


import json
import logging
import os
import subprocess
from argparse import ArgumentParser


snake_case_  :	Optional[int]							  =							logging.getLogger(__name__)







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =ArgumentParser()
				_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =parser.parse_known_args()
				for arg in unknown:
								if arg.startswith(('''-''', '''--''')):
												parser.add_argument(arg.split('''=''')[0])

				return parser.parse_args()







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =parse_args()
				_SCREAMING_SNAKE_CASE	     =8888
				_SCREAMING_SNAKE_CASE	     =int(os.environ['''SM_NUM_GPUS'''])
				_SCREAMING_SNAKE_CASE	     =json.loads(os.environ['''SM_HOSTS'''])
				_SCREAMING_SNAKE_CASE	     =len(a__)
				_SCREAMING_SNAKE_CASE	     =os.environ['''SM_CURRENT_HOST''']
				_SCREAMING_SNAKE_CASE	     =hosts.index(a__)
				_SCREAMING_SNAKE_CASE	     ='''INFO'''

				if num_nodes > 1:
								_SCREAMING_SNAKE_CASE	     =f"python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {''.join(['nested fstring' for parameter,value in args.__dict__.items()])}"
				else:
								_SCREAMING_SNAKE_CASE	     =f"python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {''.join(['nested fstring' for parameter,value in args.__dict__.items()])}"
				try:
								subprocess.run(a__		,shell=a__)
				except Exception as e:
								logger.info(a__)


if __name__ == "__main__":
							main()



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   A__							:




						def __init__(				self    :					Dict ,			_a    :					List[Any] ,			_a    :					int=13 ,			_a    :					List[str]=7 ,			_a    :					Optional[Any]=True ,			_a    :					Any=True ,			_a    :					int=True ,			_a    :					Dict=True ,			_a    :					Union[str, Any]=99 ,			_a    :					str=32 ,			_a    :					Dict=2 ,			_a    :					Dict=4 ,			_a    :					str=37 ,			_a    :					Dict="gelu" ,			_a    :					Any=0.1 ,			_a    :					int=0.1 ,			_a    :					List[str]=512 ,			_a    :					Optional[int]=16 ,			_a    :					Tuple=2 ,			_a    :					List[str]=0.02 ,			_a    :					Optional[int]=3 ,			_a    :					List[Any]=4 ,			_a    :					Union[str, Any]=None ,			)							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =13
										_SCREAMING_SNAKE_CASE	     =7
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =99
										_SCREAMING_SNAKE_CASE	     =32
										_SCREAMING_SNAKE_CASE	     =2
										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =37
										_SCREAMING_SNAKE_CASE	     ='''gelu'''
										_SCREAMING_SNAKE_CASE	     =0.1
										_SCREAMING_SNAKE_CASE	     =0.1
										_SCREAMING_SNAKE_CASE	     =512
										_SCREAMING_SNAKE_CASE	     =16
										_SCREAMING_SNAKE_CASE	     =False
										_SCREAMING_SNAKE_CASE	     =-1
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =2
										_SCREAMING_SNAKE_CASE	     =0.02
										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =None




						def 							__UpperCamelCase (				self    :					Tuple     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size     )

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

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

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

										_SCREAMING_SNAKE_CASE	     =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=_a ,			)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Optional[Any] ,			_a    :					Dict ,			_a    :					List[str] ,			_a    :					Dict ,			_a    :					List[Any] ,			_a    :					Dict ,			_a    :					Tuple     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

										_SCREAMING_SNAKE_CASE	     =[input_ids, input_mask]
										_SCREAMING_SNAKE_CASE	     =model(_a     )

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)     )




						def 							__UpperCamelCase (				self    :					int ,			_a    :					List[Any] ,			_a    :					Tuple ,			_a    :					List[str] ,			_a    :					Optional[int] ,			_a    :					Union[str, Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaForMaskedLM(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					List[str] ,			_a    :					List[Any] ,			_a    :					Optional[int] ,			_a    :					List[Any] ,			_a    :					Optional[Any] ,			_a    :					Dict ,			_a    :					Optional[int]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.num_labels
										_SCREAMING_SNAKE_CASE	     =TFDebertaForSequenceClassification(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Any ,			_a    :					Optional[int] ,			_a    :					Optional[Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int] ,			_a    :					Optional[int] ,			_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.num_labels
										_SCREAMING_SNAKE_CASE	     =TFDebertaForTokenClassification(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Dict ,			_a    :					str ,			_a    :					List[Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int] ,			_a    :					Optional[int] ,			_a    :					List[str]     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaForQuestionAnswering(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_SCREAMING_SNAKE_CASE	     =model(_a     )
										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 							__UpperCamelCase (				self    :					str     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()
										(
										    (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , 
										)	     =config_and_inputs
										_SCREAMING_SNAKE_CASE	     ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict

@require_tf
class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase									=  (
						    {
						        "feature-extraction": TFDebertaModel,
						        "fill-mask": TFDebertaForMaskedLM,
						        "question-answering": TFDebertaForQuestionAnswering,
						        "text-classification": TFDebertaForSequenceClassification,
						        "token-classification": TFDebertaForTokenClassification,
						        "zero-shot": TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase									=  False
						UpperCAmelCase									=  False




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModelTester(self     )
										_SCREAMING_SNAKE_CASE	     =ConfigTester(self ,			config_class=_a ,			hidden_size=37     )




						def 							__UpperCamelCase (				self    :					List[str]     )							->   Any:


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a     )




						def 							__UpperCamelCase (				self    :					Any     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_a     )




						def 							__UpperCamelCase (				self    :					List[Any]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_a     )




						def 							__UpperCamelCase (				self    :					Dict     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_a     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_a     )




						@slow
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
										self.assertIsNotNone(_a     )

@require_tf
class   A__							(						unittest.TestCase     ):




						@unittest.skip(reason='''Model not available yet'''     )
						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										pass




						@slow
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
										_SCREAMING_SNAKE_CASE	     =tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     )
										_SCREAMING_SNAKE_CASE	     =tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
										_SCREAMING_SNAKE_CASE	     =model(_a ,			attention_mask=_a     )[0]

										_SCREAMING_SNAKE_CASE	     =tf.constant(
										    [
										        [
										            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
										            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
										            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
										        ]
										    ]     )
										tf.debugging.assert_near(output[:, 1:4, 1:4] ,			_a ,			atol=1E-4     )