
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

		lowercase        =  True
except ImportError:
		lowercase        =  False


try:
		from torch.hub import _get_torch_home

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

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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple=OBJECTS			,				UpperCAmelCase__				:      Tuple=ATTRIBUTES       )  ->    Optional[Any]:
							lowerCamelCase_							  =					[]
							with open(UpperCAmelCase__       ) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(""","""       )[0].lower().strip()       )

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



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





class       __A:
					SCREAMING_SNAKE_CASE			    =    {}

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

												lowerCamelCase_							  =					d

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

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

					def    lowercase__      (	self					:     int   ):
												return self._pointer

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												with open(F'''{file_name}'''   ,					"""w"""   ) as stream:
																			dump(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Dict   ):
												with open(F'''{file_name}'''   ,					"""w"""   ) as stream:
																			json.dump(__UpperCamelCase   ,					__UpperCamelCase   )

					@staticmethod
					def    lowercase__      (	__UpperCamelCase					:     str   ):
												with open(__UpperCamelCase   ) as stream:
																			lowerCamelCase_							  =					load(__UpperCamelCase   ,					Loader=__UpperCamelCase   )
												return data

					def __str__(	self					:     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(__UpperCamelCase   ,					__UpperCamelCase   ):
																										r += F'''{t * (self._level)}{v}\n'''
																										self._level += 1
																			else:
																										r += F'''{t * (self._level)}{k}: {v} ({type(__UpperCamelCase   ).__name__})\n'''
																			lowerCamelCase_							  =					level
												return r[:-1]

					@classmethod
					def    lowercase__      (	cls					:     Tuple   ,					__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					cls.get_config_dict(__UpperCamelCase   ,					**__UpperCamelCase   )
												return cls(__UpperCamelCase   )



					@classmethod
					def    lowercase__      (	cls					:     int   ,					__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					kwargs.pop("""cache_dir"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""force_download"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""resume_download"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""proxies"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""local_files_only"""   ,					__UpperCamelCase   )

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

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

																			lowerCamelCase_							  =					Config.load_yaml(__UpperCamelCase   )

												except EnvironmentError:
																			lowerCamelCase_							  =					"""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      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    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(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()       )*1_0_0:.4f} %'''
							    " element-wise mismatch"
							)
							raise Exception("""tensors are all good"""       )

							# Hugging face functions below



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict       )  ->    List[Any]:
							lowerCamelCase_							  =					urlparse(UpperCAmelCase__       )
							return parsed.scheme in ("http", "https")



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Dict=True       )  ->    str:
							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      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Tuple=None			,				UpperCAmelCase__				:      Dict=0			,				UpperCAmelCase__				:      Any=None			,				)  ->    List[str]:
							lowerCamelCase_							  =					"""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
							lowerCamelCase_							  =					{"""user-agent""": ua}
							if resume_size > 0:
														lowerCamelCase_							  =					"""bytes=%d-""" % (resume_size,)
							lowerCamelCase_							  =					requests.get(UpperCAmelCase__			,				stream=UpperCAmelCase__			,				proxies=UpperCAmelCase__			,				headers=UpperCAmelCase__       )
							if response.status_code == 4_1_6:  # Range not satisfiable
														return
							lowerCamelCase_							  =					response.headers.get("""Content-Length"""       )
							lowerCamelCase_							  =					resume_size + int(UpperCAmelCase__       ) if content_length is not None else None
							lowerCamelCase_							  =					tqdm(
							    unit="""B"""			,				unit_scale=UpperCAmelCase__			,				total=UpperCAmelCase__			,				initial=UpperCAmelCase__			,				desc="""Downloading"""			,				)
							for chunk in response.iter_content(chunk_size=1_0_2_4       ):
														if chunk:  # filter out keep-alive new chunks
																					progress.update(len(UpperCAmelCase__       )       )
																					temp_file.write(UpperCAmelCase__       )
							progress.close()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      Any=False			,				UpperCAmelCase__				:      Tuple=None			,				UpperCAmelCase__				:      str=1_0			,				UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      List[Any]=None			,				UpperCAmelCase__				:      List[Any]=False			,				)  ->    str:
							if cache_dir is None:
														lowerCamelCase_							  =					TRANSFORMERS_CACHE
							if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

							os.makedirs(UpperCAmelCase__			,				exist_ok=UpperCAmelCase__       )

							lowerCamelCase_							  =					None
							if not local_files_only:
														try:
																					lowerCamelCase_							  =					requests.head(UpperCAmelCase__			,				allow_redirects=UpperCAmelCase__			,				proxies=UpperCAmelCase__			,				timeout=UpperCAmelCase__       )
																					if response.status_code == 2_0_0:
																												lowerCamelCase_							  =					response.headers.get("""ETag"""       )
														except (EnvironmentError, requests.exceptions.Timeout):
																					# etag is already None
																					pass

							lowerCamelCase_							  =					url_to_filename(UpperCAmelCase__			,				UpperCAmelCase__       )

							# get cache path to put the file
							lowerCamelCase_							  =					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:
																					lowerCamelCase_							  =					[
																					    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.
							lowerCamelCase_							  =					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:
																					lowerCamelCase_							  =					cache_path + """.incomplete"""

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

																					lowerCamelCase_							  =					_resumable_file_manager
																					if os.path.exists(UpperCAmelCase__       ):
																												lowerCamelCase_							  =					os.stat(UpperCAmelCase__       ).st_size
																					else:
																												lowerCamelCase_							  =					0
														else:
																					lowerCamelCase_							  =					partial(tempfile.NamedTemporaryFile			,				dir=UpperCAmelCase__			,				delete=UpperCAmelCase__       )
																					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"""			,				UpperCAmelCase__			,				temp_file.name			,				)

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

														os.replace(temp_file.name			,				UpperCAmelCase__       )

														lowerCamelCase_							  =					{"""url""": url, """etag""": etag}
														lowerCamelCase_							  =					cache_path + """.json"""
														with open(UpperCAmelCase__			,				"""w"""       ) as meta_file:
																					json.dump(UpperCAmelCase__			,				UpperCAmelCase__       )

							return cache_path



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      List[str]=None       )  ->    List[Any]:
							lowerCamelCase_							  =					url.encode("""utf-8"""       )
							lowerCamelCase_							  =					shaaaa(UpperCAmelCase__       )
							lowerCamelCase_							  =					url_hash.hexdigest()

							if etag:
														lowerCamelCase_							  =					etag.encode("""utf-8"""       )
														lowerCamelCase_							  =					shaaaa(UpperCAmelCase__       )
														filename += "." + etag_hash.hexdigest()

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

							return filename



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      List[str]=None			,				UpperCAmelCase__				:      List[str]=False			,				UpperCAmelCase__				:      Any=None			,				UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      List[Any]=None			,				UpperCAmelCase__				:      Optional[int]=False			,				UpperCAmelCase__				:      Tuple=False			,				UpperCAmelCase__				:      Any=False			,				)  ->    str:
							if cache_dir is None:
														lowerCamelCase_							  =					TRANSFORMERS_CACHE
							if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					str(UpperCAmelCase__       )
							if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					requests.get(UpperCAmelCase__       )
							lowerCamelCase_							  =					np.array(Image.open(BytesIO(response.content       )       )       )
							return img



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



def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							print(F'''{os.path.abspath(os.path.join(UpperCAmelCase__			,				os.pardir       )       )}/demo.ipynb'''       )



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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Tuple=1       )  ->    Optional[int]:
							return (images[i : i + batch] for i in range(0			,				len(UpperCAmelCase__       )			,				UpperCAmelCase__       ))







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint





@require_torch
class       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Optional[Any]   ):
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

												lowerCamelCase_							  =					[[1, 2, 4], [1, 2, 3, 4]]
												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )
												self.assertTrue(isinstance(dc.token_ids   ,					__UpperCamelCase   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

					def    lowercase__      (	self					:     List[Any]   ):
												# We can't have constraints that are complete subsets of another. This leads to a preverse
												# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
												# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
												# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
												# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
												lowerCamelCase_							  =					[[1, 2], [1, 2, 3, 4]]

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(__UpperCamelCase   )  # fails here

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(3   )
												lowerCamelCase_							  =					stepped is True and completed is True and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 3]   )



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(4   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2, 4]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

												dc.reset()

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 3   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 2   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.remaining() == 0   )
												self.assertTrue(dc.current_seq == [1, 2, 5]   )





