






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	,      a	,      a=13	,      a=7	,      a=True	,      a=True	,      a=True	,      a=True	,      a=99	,      a=32	,      a=2	,      a=4	,      a=37	,      a="gelu"	,      a=0.1	,      a=0.1	,      a=5_12	,      a=16	,      a=2	,      a=0.02	,      a=3	,      a=4	,      a=None	,      )	->   Optional[int]:
								snake_case_           =							parent
								snake_case_           =							13
								snake_case_           =							7
								snake_case_           =							True
								snake_case_           =							True
								snake_case_           =							True
								snake_case_           =							True
								snake_case_           =							99
								snake_case_           =							32
								snake_case_           =							2
								snake_case_           =							4
								snake_case_           =							37
								snake_case_           =							'gelu'
								snake_case_           =							0.1
								snake_case_           =							0.1
								snake_case_           =							5_12
								snake_case_           =							16
								snake_case_           =							False
								snake_case_           =							-1
								snake_case_           =							True
								snake_case_           =							2
								snake_case_           =							0.02
								snake_case_           =							3
								snake_case_           =							4
								snake_case_           =							None




	def 							_UpperCamelCase      (	self )	->   Dict:
								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_           =							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	,      a	,      a	,      a	,      a	,      a	,      a	,      a )	->   str:
								snake_case_           =							TFDebertaModel(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 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a	,      a	,      a )	->   Any:
								snake_case_           =							TFDebertaForMaskedLM(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 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a	,      a	,      a )	->   List[str]:
								snake_case_           =							self.num_labels
								snake_case_           =							TFDebertaForSequenceClassification(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 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a	,      a	,      a )	->   Dict:
								snake_case_           =							self.num_labels
								snake_case_           =							TFDebertaForTokenClassification(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 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a	,      a	,      a )	->   Any:
								snake_case_           =							TFDebertaForQuestionAnswering(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 							_UpperCamelCase      (	self )	->   int:
								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 			UpperCamelCase_				(      snake_case_	, snake_case_	, unittest.TestCase    ):



	'''simple docstring'''




	lowerCAmelCase													=			(
	    (
	        TFDebertaModel,
	        TFDebertaForMaskedLM,
	        TFDebertaForQuestionAnswering,
	        TFDebertaForSequenceClassification,
	        TFDebertaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase													=			(
	    {
	        '''feature-extraction''': TFDebertaModel,
	        '''fill-mask''': TFDebertaForMaskedLM,
	        '''question-answering''': TFDebertaForQuestionAnswering,
	        '''text-classification''': TFDebertaForSequenceClassification,
	        '''token-classification''': TFDebertaForTokenClassification,
	        '''zero-shot''': TFDebertaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase													=			False
	lowerCAmelCase													=			False




	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							TFDebertaModelTester(self )
								snake_case_           =							ConfigTester(self	,      config_class=a	,      hidden_size=37 )




	def 							_UpperCamelCase      (	self )	->   List[Any]:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a )




	def 							_UpperCamelCase      (	self )	->   List[str]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*a )




	def 							_UpperCamelCase      (	self )	->   Tuple:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a )




	def 							_UpperCamelCase      (	self )	->   str:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a )




	def 							_UpperCamelCase      (	self )	->   List[Any]:
								snake_case_           =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a )






	@slow
	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								snake_case_           =							TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
								self.assertIsNotNone(a )






@require_tf
class 			UpperCamelCase_				(      unittest.TestCase    ):



	'''simple docstring'''




	@unittest.skip(reason='Model not available yet' )
	def 							_UpperCamelCase      (	self )	->   Tuple:
								pass






	@slow
	def 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
								snake_case_           =							tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 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(
								    [
								        [
								            [-0.59_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]	,      a	,      atol=1E-4 )













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast





def 						__UpperCAmelCase      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





def 						__UpperCAmelCase      (					a_):
							snake_case_      ,		snake_case_           =							[], []
							for title, text in zip(documents['title']							,					documents['text']):
														if text is not None:
																					for passage in split_text(a_):
																												titles.append(title if title is not None else '')
																												texts.append(a_)
							return {"title": titles, "text": texts}





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_           =							load_dataset(
							    'csv'							,					data_files=[csv_path]							,					split='train'							,					delimiter='\t'							,					column_names=['title', 'text'])
							snake_case_           =							kb_dataset.map(
							    a_							,					batched=a_							,					num_proc=1)  # if you want you can load already splitted csv.
							snake_case_           =							[kb_dataset.shard(a_							,					a_							,					contiguous=a_) for i in range(a_)]
							snake_case_           =							kb_list[process_num]

							snake_case_           =							'data_' + str(a_)
							snake_case_           =							os.path.join(a_							,					a_)

							snake_case_           =							DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base')
							snake_case_           =							ctx_encoder.to(device=a_)

							def embed(
							    a_							,					a_							,					a_							,					a_) -> dict:
														snake_case_           =							ctx_tokenizer(
														    documents['title']							,					documents['text']							,					truncation=a_							,					padding='longest'							,					return_tensors='pt')['input_ids']
														snake_case_           =							ctx_encoder(input_ids.to(device=a_)							,					return_dict=a_).pooler_output
														return {"embeddings": embeddings.detach().cpu().numpy()}

							snake_case_           =							Features(
							    {'text': Value('string'), 'title': Value('string'), 'embeddings': Sequence(Value('float32'))})  # optional, save as float32 instead of float64 to save space

							snake_case_           =							data_shrad.map(
							    partial(a_							,					ctx_encoder=a_							,					ctx_tokenizer=a_							,					device=a_)							,					batched=a_							,					batch_size=16							,					features=a_							,					)
							dataset.save_to_disk(a_)





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

							for shard_address in glob(str(a_) + '/*/'):
														data_shard_list.append(load_from_disk(a_))

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk





