


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch

class   A__							:




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[Any]=14 ,			_a    :					Optional[Any]=7 ,			_a    :					List[Any]=True ,			_a    :					Union[str, Any]=True ,			_a    :					int=False ,			_a    :					Any=True ,			_a    :					Union[str, Any]=99 ,			_a    :					Dict=32 ,			_a    :					Tuple=4 ,			_a    :					Optional[int]=4 ,			_a    :					Optional[int]=[[["global", "local"], 2]] ,			_a    :					List[str]=37 ,			_a    :					List[str]="gelu" ,			_a    :					List[str]=0.1 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Any=512 ,			_a    :					Optional[int]=7 ,			_a    :					str=0.02 ,			)							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =batch_size
										_SCREAMING_SNAKE_CASE	     =seq_length
										_SCREAMING_SNAKE_CASE	     =is_training
										_SCREAMING_SNAKE_CASE	     =use_input_mask
										_SCREAMING_SNAKE_CASE	     =use_token_type_ids
										_SCREAMING_SNAKE_CASE	     =use_labels
										_SCREAMING_SNAKE_CASE	     =vocab_size
										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_hidden_layers
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =attention_types
										_SCREAMING_SNAKE_CASE	     =intermediate_size
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =max_position_embeddings
										_SCREAMING_SNAKE_CASE	     =window_size
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =None
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1
										_SCREAMING_SNAKE_CASE	     =vocab_size - 1




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


										"""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	     =GPTNeoConfig(
										    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_layers=self.num_hidden_layers ,			num_heads=self.num_attention_heads ,			max_position_embeddings=self.max_position_embeddings ,			use_cache=_a ,			bos_token_id=self.bos_token_id ,			eos_token_id=self.eos_token_id ,			pad_token_id=self.pad_token_id ,			window_size=self.window_size ,			attention_types=self.attention_types ,			)

										return (config, input_ids, input_mask)




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


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =20
										_SCREAMING_SNAKE_CASE	     =model_class_name(_a     )

										_SCREAMING_SNAKE_CASE	     =model.init_cache(input_ids.shape[0] ,			_a     )
										_SCREAMING_SNAKE_CASE	     =jnp.ones((input_ids.shape[0], max_decoder_length) ,			dtype='''i4'''     )

										_SCREAMING_SNAKE_CASE	     =jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1     )[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1)     )
										_SCREAMING_SNAKE_CASE	     =model(
										    input_ids[:, :-1] ,			attention_mask=_a ,			past_key_values=_a ,			position_ids=_a ,			)

										_SCREAMING_SNAKE_CASE	     =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4'''     )
										_SCREAMING_SNAKE_CASE	     =model(
										    input_ids[:, -1:] ,			attention_mask=_a ,			past_key_values=outputs_cache.past_key_values ,			position_ids=_a ,			)

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										_SCREAMING_SNAKE_CASE	     =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
										self.parent.assertTrue(diff < 1E-3 ,			msg=f"Max diff is {diff}"     )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =20
										_SCREAMING_SNAKE_CASE	     =model_class_name(_a     )

										_SCREAMING_SNAKE_CASE	     =jnp.concatenate(
										    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )] ,			axis=-1 ,			)

										_SCREAMING_SNAKE_CASE	     =model.init_cache(input_ids.shape[0] ,			_a     )
										_SCREAMING_SNAKE_CASE	     =jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1     )[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1)     )

										_SCREAMING_SNAKE_CASE	     =model(
										    input_ids[:, :-1] ,			attention_mask=_a ,			past_key_values=_a ,			position_ids=_a ,			)
										_SCREAMING_SNAKE_CASE	     =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4'''     )
										_SCREAMING_SNAKE_CASE	     =model(
										    input_ids[:, -1:] ,			past_key_values=outputs_cache.past_key_values ,			attention_mask=_a ,			position_ids=_a ,			)

										_SCREAMING_SNAKE_CASE	     =model(_a ,			attention_mask=_a     )

										_SCREAMING_SNAKE_CASE	     =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
										self.parent.assertTrue(diff < 1E-3 ,			msg=f"Max diff is {diff}"     )

@require_flax
class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
						UpperCAmelCase									=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




						def 							__UpperCamelCase (				self    :					Dict     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =FlaxGPTNeoModelTester(self     )




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


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(_a ,			_a ,			_a ,			_a     )




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


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    _a ,			_a ,			_a ,			_a     )




						@slow
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =GPTaTokenizer.from_pretrained('''gpt2''' ,			pad_token='''<|endoftext|>''' ,			padding_side='''left'''     )
										_SCREAMING_SNAKE_CASE	     =tokenizer(['''Hello this is a long string''', '''Hey'''] ,			return_tensors='''np''' ,			padding=_a ,			truncation=_a     )

										_SCREAMING_SNAKE_CASE	     =FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''     )
										_SCREAMING_SNAKE_CASE	     =False
										_SCREAMING_SNAKE_CASE	     =model.config.eos_token_id

										_SCREAMING_SNAKE_CASE	     =jax.jit(model.generate     )

										_SCREAMING_SNAKE_CASE	     =jit_generate(
										    inputs['''input_ids'''] ,			attention_mask=inputs['''attention_mask'''] ,			pad_token_id=tokenizer.pad_token_id     ).sequences

										_SCREAMING_SNAKE_CASE	     =tokenizer.batch_decode(_a ,			skip_special_tokens=_a     )

										_SCREAMING_SNAKE_CASE	     =[
										    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
										    '''Hey, I\'m a little late to the party. I\'m going to''',
										]

										self.assertListEqual(_a ,			_a     )




						@is_pt_flax_cross_test
						def 							__UpperCamelCase (				self    :					str     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__     ):
																		# prepare inputs
																		_SCREAMING_SNAKE_CASE	     =self._prepare_for_class(_a ,			_a     )
																		_SCREAMING_SNAKE_CASE	     ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_SCREAMING_SNAKE_CASE	     =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_SCREAMING_SNAKE_CASE	     =getattr(_a ,			_a     )

																		_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =pt_inputs['''input_ids'''].shape
																		_SCREAMING_SNAKE_CASE	     =np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,)     )
																		for batch_idx, start_index in enumerate(_a     ):
																						_SCREAMING_SNAKE_CASE	     =0
																						_SCREAMING_SNAKE_CASE	     =1
																						_SCREAMING_SNAKE_CASE	     =0
																						_SCREAMING_SNAKE_CASE	     =1
																		_SCREAMING_SNAKE_CASE	     =pt_model_class(_a     ).eval()
																		_SCREAMING_SNAKE_CASE	     =model_class(_a ,			dtype=jnp.floataa     )

																		_SCREAMING_SNAKE_CASE	     =convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			_a     )
																		_SCREAMING_SNAKE_CASE	     =fx_state

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =pt_model(**_a     ).to_tuple()

																		_SCREAMING_SNAKE_CASE	     =fx_model(**_a     ).to_tuple()
																		self.assertEqual(len(_a     ) ,			len(_a     ) ,			'''Output lengths differ between Flax and PyTorch'''     )
																		for fx_output, pt_output in zip(_a ,			_a     ):
																						self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4E-2     )

																		with tempfile.TemporaryDirectory() as tmpdirname:
																						pt_model.save_pretrained(_a     )
																						_SCREAMING_SNAKE_CASE	     =model_class.from_pretrained(_a ,			from_pt=_a     )

																		_SCREAMING_SNAKE_CASE	     =fx_model_loaded(**_a     ).to_tuple()
																		self.assertEqual(
																		    len(_a     ) ,			len(_a     ) ,			'''Output lengths differ between Flax and PyTorch'''     )
																		for fx_output_loaded, pt_output in zip(_a ,			_a     ):
																						self.assert_almost_equals(fx_output_loaded[:, -1] ,			pt_output[:, -1].numpy() ,			4E-2     )




						@is_pt_flax_cross_test
						def 							__UpperCamelCase (				self    :					Tuple     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__     ):
																		# prepare inputs
																		_SCREAMING_SNAKE_CASE	     =self._prepare_for_class(_a ,			_a     )
																		_SCREAMING_SNAKE_CASE	     ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_SCREAMING_SNAKE_CASE	     =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_SCREAMING_SNAKE_CASE	     =getattr(_a ,			_a     )

																		_SCREAMING_SNAKE_CASE	     =pt_model_class(_a     ).eval()
																		_SCREAMING_SNAKE_CASE	     =model_class(_a ,			dtype=jnp.floataa     )

																		_SCREAMING_SNAKE_CASE	     =load_flax_weights_in_pytorch_model(_a ,			fx_model.params     )
																		_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =pt_inputs['''input_ids'''].shape
																		_SCREAMING_SNAKE_CASE	     =np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,)     )
																		for batch_idx, start_index in enumerate(_a     ):
																						_SCREAMING_SNAKE_CASE	     =0
																						_SCREAMING_SNAKE_CASE	     =1
																						_SCREAMING_SNAKE_CASE	     =0
																						_SCREAMING_SNAKE_CASE	     =1

																		# make sure weights are tied in PyTorch
																		pt_model.tie_weights()

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =pt_model(**_a     ).to_tuple()

																		_SCREAMING_SNAKE_CASE	     =fx_model(**_a     ).to_tuple()
																		self.assertEqual(len(_a     ) ,			len(_a     ) ,			'''Output lengths differ between Flax and PyTorch'''     )
																		for fx_output, pt_output in zip(_a ,			_a     ):
																						self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4E-2     )

																		with tempfile.TemporaryDirectory() as tmpdirname:
																						fx_model.save_pretrained(_a     )
																						_SCREAMING_SNAKE_CASE	     =pt_model_class.from_pretrained(_a ,			from_flax=_a     )

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE	     =pt_model_loaded(**_a     ).to_tuple()

																		self.assertEqual(
																		    len(_a     ) ,			len(_a     ) ,			'''Output lengths differ between Flax and PyTorch'''     )
																		for fx_output, pt_output in zip(_a ,			_a     ):
																						self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4E-2     )




						@slow
						def 							__UpperCamelCase (				self    :					Tuple     )							->   Union[str, Any]:


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''     )
														_SCREAMING_SNAKE_CASE	     =model(np.ones((1, 1)     )     )
														self.assertIsNotNone(_a     )



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     )