features[self.model_input_names[0]]

    __lowerCamelCase			:			Any       			=					len(_a  )
    if not all(len(_a  ) == batch_size for v in processed_features.values()  ):
      raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

    __lowerCamelCase			:			List[str]       			=					[]
    for i in range(_a  ):
      __lowerCamelCase			:			Optional[int]       			=					{k: v[i] for k, v in processed_features.items()}
      # truncation
      __lowerCamelCase			:			List[str]       			=					self._truncate(
          _a     ,      max_length=_a     ,      pad_to_multiple_of=_a     ,      truncation=_a     ,      )
      truncated_inputs.append(_a  )

    if padding_strategy == PaddingStrategy.LONGEST:
      # make sure that `max_length` cannot be longer than the longest truncated length
      __lowerCamelCase			:			Any       			=					max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
      __lowerCamelCase			:			Dict       			=					PaddingStrategy.MAX_LENGTH

    __lowerCamelCase			:			Any       			=					{}
    for i in range(_a  ):
      # padding
      __lowerCamelCase			:			Any       			=					self._pad(
          truncated_inputs[i]     ,      max_length=_a     ,      padding_strategy=_a     ,      pad_to_multiple_of=_a     ,      return_attention_mask=_a     ,      )

      for key, value in outputs.items():
        if key not in batch_outputs:
          __lowerCamelCase			:			Any       			=					[]
        if value.dtype is np.dtype(np.floataa  ):
          __lowerCamelCase			:			Dict       			=					value.astype(np.floataa  )
        batch_outputs[key].append(_a  )

    return BatchFeature(_a     ,      tensor_type=_a  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Union[Dict[str, np.ndarray], BatchFeature]     ,      _a :      Optional[int] = None     ,      _a :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,      _a :      Optional[int] = None     ,      _a :      Optional[bool] = None     ,      )   ->   dict:
    __lowerCamelCase			:			Union[str, Any]       			=					processed_features[self.model_input_names[0]]

    if padding_strategy == PaddingStrategy.LONGEST:
      __lowerCamelCase			:			Any       			=					len(_a  )

    if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
      __lowerCamelCase			:			Dict       			=					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

    __lowerCamelCase			:			Dict       			=					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_a  ) < max_length

    if return_attention_mask and "attention_mask" not in processed_features:
      __lowerCamelCase			:			Optional[Any]       			=					np.ones(len(_a  )     ,      dtype=np.intaa  )

    if needs_to_be_padded:
      __lowerCamelCase			:			List[Any]       			=					max_length - len(_a  )
      if self.padding_side == "right":
        if return_attention_mask:
          __lowerCamelCase			:			List[str]       			=					np.pad(
              processed_features['attention_mask']     ,      (0, difference)  )
        __lowerCamelCase			:			Optional[Any]       			=					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        __lowerCamelCase			:			str       			=					np.pad(
            _a     ,      _a     ,      'constant'     ,      constant_values=self.padding_value  )
      elif self.padding_side == "left":
        if return_attention_mask:
          __lowerCamelCase			:			str       			=					np.pad(
              processed_features['attention_mask']     ,      (difference, 0)  )
        __lowerCamelCase			:			Tuple       			=					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        __lowerCamelCase			:			Optional[int]       			=					np.pad(
            _a     ,      _a     ,      'constant'     ,      constant_values=self.padding_value  )
      else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

    return processed_features




  def 		_lowercase	(					self :      Tuple     ,      _a :      Union[Dict[str, np.ndarray], BatchFeature]     ,      _a :      Optional[int] = None     ,      _a :      Optional[int] = None     ,      _a :      Optional[bool] = None     ,      )   ->   List[Any]:
    if not truncation:
      return processed_features
    elif truncation and max_length is None:
      raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

    __lowerCamelCase			:			List[Any]       			=					processed_features[self.model_input_names[0]]

    # find `max_length` that fits `pad_to_multiple_of`
    if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
      __lowerCamelCase			:			Optional[int]       			=					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

    __lowerCamelCase			:			Optional[Any]       			=					len(_a  ) > max_length

    if needs_to_be_truncated:
      __lowerCamelCase			:			str       			=					processed_features[self.model_input_names[0]][:max_length]
      if "attention_mask" in processed_features:
        __lowerCamelCase			:			Tuple       			=					processed_features['attention_mask'][:max_length]

    return processed_features




  def 		_lowercase	(					self :      str     ,      _a :      Union[str, Any]=False     ,      _a :      Optional[int]=None  )   ->   List[str]:

    # Get padding strategy
    if padding is not False:
      if padding is True:
        __lowerCamelCase			:			Tuple       			=					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
      elif not isinstance(_a     ,      _a  ):
        __lowerCamelCase			:			Union[str, Any]       			=					PaddingStrategy(_a  )
      elif isinstance(_a     ,      _a  ):
        __lowerCamelCase			:			Dict       			=					padding
    else:
      __lowerCamelCase			:			Optional[Any]       			=					PaddingStrategy.DO_NOT_PAD

    # Set max length if needed
    if max_length is None:
      if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'  )

        # Test if we have a padding value
    if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
      raise ValueError(
          'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
          ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

    return padding_strategy











'''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											=		True
except ImportError:
   _UpperCamelCase											=		False


try:
   from torch.hub import _get_torch_home

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

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

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

def 			a_	(				_lowerCAmelCase=OBJECTS			,_lowerCAmelCase=ATTRIBUTES			) -> Union[str, Any]:
  __lowerCamelCase			:			Dict       			=					[]
  with open(_lowerCAmelCase			) as f:
    for object in f.readlines():
      vg_classes.append(object.split(','			)[0].lower().strip()			)

  __lowerCamelCase			:			List[Any]       			=					[]
  with open(_lowerCAmelCase			) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(','			)[0].lower().strip()			)
  return vg_classes, vg_attrs

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






class    lowerCamelCase_       :
  """simple docstring"""




  a_				   ={}




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

    __lowerCamelCase			:			Optional[int]       			=					d




  def __repr__(					self :      int  )   ->   Any:
    return str(list((self._pointer.keys())  )  )




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




  def 		_lowercase	(					self :      Dict  )   ->   Optional[Any]:
    return self._pointer




  def 		_lowercase	(					self :      Any     ,      _a :      List[str]     ,      _a :      List[str]  )   ->   Dict:
    with open(f'{file_name}'     ,      'w'  ) as stream:
      dump(_a     ,      _a  )




  def 		_lowercase	(					self :      Any     ,      _a :      Union[str, Any]     ,      _a :      List[Any]  )   ->   List[Any]:
    with open(f'{file_name}'     ,      'w'  ) as stream:
      json.dump(_a     ,      _a  )




  @staticmethod
  def 		_lowercase	(					_a :      Any  )   ->   List[str]:
    with open(_a  ) as stream:
      __lowerCamelCase			:			Dict       			=					load(_a     ,      Loader=_a  )
    return data




  def __str__(					self :      Any  )   ->   List[str]:
    __lowerCamelCase			:			Dict       			=					'    '
    if self._name != "root":
      __lowerCamelCase			:			Dict       			=					f'{t * (self._level-1)}{self._name}:\n'
    else:
      __lowerCamelCase			:			str       			=					''
    __lowerCamelCase			:			List[str]       			=					self._level
    for i, (k, v) in enumerate(self._pointer.items()  ):
      if isinstance(_a     ,      _a  ):
        r += f'{t * (self._level)}{v}\n'
        self._level += 1
      else:
        r += f'{t * (self._level)}{k}: {v} ({type(_a  ).__name__})\n'
      __lowerCamelCase			:			Any       			=					level
    return r[:-1]




  @classmethod
  def 		_lowercase	(					cls :      int     ,      _a :      str     ,      **_a :      List[str]  )   ->   Union[str, Any]:
    __lowerCamelCase			,__lowerCamelCase			:			str       			=					cls.get_config_dict(_a     ,      **_a  )
    return cls(_a  )




  @classmethod
  def 		_lowercase	(					cls :      Dict     ,      _a :      str     ,      **_a :      List[str]  )   ->   List[Any]:
    __lowerCamelCase			:			str       			=					kwargs.pop('cache_dir'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('force_download'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('resume_download'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('proxies'     ,      _a  )
    __lowerCamelCase			:			Optional[Any]       			=					kwargs.pop('local_files_only'     ,      _a  )

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

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

      __lowerCamelCase			:			Optional[Any]       			=					Config.load_yaml(_a  )

    except EnvironmentError:
      __lowerCamelCase			:			Optional[int]       			=					'Can\'t load config for'
      raise EnvironmentError(_a  )

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

    return Config.load_yaml(_a  ), kwargs




def 			a_	(				_lowerCAmelCase			) -> Dict:
  __lowerCamelCase			:			Dict       			=					torch.load('dump.pt'			,map_location=in_tensor.device			)
  __lowerCamelCase			:			Optional[int]       			=					in_tensor.numpy()
  __lowerCamelCase			:			int       			=					out_tensor.numpy()[0]
  print(na.shape			,na[0, 0, :5]			)
  print(na.shape			,na[0, 0, :5]			)
  assert np.allclose(_lowerCAmelCase			,_lowerCAmelCase			,rtol=0.01			,atol=0.1			), (
      F'{sum([1 for x in np.isclose(_lowerCAmelCase			,_lowerCAmelCase			,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 			a_	(				_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			List[str]       			=					urlparse(_lowerCAmelCase			)
  return parsed.scheme in ("http", "https")

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=True			) -> str:
  __lowerCamelCase			:			Any       			=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  __lowerCamelCase			:			List[str]       			=					'/' not in model_id
  if legacy_format:
    return F'{endpoint}/{model_id}-{filename}'
  else:
    return F'{endpoint}/{model_id}/{filename}'

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

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=10			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,) -> List[str]:
  if cache_dir is None:
    __lowerCamelCase			:			Any       			=					TRANSFORMERS_CACHE
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			Tuple       			=					str(_lowerCAmelCase			)

  os.makedirs(_lowerCAmelCase			,exist_ok=_lowerCAmelCase			)

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

  __lowerCamelCase			:			Tuple       			=					url_to_filename(_lowerCAmelCase			,_lowerCAmelCase			)

  # get cache path to put the file
  __lowerCamelCase			:			Tuple       			=					os.path.join(_lowerCAmelCase			,_lowerCAmelCase			)

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

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

    if resume_download:
      __lowerCamelCase			:			Optional[int]       			=					cache_path + '.incomplete'

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

      __lowerCamelCase			:			Optional[int]       			=					_resumable_file_manager
      if os.path.exists(_lowerCAmelCase			):
        __lowerCamelCase			:			int       			=					os.stat(_lowerCAmelCase			).st_size
      else:
        __lowerCamelCase			:			Dict       			=					0
    else:
      __lowerCamelCase			:			str       			=					partial(tempfile.NamedTemporaryFile			,dir=_lowerCAmelCase			,delete=_lowerCAmelCase			)
      __lowerCamelCase			:			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'			,_lowerCAmelCase			,temp_file.name			,)

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

    os.replace(temp_file.name			,_lowerCAmelCase			)

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

  return cache_path

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			) -> Dict:
  __lowerCamelCase			:			Any       			=					url.encode('utf-8'			)
  __lowerCamelCase			:			Tuple       			=					shaaaa(_lowerCAmelCase			)
  __lowerCamelCase			:			Dict       			=					url_hash.hexdigest()

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

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

  return filename

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=False			,_lowerCAmelCase=False			,) -> str:
  if cache_dir is None:
    __lowerCamelCase			:			Optional[Any]       			=					TRANSFORMERS_CACHE
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			List[str]       			=					str(_lowerCAmelCase			)
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			List[str]       			=					str(_lowerCAmelCase			)

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

  if extract_compressed_file:
    if not is_zipfile(_lowerCAmelCase			) and not tarfile.is_tarfile(_lowerCAmelCase			):
      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/"
    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					os.path.split(_lowerCAmelCase			)
    __lowerCamelCase			:			str       			=					output_file.replace('.'			,'-'			) + '-extracted'
    __lowerCamelCase			:			Optional[int]       			=					os.path.join(_lowerCAmelCase			,_lowerCAmelCase			)

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

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

    return output_path_extracted

  return output_path

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

def 			a_	(				_lowerCAmelCase			) -> List[Any]:
  __lowerCamelCase			:			str       			=					requests.get(_lowerCAmelCase			)
  __lowerCamelCase			:			Dict       			=					np.array(Image.open(BytesIO(response.content			)			)			)
  return img

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

def 			a_	(				) -> Dict:
  print(F'{os.path.abspath(os.path.join(_lowerCAmelCase			,os.pardir			)			)}/demo.ipynb'			)

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase="RGB"			) -> List[Any]:
  assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			)
  if os.path.isfile(_lowerCAmelCase			):
    __lowerCamelCase			:			Any       			=					cva.imread(_lowerCAmelCase			)
  else:
    __lowerCamelCase			:			int       			=					get_image_from_url(_lowerCAmelCase			)
    assert img is not None, F'could not connect to: {im}'
  __lowerCamelCase			:			Dict       			=					cva.cvtColor(_lowerCAmelCase			,cva.COLOR_BGR2RGB			)
  if input_format == "RGB":
    __lowerCamelCase			:			Any       			=					img[:, :, ::-1]
  return img


def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=1			) -> Union[str, Any]:
  return (images[i : i + batch] for i in range(0			,len(_lowerCAmelCase			)			,_lowerCAmelCase			))






