




'''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						:    str      ,		a_						:    int      ,		a_						:    Tuple=13      ,		a_						:    str=7      ,		a_						:    Tuple=True      ,		a_						:    Optional[Any]=True      ,		a_						:    Union[str, Any]=True      ,		a_						:    List[str]=True      ,		a_						:    List[Any]=99      ,		a_						:    Optional[int]=32      ,		a_						:    int=2      ,		a_						:    int=4      ,		a_						:    str=37      ,		a_						:    Dict="gelu"      ,		a_						:    List[str]=0.1      ,		a_						:    Any=0.1      ,		a_						:    Any=512      ,		a_						:    Any=16      ,		a_						:    List[Any]=2      ,		a_						:    List[str]=0.02      ,		a_						:    Optional[Any]=False      ,		a_						:    List[Any]=True      ,		a_						:    Optional[Any]="None"      ,		a_						:    int=3      ,		a_						:    int=4      ,		a_						:    List[Any]=None      ,		):



						"""simple docstring"""




						__snake_case			=		parent
						__snake_case			=		batch_size
						__snake_case			=		seq_length
						__snake_case			=		is_training
						__snake_case			=		use_input_mask
						__snake_case			=		use_token_type_ids
						__snake_case			=		use_labels
						__snake_case			=		vocab_size
						__snake_case			=		hidden_size
						__snake_case			=		num_hidden_layers
						__snake_case			=		num_attention_heads
						__snake_case			=		intermediate_size
						__snake_case			=		hidden_act
						__snake_case			=		hidden_dropout_prob
						__snake_case			=		attention_probs_dropout_prob
						__snake_case			=		max_position_embeddings
						__snake_case			=		type_vocab_size
						__snake_case			=		type_sequence_label_size
						__snake_case			=		initializer_range
						__snake_case			=		num_labels
						__snake_case			=		num_choices
						__snake_case			=		relative_attention
						__snake_case			=		position_biased_input
						__snake_case			=		pos_att_type
						__snake_case			=		scope





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




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

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

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

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

						__snake_case			=		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=a_      ,		)

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





			def 							A (      self						:    Any      ,		a_						:    Optional[Any]      ,		a_						:    str      ,		a_						:    List[str]      ,		a_						:    str      ,		a_						:    Union[str, Any]      ,		a_						:    Tuple      ,		a_						:    Dict							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaModel(config=a_							)
						__snake_case			=		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

						__snake_case			=		[input_ids, input_mask]
						__snake_case			=		model(a_							)

						__snake_case			=		model(a_							)

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





			def 							A (      self						:    Dict      ,		a_						:    Any      ,		a_						:    Any      ,		a_						:    Union[str, Any]      ,		a_						:    str      ,		a_						:    Optional[Any]      ,		a_						:    Union[str, Any]      ,		a_						:    Dict							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaForMaskedLM(config=a_							)
						__snake_case			=		{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						__snake_case			=		model(a_							)
						self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)							)





			def 							A (      self						:    Dict      ,		a_						:    List[str]      ,		a_						:    List[str]      ,		a_						:    List[str]      ,		a_						:    List[str]      ,		a_						:    Union[str, Any]      ,		a_						:    Optional[int]      ,		a_						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		self.num_labels
						__snake_case			=		TFDebertaVaForSequenceClassification(config=a_							)
						__snake_case			=		{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						__snake_case			=		model(a_							)
						self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)							)





			def 							A (      self						:    Union[str, Any]      ,		a_						:    Tuple      ,		a_						:    int      ,		a_						:    Tuple      ,		a_						:    Union[str, Any]      ,		a_						:    List[str]      ,		a_						:    Union[str, Any]      ,		a_						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		self.num_labels
						__snake_case			=		TFDebertaVaForTokenClassification(config=a_							)
						__snake_case			=		{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						__snake_case			=		model(a_							)
						self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)							)





			def 							A (      self						:    Any      ,		a_						:    Optional[int]      ,		a_						:    Optional[Any]      ,		a_						:    Any      ,		a_						:    Any      ,		a_						:    Dict      ,		a_						:    int      ,		a_						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaForQuestionAnswering(config=a_							)
						__snake_case			=		{
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						__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 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




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







@require_tf
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			__SCREAMING_SNAKE_CASE    =			(
			    {
			        """feature-extraction""": TFDebertaVaModel,
			        """fill-mask""": TFDebertaVaForMaskedLM,
			        """question-answering""": TFDebertaVaForQuestionAnswering,
			        """text-classification""": TFDebertaVaForSequenceClassification,
			        """token-classification""": TFDebertaVaForTokenClassification,
			        """zero-shot""": TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			__SCREAMING_SNAKE_CASE    =			False
			__SCREAMING_SNAKE_CASE    =			False





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaModelTester(self							)
						__snake_case			=		ConfigTester(self      ,		config_class=a_      ,		hidden_size=37							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						self.config_tester.run_common_tests()





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_							)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*a_							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*a_							)





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*a_							)





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a_							)






			@slow
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
						self.assertIsNotNone(a_							)







@require_tf
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			@unittest.skip(reason="Model not available yet"							)
			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						pass






			@slow
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"							)
						__snake_case			=		tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]							)
						__snake_case			=		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
						__snake_case			=		model(a_      ,		attention_mask=a_							)[0]

						__snake_case			=		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]      ,		a_      ,		atol=1e-4							)











'''simple docstring'''





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


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		{
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""rwkv"""
     SCREAMING_SNAKE_CASE_				:       Any       							=		{"""max_position_embeddings""": """context_length"""}






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=50_277   ,_SCREAMING_SNAKE_CASE=1_024   ,_SCREAMING_SNAKE_CASE=4_096   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=6   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[str]:
          _snake_case				= vocab_size
          _snake_case				= context_length
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= attention_hidden_size if attention_hidden_size is not None else hidden_size
          _snake_case				= intermediate_size if intermediate_size is not None else 4 * hidden_size
          _snake_case				= layer_norm_epsilon
          _snake_case				= rescale_every
          _snake_case				= use_cache

          _snake_case				= bos_token_id
          _snake_case				= eos_token_id

          super().__init__(
              tie_word_embeddings=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )


