


'''simple docstring'''
import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
						from comet_ml import OfflineExperiment

if is_tensorboard_available():
						import struct

						import tensorboard.compat.proto.event_pba as event_pba

__magic_name__   :   List[str]  							=logging.getLogger(__name__)






@require_tensorboard
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def 					__A					( self				:					List[Any]  )							->					Tuple:
										__magic_name__	  = "test_project_with_config"
										with tempfile.TemporaryDirectory() as dirpath:
														__magic_name__	  = Accelerator(log_with="tensorboard"	,				project_dir=_lowerCamelCase  )
														__magic_name__	  = {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers(_lowerCamelCase	,				_lowerCamelCase  )
														accelerator.end_training()
														for child in Path(f'{dirpath}/{project_name}'  ).glob("*/**"  ):
																		__magic_name__	  = list(filter(lambda _lowerCamelCase  : x.is_file()	,				child.iterdir()  )  )[0]
														self.assertNotEqual(str(_lowerCamelCase  )	,				""  )
						def 					__A					( self				:					Optional[Any]  )							->					Optional[int]:
										__magic_name__	  = "test_project_with_log"
										with tempfile.TemporaryDirectory() as dirpath:
														__magic_name__	  = Accelerator(log_with="tensorboard"	,				project_dir=_lowerCamelCase  )
														accelerator.init_trackers(_lowerCamelCase  )
														__magic_name__	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(_lowerCamelCase	,				step=0  )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														__magic_name__	  = list(filter(lambda _lowerCamelCase  : x.is_file()	,				Path(f'{dirpath}/{project_name}'  ).iterdir()  )  )[0]
														self.assertNotEqual(str(_lowerCamelCase  )	,				""  )
						def 					__A					( self				:					Any  )							->					List[str]:
										__magic_name__	  = "test_project_with_log"
										with tempfile.TemporaryDirectory() as dirpath:
														__magic_name__	  = Accelerator(log_with="tensorboard"	,				project_dir=_lowerCamelCase  )
														accelerator.init_trackers(_lowerCamelCase  )
														__magic_name__	  = {"tensor": torch.tensor(1  )}
														accelerator.log(_lowerCamelCase	,				step=0  )
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														__magic_name__	  = list(filter(lambda _lowerCamelCase  : x.is_file()	,				Path(f'{dirpath}/{project_name}'  ).iterdir()  )  )[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(_lowerCamelCase	,				"rb"  ) as f:
																		__magic_name__	  = f.read()
														__magic_name__	  = False
														while data:
																		__magic_name__	  = struct.unpack("Q"	,				data[:8]  )

																		__magic_name__	  = data[12 : 12 + int(header[0]  )]  # 8+4
																		__magic_name__	  = data[12 + int(header[0]  ) + 4 :]
																		__magic_name__	  = event_pba.Event()

																		event.ParseFromString(_lowerCamelCase  )
																		if event.HasField("summary"  ):
																						for value in event.summary.value:
																										if value.simple_value == 1.0 and value.tag == "tensor":
																														__magic_name__	  = True
														self.assertTrue(_lowerCamelCase	,				"Converted tensor was not found in the log file!"  )
						def 					__A					( self				:					List[str]  )							->					Tuple:
										with self.assertRaisesRegex(_lowerCamelCase	,				"Logging with `tensorboard` requires a `logging_dir`"  ):
														__magic_name__	  = Accelerator(log_with="tensorboard"  )
										with tempfile.TemporaryDirectory() as dirpath:
														__magic_name__	  = Accelerator(log_with="tensorboard"	,				project_dir=_lowerCamelCase  )

						def 					__A					( self				:					List[str]  )							->					int:
										__magic_name__	  = ProjectConfiguration(total_limit=30  )
										with tempfile.TemporaryDirectory() as dirpath:
														__magic_name__	  = Accelerator(log_with="tensorboard"	,				project_dir=_lowerCamelCase	,				project_config=_lowerCamelCase  )






@require_wandb
@mock.patch.dict(os.environ   ,		{'''WANDB_MODE''': '''offline'''}   )
class 		UpperCamelCase_		(					A   ,		A   ):





						"""simple docstring"""




						def 					__A					( self				:					Optional[int]  )							->					List[Any]:
										super().setUp()
										# wandb let's us override where logs are stored to via the WANDB_DIR env var
										self.add_mocks(mock.patch.dict(os.environ	,				{"WANDB_DIR": self.tmpdir}  )  )
						@staticmethod
						def 					__A					( _lowerCamelCase				:					str	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					bool = True  )							->					Any:
										__magic_name__	  = rf'{section} ([\S\s]*?)\n\n'
										if record:
														__magic_name__	  = rf'Record: {pattern}'
										__magic_name__	  = re.findall(_lowerCamelCase	,				_lowerCamelCase  )[0]
										# A config
										if section == "config" or section == "history":
														__magic_name__	  = re.findall(r"\"([a-zA-Z0-9_.,]+)"	,				_lowerCamelCase  )
														return {key: val for key, val in zip(cleaned_record[0::2]	,				cleaned_record[1::2]  )}
										# Everything else
										else:
														return dict(re.findall(r"(\w+): \"([^\s]+)\""	,				_lowerCamelCase  )  )

						@skip
						def 					__A					( self				:					str  )							->					Optional[Any]:
										__magic_name__	  = "test_project_with_config"
										__magic_name__	  = Accelerator(log_with="wandb"  )
										__magic_name__	  = {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
										__magic_name__	  = {"wandb": {"tags": ["my_tag"]}}
										accelerator.init_trackers(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )
										__magic_name__	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
										accelerator.log(_lowerCamelCase	,				step=0  )
										accelerator.end_training()
										# The latest offline log is stored at wandb/latest-run/*.wandb
										for child in Path(f'{self.tmpdir}/wandb/latest-run'  ).glob("*"  ):
														if child.is_file() and child.suffix == ".wandb":
																		__magic_name__	  = subprocess.check_output(
																		    ["wandb", "sync", "--view", "--verbose", str(_lowerCamelCase  )]	,				env=os.environ.copy()  ).decode("utf8"	,				"ignore"  )
																		break

        # Check HPS through careful parsing and cleaning
										__magic_name__	  = self.parse_log(_lowerCamelCase	,				"config"  )
										self.assertEqual(logged_items["num_iterations"]	,				"12"  )
										self.assertEqual(logged_items["learning_rate"]	,				"0.01"  )
										self.assertEqual(logged_items["some_boolean"]	,				"false"  )
										self.assertEqual(logged_items["some_string"]	,				"some_value"  )
										self.assertEqual(logged_items["some_string"]	,				"some_value"  )

										# Run tags
										__magic_name__	  = self.parse_log(_lowerCamelCase	,				"run"	,				_lowerCamelCase  )
										self.assertEqual(logged_items["tags"]	,				"my_tag"  )

										# Actual logging
										__magic_name__	  = self.parse_log(_lowerCamelCase	,				"history"  )
										self.assertEqual(logged_items["total_loss"]	,				"0.1"  )
										self.assertEqual(logged_items["iteration"]	,				"1"  )
										self.assertEqual(logged_items["my_text"]	,				"some_value"  )
										self.assertEqual(logged_items["_step"]	,				"0"  )





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




				__magic_name__	  = run_name
				__magic_name__	  = OfflineExperiment(project_name=lowerCamelCase_	,  offline_directory=lowerCamelCase_						)
				logger.info(F'Initialized offline CometML project {self.run_name}'						)
				logger.info("Make sure to log any initial configurations with `self.store_init_configuration` before training!"						)






@require_comet_ml
@mock.patch.object(A   ,		'''__init__'''   ,		A   )
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@staticmethod
						def 					__A					( _lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					bool = False  )							->					List[Any]:
										for log in log_list:
														__magic_name__	  = json.loads(_lowerCamelCase  )["payload"]
														if is_param and "param" in j.keys():
																		if j["param"]["paramName"] == key:
																						return j["param"]["paramValue"]
														if "log_other" in j.keys():
																		if j["log_other"]["key"] == key:
																						return j["log_other"]["val"]
														if "metric" in j.keys():
																		if j["metric"]["metricName"] == key:
																						return j["metric"]["metricValue"]
						def 					__A					( self				:					str  )							->					Tuple:
										with tempfile.TemporaryDirectory() as d:
														__magic_name__	  = CometMLTracker("test_project_with_config"	,				_lowerCamelCase  )
														__magic_name__	  = Accelerator(log_with=_lowerCamelCase  )
														__magic_name__	  = {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers(_lowerCamelCase	,				_lowerCamelCase  )
														accelerator.end_training()
														__magic_name__	  = os.listdir(_lowerCamelCase  )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														__magic_name__	  = os.path.join(_lowerCamelCase	,				_lowerCamelCase  )
														__magic_name__	  = zipfile.ZipFile(_lowerCamelCase	,				"r"  )
														__magic_name__	  = archive.open("messages.json"  ).read().decode("utf-8"  )
										__magic_name__	  = log.split("\n"  )[:-1]
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"num_iterations"	,				_lowerCamelCase  )	,				12  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"learning_rate"	,				_lowerCamelCase  )	,				0.01  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"some_boolean"	,				_lowerCamelCase  )	,				_lowerCamelCase  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"some_string"	,				_lowerCamelCase  )	,				"some_value"  )

						def 					__A					( self				:					Dict  )							->					List[Any]:
										with tempfile.TemporaryDirectory() as d:
														__magic_name__	  = CometMLTracker("test_project_with_config"	,				_lowerCamelCase  )
														__magic_name__	  = Accelerator(log_with=_lowerCamelCase  )
														accelerator.init_trackers(_lowerCamelCase  )
														__magic_name__	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(_lowerCamelCase	,				step=0  )
														accelerator.end_training()
														__magic_name__	  = os.listdir(_lowerCamelCase  )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														__magic_name__	  = os.path.join(_lowerCamelCase	,				_lowerCamelCase  )
														__magic_name__	  = zipfile.ZipFile(_lowerCamelCase	,				"r"  )
														__magic_name__	  = archive.open("messages.json"  ).read().decode("utf-8"  )
										__magic_name__	  = log.split("\n"  )[:-1]
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"curr_step"	,				_lowerCamelCase  )	,				0  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"total_loss"  )	,				0.1  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"iteration"  )	,				1  )
										self.assertEqual(self.get_value_from_key(_lowerCamelCase	,				"my_text"  )	,				"some_value"  )






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[Any]         =     [
						    '''total_loss''',
						    '''iteration''',
						    '''my_text''',
						    '''learning_rate''',
						    '''num_iterations''',
						    '''some_boolean''',
						    '''some_string''',
						]

						UpperCAmelCase__	:       List[str]         =     '''my_custom_tracker'''
						UpperCAmelCase__	:       Dict         =     False
						def __init__( self				:					Tuple	,				_lowerCamelCase				:					str  )							->					List[str]:
										__magic_name__	  = open(f'{dir}/log.csv'	,				"w+"  )
										__magic_name__	  = csv.DictWriter(self.f	,				fieldnames=self._col_names  )
										self.writer.writeheader()
						@property
						def 					__A					( self				:					int  )							->					Any:
										return self.writer
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					dict  )							->					List[Any]:
										logger.info("Call init"  )
										self.writer.writerow(_lowerCamelCase  )
						def 					__A					( self				:					Dict	,				_lowerCamelCase				:					dict	,				_lowerCamelCase				:					Optional[int]  )							->					int:
										logger.info("Call log"  )
										self.writer.writerow(_lowerCamelCase  )

						def 					__A					( self				:					str  )							->					List[str]:
										self.f.close()






class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def 					__A					( self				:					str  )							->					Tuple:
										with tempfile.TemporaryDirectory() as d:
														__magic_name__	  = MyCustomTracker(_lowerCamelCase  )
														__magic_name__	  = Accelerator(log_with=_lowerCamelCase  )
														__magic_name__	  = {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers("Some name"	,				_lowerCamelCase  )
														accelerator.end_training()
														with open(f'{d}/log.csv'	,				"r"  ) as f:
																		__magic_name__	  = csv.DictReader(_lowerCamelCase  )
																		__magic_name__	  = next(_lowerCamelCase  )
																		__magic_name__	  = {
																		    "total_loss": "",
																		    "iteration": "",
																		    "my_text": "",
																		    "learning_rate": "0.01",
																		    "num_iterations": "12",
																		    "some_boolean": "False",
																		    "some_string": "some_value",
																		}
																		self.assertDictEqual(_lowerCamelCase	,				_lowerCamelCase  )

						def 					__A					( self				:					List[Any]  )							->					str:
										with tempfile.TemporaryDirectory() as d:
														__magic_name__	  = MyCustomTracker(_lowerCamelCase  )
														__magic_name__	  = Accelerator(log_with=_lowerCamelCase  )
														accelerator.init_trackers("Some name"  )
														__magic_name__	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(_lowerCamelCase	,				step=0  )
														accelerator.end_training()
														with open(f'{d}/log.csv'	,				"r"  ) as f:
																		__magic_name__	  = csv.DictReader(_lowerCamelCase  )
																		__magic_name__	  = next(_lowerCamelCase  )
																		__magic_name__	  = {
																		    "total_loss": "0.1",
																		    "iteration": "1",
																		    "my_text": "some_value",
																		    "learning_rate": "",
																		    "num_iterations": "",
																		    "some_boolean": "",
																		    "some_string": "",
																		}
																		self.assertDictEqual(_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  )



