


'''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 					_snake_case   (     A=OBJECTS ,    A=ATTRIBUTES       )						->    Optional[Any]:
							lowerCAmelCase__          =	[]
							with open(A       ) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(''','''       )[0].lower().strip()       )

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


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




class 		a__				:





							'''simple docstring'''






							lowercase__			:   Optional[Any]							=      {}







							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_ = "root"					,    lowerCamelCase_=0   )       ->							List[str]:
														lowerCAmelCase__          =	name
														lowerCAmelCase__          =	level
														lowerCAmelCase__          =	{}
														for k, v in dictionary.items():
																					if v is None:
																												raise ValueError()
																					lowerCAmelCase__          =	copy.deepcopy(A_   )
																					lowerCAmelCase__          =	copy.deepcopy(A_   )
																					if isinstance(A_					,    A_   ):
																												lowerCAmelCase__          =	Config(A_					,    name=A_					,    level=level + 1   )
																					lowerCAmelCase__          =	v
																					setattr(self					,    A_					,    A_   )

														lowerCAmelCase__          =	d







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







							def __setattr__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							List[str]:
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	key.split('''.'''   )
														lowerCAmelCase__          =	len(A_   ) - 1
														lowerCAmelCase__          =	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__          =	val
																												else:
																																			lowerCAmelCase__          =	pointer[l]







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														return self._pointer







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Optional[Any]:






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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							int:






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







							@staticmethod
							def   __SCREAMING_SNAKE_CASE							(		lowerCamelCase_   )       ->							str:






														with open(A_   ) as stream:
																					lowerCAmelCase__          =	load(A_					,    Loader=A_   )
														return data







							def __str__(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	'''    '''
														if self._name != "root":
																					lowerCAmelCase__          =	F"""{t * (self._level-1)}{self._name}:\n"""
														else:
																					lowerCAmelCase__          =	''''''
														lowerCAmelCase__          =	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__          =	level
														return r[:-1]







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_					,    **lowerCamelCase_   )       ->							List[Any]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	cls.get_config_dict(A_					,    **A_   )
														return cls(A_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_					,    **lowerCamelCase_   )       ->							List[str]:
														lowerCAmelCase__          =	kwargs.pop('''cache_dir'''					,    A_   )
														lowerCAmelCase__          =	kwargs.pop('''force_download'''					,    A_   )
														lowerCAmelCase__          =	kwargs.pop('''resume_download'''					,    A_   )
														lowerCAmelCase__          =	kwargs.pop('''proxies'''					,    A_   )
														lowerCAmelCase__          =	kwargs.pop('''local_files_only'''					,    A_   )

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

														try:
																					# Load from URL or cache if already cached
																					lowerCAmelCase__          =	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__          =	Config.load_yaml(A_   )

														except EnvironmentError:
																					lowerCAmelCase__          =	'''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 					_snake_case   (     A       )						->    Union[str, Any]:
							lowerCAmelCase__          =	torch.load('''dump.pt''' ,    map_location=in_tensor.device       )
							lowerCAmelCase__          =	in_tensor.numpy()
							lowerCAmelCase__          =	out_tensor.numpy()[0]
							print(na.shape ,    na[0, 0, :5]       )
							print(na.shape ,    na[0, 0, :5]       )
							assert np.allclose(A ,    A ,    rtol=0.01 ,    atol=0.1       ), (
							    F"""{sum([1 for x in np.isclose(A ,    A ,    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 					_snake_case   (     A       )						->    Tuple:
							lowerCAmelCase__          =	urlparse(A       )
							return parsed.scheme in ("http", "https")


def 					_snake_case   (     A ,    A ,    A=True       )						->    Optional[Any]:
							lowerCAmelCase__          =	CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
							lowerCAmelCase__          =	'''/''' not in model_id
							if legacy_format:
														return F"""{endpoint}/{model_id}-{filename}"""
							else:
														return F"""{endpoint}/{model_id}/{filename}"""


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


def 					_snake_case   (     A ,    A=None ,    A=False ,    A=None ,    A=10 ,    A=False ,    A=None ,    A=False ,    )						->    Union[str, Any]:
							if cache_dir is None:
														lowerCAmelCase__          =	TRANSFORMERS_CACHE
							if isinstance(A ,    A       ):
														lowerCAmelCase__          =	str(A       )

							os.makedirs(A ,    exist_ok=A       )

							lowerCAmelCase__          =	None
							if not local_files_only:
														try:
																					lowerCAmelCase__          =	requests.head(A ,    allow_redirects=A ,    proxies=A ,    timeout=A       )
																					if response.status_code == 200:
																												lowerCAmelCase__          =	response.headers.get('''ETag'''       )
														except (EnvironmentError, requests.exceptions.Timeout):
																					# etag is already None
																					pass

							lowerCAmelCase__          =	url_to_filename(A ,    A       )

							# get cache path to put the file
							lowerCAmelCase__          =	os.path.join(A ,    A       )

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

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

														if resume_download:
																					lowerCAmelCase__          =	cache_path + '''.incomplete'''

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

																					lowerCAmelCase__          =	_resumable_file_manager
																					if os.path.exists(A       ):
																												lowerCAmelCase__          =	os.stat(A       ).st_size
																					else:
																												lowerCAmelCase__          =	0
														else:
																					lowerCAmelCase__          =	partial(tempfile.NamedTemporaryFile ,    dir=A ,    delete=A       )
																					lowerCAmelCase__          =	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''' ,    A ,    temp_file.name ,    )

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

														os.replace(temp_file.name ,    A       )

														lowerCAmelCase__          =	{'''url''': url, '''etag''': etag}
														lowerCAmelCase__          =	cache_path + '''.json'''
														with open(A ,    '''w'''       ) as meta_file:
																					json.dump(A ,    A       )

							return cache_path


def 					_snake_case   (     A ,    A=None       )						->    Union[str, Any]:
							lowerCAmelCase__          =	url.encode('''utf-8'''       )
							lowerCAmelCase__          =	shaaaa(A       )
							lowerCAmelCase__          =	url_hash.hexdigest()

							if etag:
														lowerCAmelCase__          =	etag.encode('''utf-8'''       )
														lowerCAmelCase__          =	shaaaa(A       )
														filename += "." + etag_hash.hexdigest()

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

							return filename


def 					_snake_case   (     A ,    A=None ,    A=False ,    A=None ,    A=False ,    A=None ,    A=False ,    A=False ,    A=False ,    )						->    List[str]:
							if cache_dir is None:
														lowerCAmelCase__          =	TRANSFORMERS_CACHE
							if isinstance(A ,    A       ):
														lowerCAmelCase__          =	str(A       )
							if isinstance(A ,    A       ):
														lowerCAmelCase__          =	str(A       )

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

							if extract_compressed_file:
														if not is_zipfile(A       ) and not tarfile.is_tarfile(A       ):
																					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__          =	os.path.split(A       )
														lowerCAmelCase__          =	output_file.replace('''.''' ,    '''-'''       ) + '''-extracted'''
														lowerCAmelCase__          =	os.path.join(A ,    A       )

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

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

														return output_path_extracted

							return output_path


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


def 					_snake_case   (     A       )						->    Any:
							lowerCAmelCase__          =	requests.get(A       )
							lowerCAmelCase__          =	np.array(Image.open(BytesIO(response.content       )       )       )
							return img


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


def 					_snake_case   (     )						->    List[str]:
							print(F"""{os.path.abspath(os.path.join(A ,    os.pardir       )       )}/demo.ipynb"""       )


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






def 					_snake_case   (     A ,    A=1       )						->    List[str]:
							return (images[i : i + batch] for i in range(0 ,    len(A       ) ,    A       ))

'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							UpperCamelCase					       =			np.stack((horizontal_flow, vertical_flow)						,			2)

							# Create a grid of all pixel coordinates and subtract the flow to get the
							# target pixels coordinates
							UpperCamelCase					       =			np.stack(
							    np.meshgrid(np.arange(0						,			image.shape[1])						,			np.arange(0						,			image.shape[0]))						,			2)
							UpperCamelCase					       =			np.round(grid - flow).astype(np.intaa)

							# Find the locations outside of the original image
							UpperCamelCase					       =			(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]))
							UpperCamelCase					       =			0

							UpperCamelCase					       =			image[grid[:, :, 1], grid[:, :, 0]]

							# Set pixels at invalid locations to 0
							UpperCamelCase					       =			0

							return warped







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# Prepare kernels for the calculation of the derivatives and the average velocity
							UpperCamelCase					       =			np.array([[-1, 1], [-1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[-1, -1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array([[1, 1], [1, 1]]) * 0.25
							UpperCamelCase					       =			np.array(
							    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

							# Iteratively refine the flow
							for _ in range(A):
														UpperCamelCase					       =			warp(A						,			A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A) + convolve(A						,			-kernel_t)

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# This updates the flow as proposed in the paper (Step 12)
														UpperCamelCase					       =			(
														    derivative_x * avg_horizontal_velocity
														    + derivative_y * avg_vertical_velocity
														    + derivative_t
														)
														UpperCamelCase					       =			update / (alpha**2 + derivative_x**2 + derivative_y**2)

														UpperCamelCase					       =			avg_horizontal_velocity - derivative_x * update
														UpperCamelCase					       =			avg_vertical_velocity - derivative_y * update

							return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()




