'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
		    TFDebertaVaForMaskedLM,
		    TFDebertaVaForQuestionAnswering,
		    TFDebertaVaForSequenceClassification,
		    TFDebertaVaForTokenClassification,
		    TFDebertaVaModel,
		)




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       str=13							,							UpperCamelCase_ :       Union[str, Any]=7							,							UpperCamelCase_ :       Optional[Any]=True							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       List[Any]=99							,							UpperCamelCase_ :       int=32							,							UpperCamelCase_ :       int=2							,							UpperCamelCase_ :       Any=4							,							UpperCamelCase_ :       Dict=37							,							UpperCamelCase_ :       Any="gelu"							,							UpperCamelCase_ :       Optional[int]=0.1							,							UpperCamelCase_ :       Optional[Any]=0.1							,							UpperCamelCase_ :       List[Any]=5_12							,							UpperCamelCase_ :       Any=16							,							UpperCamelCase_ :       Tuple=2							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       List[str]=False							,							UpperCamelCase_ :       str=True							,							UpperCamelCase_ :       Dict="None"							,							UpperCamelCase_ :       List[Any]=3							,							UpperCamelCase_ :       Any=4							,							UpperCamelCase_ :       Optional[Any]=None							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = parent
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = batch_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = seq_length
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = is_training
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_input_mask
													SCREAMING_SNAKE_CASE__     :Dict						    = use_token_type_ids
													SCREAMING_SNAKE_CASE__     :Any						    = use_labels
													SCREAMING_SNAKE_CASE__     :Any						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_size
													SCREAMING_SNAKE_CASE__     :List[Any]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Any						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Any						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :List[Any]						    = attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :Tuple						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :str						    = type_vocab_size
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_labels
													SCREAMING_SNAKE_CASE__     :Tuple						    = num_choices
													SCREAMING_SNAKE_CASE__     :str						    = relative_attention
													SCREAMING_SNAKE_CASE__     :Any						    = position_biased_input
													SCREAMING_SNAKE_CASE__     :int						    = pos_att_type
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = scope




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size      )

													SCREAMING_SNAKE_CASE__     :Dict						    = None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE__     :int						    = random_attention_mask([self.batch_size, self.seq_length]      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE__     :int						    = ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = None
													SCREAMING_SNAKE_CASE__     :str						    = None
													SCREAMING_SNAKE_CASE__     :List[Any]						    = None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = ids_tensor([self.batch_size]							,							self.type_sequence_label_size      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = DebertaVaConfig(
													    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							,							position_biased_input=self.position_biased_input							,							initializer_range=self.initializer_range							,							return_dict=UpperCamelCase_							,							)

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




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Optional[int]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = TFDebertaVaModel(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

													SCREAMING_SNAKE_CASE__     :str						    = [input_ids, input_mask]
													SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = model(UpperCamelCase_      )

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




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Optional[Any]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = TFDebertaVaForMaskedLM(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {
													    'input_ids': input_ids,
													    'attention_mask': input_mask,
													    'token_type_ids': token_type_ids,
													}
													SCREAMING_SNAKE_CASE__     :Dict						    = model(UpperCamelCase_      )
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)      )




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = self.num_labels
													SCREAMING_SNAKE_CASE__     :Tuple						    = TFDebertaVaForSequenceClassification(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = {
													    'input_ids': input_ids,
													    'attention_mask': input_mask,
													    'token_type_ids': token_type_ids,
													}

													SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_      )
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[int]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.num_labels
													SCREAMING_SNAKE_CASE__     :Tuple						    = TFDebertaVaForTokenClassification(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = {
													    'input_ids': input_ids,
													    'attention_mask': input_mask,
													    'token_type_ids': token_type_ids,
													}
													SCREAMING_SNAKE_CASE__     :Tuple						    = model(UpperCamelCase_      )
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)      )




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Optional[int]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       str      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = TFDebertaVaForQuestionAnswering(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = {
													    'input_ids': input_ids,
													    'attention_mask': input_mask,
													    'token_type_ids': token_type_ids,
													}

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = model(UpperCamelCase_      )
													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      __lowerCamelCase       (    self :       Optional[int]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :int						    = 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__
													)    ,      
													)     :str						    = config_and_inputs
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
													return config, inputs_dict




@require_tf
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= (
						    (
						        TFDebertaVaModel,
						        TFDebertaVaForMaskedLM,
						        TFDebertaVaForQuestionAnswering,
						        TFDebertaVaForSequenceClassification,
						        TFDebertaVaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						A_			:							Dict     		= (
						    {
						        'feature-extraction': TFDebertaVaModel,
						        'fill-mask': TFDebertaVaForMaskedLM,
						        'question-answering': TFDebertaVaForQuestionAnswering,
						        'text-classification': TFDebertaVaForSequenceClassification,
						        'token-classification': TFDebertaVaForTokenClassification,
						        'zero-shot': TFDebertaVaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						A_			:							Optional[int]     		= False
						A_			:							int     		= False




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Dict						    = TFDebertaVaModelTester(self      )
													SCREAMING_SNAKE_CASE__     :Any						    = ConfigTester(self							,							config_class=UpperCamelCase_							,							hidden_size=37      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				str:
													self.config_tester.run_common_tests()




						def      __lowerCamelCase       (    self :       str      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
													self.assertIsNotNone(UpperCamelCase_      )




@require_tf
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@unittest.skip(reason='Model not available yet'      )
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				int:
													pass




						@slow
						def      __lowerCamelCase       (    self :       List[str]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :str						    = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
													SCREAMING_SNAKE_CASE__     :str						    = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
													SCREAMING_SNAKE_CASE__     :str						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      )[0]

													SCREAMING_SNAKE_CASE__     :List[str]						    = tf.constant(
													    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
													tf.debugging.assert_near(output[:, 1:4, 1:4]							,							UpperCamelCase_							,							atol=1e-4      )







'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'gpt_neox'




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int=5_04_32							,							UpperCamelCase_ :       Dict=61_44							,							UpperCamelCase_ :       int=44							,							UpperCamelCase_ :       Any=64							,							UpperCamelCase_ :       Optional[int]=2_45_76							,							UpperCamelCase_ :       str="gelu"							,							UpperCamelCase_ :       Tuple=0.25							,							UpperCamelCase_ :       Tuple=1_00_00							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Optional[int]=0.0							,							UpperCamelCase_ :       str=0.1							,							UpperCamelCase_ :       List[Any]=20_48							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       Optional[Any]=0							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       Tuple=None							,							**UpperCamelCase_ :       Any							,							)     ->				int:
													super().__init__(bos_token_id=UpperCamelCase_							,							eos_token_id=UpperCamelCase_							,							**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Dict						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rotary_pct
													SCREAMING_SNAKE_CASE__     :Any						    = rotary_emb_base
													SCREAMING_SNAKE_CASE__     :Dict						    = attention_dropout
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_dropout
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = classifier_dropout
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_cache
													SCREAMING_SNAKE_CASE__     :Any						    = tie_word_embeddings
													SCREAMING_SNAKE_CASE__     :List[str]						    = use_parallel_residual
													SCREAMING_SNAKE_CASE__     :int						    = rope_scaling
													self._rope_scaling_validation()

													if self.hidden_size % self.num_attention_heads != 0:
																				raise ValueError(
																				    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Union[str, Any]:
													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling							,							UpperCamelCase_      ) or len(self.rope_scaling      ) != 2:
																				raise ValueError(
																				    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
																				    f'''got {self.rope_scaling}'''      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.rope_scaling.get('type'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.rope_scaling.get('factor'							,							UpperCamelCase_      )
													if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																				raise ValueError(
																				    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
													if rope_scaling_factor is None or not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) or rope_scaling_factor <= 1.0:
																				raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )






