


'''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

						__magic_name__   :   List[str]  							=True
except ImportError:
						__magic_name__   :   Optional[int]  							=False


try:
						from torch.hub import _get_torch_home

						__magic_name__   :   List[str]  							=_get_torch_home()
except ImportError:
						__magic_name__   :   Optional[int]  							=os.path.expanduser(
						    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
						)

__magic_name__   :   Tuple  							=os.path.join(torch_cache_home, 'transformers')

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




def      __snake_case       (       lowerCamelCase_ :				Optional[Any]=OBJECTS	,  lowerCamelCase_ :				Optional[int]=ATTRIBUTES						):
				'''simple docstring'''




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

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




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




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






class 		UpperCamelCase_		:





						"""simple docstring"""




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

										__magic_name__	  = d
						def __repr__( self				:					Tuple  )							->					List[Any]:
										return str(list((self._pointer.keys())  )  )
						def __setattr__( self				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]  )							->					List[str]:
										__magic_name__	  = val
										__magic_name__	  = val
										__magic_name__	  = key.split("."  )
										__magic_name__	  = len(_lowerCamelCase  ) - 1
										__magic_name__	  = self._pointer
										if len(_lowerCamelCase  ) > 1:
														for i, l in enumerate(_lowerCamelCase  ):
																		if hasattr(self	,				_lowerCamelCase  ) and isinstance(getattr(self	,				_lowerCamelCase  )	,				_lowerCamelCase  ):
																						setattr(getattr(self	,				_lowerCamelCase  )	,				".".join(levels[i:]  )	,				_lowerCamelCase  )
																		if l == last_level:
																						__magic_name__	  = val
																		else:
																						__magic_name__	  = pointer[l]
						def 					__A					( self				:					Dict  )							->					Optional[int]:
										return self._pointer
						def 					__A					( self				:					int	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]  )							->					List[str]:
										with open(f'{file_name}'	,				"w"  ) as stream:
														dump(_lowerCamelCase	,				_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					int  )							->					Tuple:
										with open(f'{file_name}'	,				"w"  ) as stream:
														json.dump(_lowerCamelCase	,				_lowerCamelCase  )
						@staticmethod
						def 					__A					( _lowerCamelCase				:					str  )							->					List[str]:
										with open(_lowerCamelCase  ) as stream:
														__magic_name__	  = load(_lowerCamelCase	,				Loader=_lowerCamelCase  )
										return data
						def __str__( self				:					Union[str, Any]  )							->					List[str]:
										__magic_name__	  = "    "
										if self._name != "root":
														__magic_name__	  = f'{t * (self._level-1)}{self._name}:\n'
										else:
														__magic_name__	  = ""
										__magic_name__	  = self._level
										for i, (k, v) in enumerate(self._pointer.items()  ):
														if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
																		r += f'{t * (self._level)}{v}\n'
																		self._level += 1
														else:
																		r += f'{t * (self._level)}{k}: {v} ({type(_lowerCamelCase  ).__name__})\n'
														__magic_name__	  = level
										return r[:-1]
						@classmethod
						def 					__A					( cls				:					str	,				_lowerCamelCase				:					str	,				**_lowerCamelCase				:					List[Any]  )							->					str:
										__magic_name__  ,      __magic_name__	  = cls.get_config_dict(_lowerCamelCase	,				**_lowerCamelCase  )
										return cls(_lowerCamelCase  )

						@classmethod
						def 					__A					( cls				:					Dict	,				_lowerCamelCase				:					str	,				**_lowerCamelCase				:					Dict  )							->					Dict:
										__magic_name__	  = kwargs.pop("cache_dir"	,				_lowerCamelCase  )
										__magic_name__	  = kwargs.pop("force_download"	,				_lowerCamelCase  )
										__magic_name__	  = kwargs.pop("resume_download"	,				_lowerCamelCase  )
										__magic_name__	  = kwargs.pop("proxies"	,				_lowerCamelCase  )
										__magic_name__	  = kwargs.pop("local_files_only"	,				_lowerCamelCase  )

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

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

														__magic_name__	  = Config.load_yaml(_lowerCamelCase  )

										except EnvironmentError:
														__magic_name__	  = "Can't load config for"
														raise EnvironmentError(_lowerCamelCase  )

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

										return Config.load_yaml(_lowerCamelCase  ), kwargs





def      __snake_case       (       lowerCamelCase_ :				Optional[Any]						):
				'''simple docstring'''




				__magic_name__	  = torch.load("dump.pt"	,  map_location=in_tensor.device						)
				__magic_name__	  = in_tensor.numpy()
				__magic_name__	  = 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      __snake_case       (       lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				__magic_name__	  = urlparse(lowerCamelCase_						)
				return parsed.scheme in ("http", "https")




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				Dict=True						):
				'''simple docstring'''




				__magic_name__	  = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				__magic_name__	  = "/" not in model_id
				if legacy_format:
								return F'{endpoint}/{model_id}-{filename}'
				else:
								return F'{endpoint}/{model_id}/{filename}'




def      __snake_case       (       lowerCamelCase_ :				Dict	,  lowerCamelCase_ :				Any	,  lowerCamelCase_ :				Optional[int]=None	,  lowerCamelCase_ :				Optional[int]=0	,  lowerCamelCase_ :				str=None	,  ):
				'''simple docstring'''




				__magic_name__	  = "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
				__magic_name__	  = {"user-agent": ua}
				if resume_size > 0:
								__magic_name__	  = "bytes=%d-" % (resume_size,)
				__magic_name__	  = requests.get(lowerCamelCase_	,  stream=lowerCamelCase_	,  proxies=lowerCamelCase_	,  headers=lowerCamelCase_						)
				if response.status_code == 416:  # Range not satisfiable
								return
				__magic_name__	  = response.headers.get("Content-Length"						)
				__magic_name__	  = resume_size + int(lowerCamelCase_						) if content_length is not None else None
				__magic_name__	  = 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      __snake_case       (       lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				int=None	,  lowerCamelCase_ :				Dict=False	,  lowerCamelCase_ :				Tuple=None	,  lowerCamelCase_ :				List[str]=10	,  lowerCamelCase_ :				Optional[Any]=False	,  lowerCamelCase_ :				List[str]=None	,  lowerCamelCase_ :				Any=False	,  ):
				'''simple docstring'''




				if cache_dir is None:
								__magic_name__	  = TRANSFORMERS_CACHE
				if isinstance(lowerCamelCase_	,  lowerCamelCase_						):
								__magic_name__	  = str(lowerCamelCase_						)

				os.makedirs(lowerCamelCase_	,  exist_ok=lowerCamelCase_						)

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

				__magic_name__	  = url_to_filename(lowerCamelCase_	,  lowerCamelCase_						)

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

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

												__magic_name__	  = _resumable_file_manager
												if os.path.exists(lowerCamelCase_						):
																__magic_name__	  = os.stat(lowerCamelCase_						).st_size
												else:
																__magic_name__	  = 0
								else:
												__magic_name__	  = partial(tempfile.NamedTemporaryFile	,  dir=lowerCamelCase_	,  delete=lowerCamelCase_						)
												__magic_name__	  = 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_						)

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

				return cache_path




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				Union[str, Any]=None						):
				'''simple docstring'''




				__magic_name__	  = url.encode("utf-8"						)
				__magic_name__	  = shaaaa(lowerCamelCase_						)
				__magic_name__	  = url_hash.hexdigest()

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

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

				return filename




def      __snake_case       (       lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				List[str]=None	,  lowerCamelCase_ :				Optional[Any]=False	,  lowerCamelCase_ :				str=None	,  lowerCamelCase_ :				List[str]=False	,  lowerCamelCase_ :				Tuple=None	,  lowerCamelCase_ :				List[Any]=False	,  lowerCamelCase_ :				List[Any]=False	,  lowerCamelCase_ :				Tuple=False	,  ):
				'''simple docstring'''




				if cache_dir is None:
								__magic_name__	  = TRANSFORMERS_CACHE
				if isinstance(lowerCamelCase_	,  lowerCamelCase_						):
								__magic_name__	  = str(lowerCamelCase_						)
				if isinstance(lowerCamelCase_	,  lowerCamelCase_						):
								__magic_name__	  = str(lowerCamelCase_						)

				if is_remote_url(lowerCamelCase_						):
								# URL, so get it from the cache (downloading if necessary)
								__magic_name__	  = 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.
								__magic_name__	  = 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/"
								__magic_name__  ,      __magic_name__	  = os.path.split(lowerCamelCase_						)
								__magic_name__	  = output_file.replace("."	,  "-"						) + "-extracted"
								__magic_name__	  = 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
								__magic_name__	  = 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_						):
																__magic_name__	  = 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      __snake_case       (       lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				List[Any]=","						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Any						):
				'''simple docstring'''




				__magic_name__	  = requests.get(lowerCamelCase_						)
				__magic_name__	  = np.array(Image.open(BytesIO(response.content						)						)						)
				return img




def      __snake_case       (       lowerCamelCase_ :				List[Any]						):
				'''simple docstring'''




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




def      __snake_case       (       ):
				'''simple docstring'''




				print(F'{os.path.abspath(os.path.join(lowerCamelCase_	,  os.pardir						)						)}/demo.ipynb'						)




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				int="RGB"						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Tuple	,  lowerCamelCase_ :				Optional[Any]=1						):
				'''simple docstring'''




				return (images[i : i + batch] for i in range(0	,  len(lowerCamelCase_						)	,  lowerCamelCase_						))







'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=13	,				_lowerCamelCase				:					Union[str, Any]=7	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					str=37	,				_lowerCamelCase				:					Optional[int]="gelu"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					Dict=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Union[str, Any]=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					List[Any]=0.02	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=4	,				_lowerCamelCase				:					int=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = 99
										__magic_name__	  = 32
										__magic_name__	  = 2
										__magic_name__	  = 4
										__magic_name__	  = 37
										__magic_name__	  = "gelu"
										__magic_name__	  = 0.1
										__magic_name__	  = 0.1
										__magic_name__	  = 5_12
										__magic_name__	  = 16
										__magic_name__	  = False
										__magic_name__	  = -1
										__magic_name__	  = True
										__magic_name__	  = 2
										__magic_name__	  = 0.02
										__magic_name__	  = 3
										__magic_name__	  = 4
										__magic_name__	  = None
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = None
										if self.use_input_mask:
														__magic_name__	  = random_attention_mask([self.batch_size, self.seq_length]  )

										__magic_name__	  = None
										if self.use_token_type_ids:
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size  )

										__magic_name__	  = None
										__magic_name__	  = None
										__magic_name__	  = None
										if self.use_labels:
														__magic_name__	  = ids_tensor([self.batch_size]	,				self.type_sequence_label_size  )
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels  )

										__magic_name__	  = DebertaConfig(
										    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				relative_attention=self.relative_attention	,				max_relative_positions=self.max_relative_positions	,				position_biased_input=self.position_biased_input	,				initializer_range=self.initializer_range	,				return_dict=_lowerCamelCase	,				)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]  )							->					Optional[int]:
										__magic_name__	  = TFDebertaModel(config=_lowerCamelCase  )
										__magic_name__	  = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__magic_name__	  = [input_ids, input_mask]
										__magic_name__	  = model(_lowerCamelCase  )

										__magic_name__	  = model(_lowerCamelCase  )

										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Dict:
										__magic_name__	  = TFDebertaForMaskedLM(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Tuple  )							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForSequenceClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForTokenClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = TFDebertaForQuestionAnswering(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)  )
										self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)  )

						def 					__A					( self				:					Dict  )							->					Dict:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       List[str]         =     False
						def 					__A					( self				:					Optional[Any]  )							->					str:
										__magic_name__	  = TFDebertaModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				hidden_size=37  )
						def 					__A					( self				:					int  )							->					Optional[Any]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										self.assertIsNotNone(_lowerCamelCase  )






@require_tf
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@unittest.skip(reason="Model not available yet"  )
						def 					__A					( self				:					str  )							->					str:
										pass

						@slow
						def 					__A					( self				:					str  )							->					Dict:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										__magic_name__	  = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
										__magic_name__	  = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase  )[0]

										__magic_name__	  = tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]  )
										tf.debugging.assert_near(output[:, 1:4, 1:4]	,				_lowerCamelCase	,				atol=1e-4  )



