





'''simple docstring'''


import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.bert.modeling_flax_bert import (
		    FlaxBertForMaskedLM,
		    FlaxBertForMultipleChoice,
		    FlaxBertForNextSentencePrediction,
		    FlaxBertForPreTraining,
		    FlaxBertForQuestionAnswering,
		    FlaxBertForSequenceClassification,
		    FlaxBertForTokenClassification,
		    FlaxBertModel,
		)






class       UpperCamelCase__  (		unittest.TestCase     ):







	def __init__(				self			:   Optional[Any] , lowerCamelCase			:   str , lowerCamelCase			:   Dict=1_3 , lowerCamelCase			:   Union[str, Any]=7 , lowerCamelCase			:   Any=True , lowerCamelCase			:   Tuple=True , lowerCamelCase			:   int=True , lowerCamelCase			:   Any=True , lowerCamelCase			:   str=9_9 , lowerCamelCase			:   str=3_2 , lowerCamelCase			:   int=5 , lowerCamelCase			:   int=4 , lowerCamelCase			:   Union[str, Any]=3_7 , lowerCamelCase			:   str="gelu" , lowerCamelCase			:   int=0.1 , lowerCamelCase			:   Any=0.1 , lowerCamelCase			:   List[str]=5_1_2 , lowerCamelCase			:   Union[str, Any]=1_6 , lowerCamelCase			:   str=2 , lowerCamelCase			:   Any=0.02 , lowerCamelCase			:   Any=4 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_attention_mask
				a__     = use_token_type_ids
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size
				a__     = hidden_act
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = type_vocab_size
				a__     = type_sequence_label_size
				a__     = initializer_range
				a__     = num_choices







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)

				a__     = None
				if self.use_attention_mask:
							a__     = random_attention_mask([self.batch_size, self.seq_length]		)

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

				a__     = BertConfig(
				    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 , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , )

				return config, input_ids, token_type_ids, attention_mask







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__   ,				a__   ,				a__   ,				a__     = config_and_inputs
				a__     = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__   ,				a__   ,				a__   ,				a__     = config_and_inputs

				a__     = True
				a__     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				a__     = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2		)

				return (
				    config,
				    input_ids,
				    attention_mask,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)






@require_flax
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			List[Any]								=						True

	lowerCAmelCase__					:			Tuple								=						(
	    (
	        FlaxBertModel,
	        FlaxBertForPreTraining,
	        FlaxBertForMaskedLM,
	        FlaxBertForMultipleChoice,
	        FlaxBertForQuestionAnswering,
	        FlaxBertForNextSentencePrediction,
	        FlaxBertForSequenceClassification,
	        FlaxBertForTokenClassification,
	        FlaxBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = FlaxBertModelTester(self		)







	@slow
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				# Only check this for base model, not necessary for all model classes.
				# This will also help speed-up tests.
				a__     = FlaxBertModel.from_pretrained("bert-base-cased"		)
				a__     = model(np.ones((1, 1)		)		)
				self.assertIsNotNone(lowerCamelCase		)










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = parser.parse_args()

			with open(args.src_path					,  "r"     ) as src_file, open(args.evaluation_set					,  "w"     ) as eval_file, open(
			    args.gold_data_path					,  "w"     ) as gold_file:
						a__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



