import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)



class  UpperCamelCase				( unittest.TestCase ):

					'''simple docstring'''

					def __init__(					self				,				A_				,				A_=13				,				A_=7				,				A_=True				,				A_=True				,				A_=True				,				A_=True				,				A_=99				,				A_=32				,				A_=5				,				A_=4				,				A_=37				,				A_="gelu"				,				A_=0.1				,				A_=0.1				,				A_=5_12				,				A_=16				,				A_=2				,				A_=0.02				,				A_=4				,				) ->				Tuple:






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



					def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






											"""simple docstring"""
											_lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

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

											_lowerCamelCase			    =				DistilBertConfig(
											    vocab_size=self.vocab_size				,				dim=self.hidden_size				,				n_layers=self.num_hidden_layers				,				n_heads=self.num_attention_heads				,				hidden_dim=self.intermediate_size				,				hidden_act=self.hidden_act				,				dropout=self.hidden_dropout_prob				,				attention_dropout=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				initializer_range=self.initializer_range				,				tie_weights_=UpperCamelCase__				,				)

											return config, input_ids, attention_mask





					def 		UpperCamelCase_						(					self						) ->				str:






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





@require_flax
class  UpperCamelCase				( UpperCamelCase_   ,  unittest.TestCase ):

					'''simple docstring'''

					A_										=  (
					    (
					        FlaxDistilBertModel,
					        FlaxDistilBertForMaskedLM,
					        FlaxDistilBertForMultipleChoice,
					        FlaxDistilBertForQuestionAnswering,
					        FlaxDistilBertForSequenceClassification,
					        FlaxDistilBertForTokenClassification,
					        FlaxDistilBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def 		UpperCamelCase_						(					self						) ->				Dict:






											"""simple docstring"""
											_lowerCamelCase			    =				FlaxDistilBertModelTester(self						)





					@slow
					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																	_lowerCamelCase			    =				model_class_name.from_pretrained('''distilbert-base-uncased'''						)
																	_lowerCamelCase			    =				model(np.ones((1, 1)						)						)
																	self.assertIsNotNone(UpperCamelCase__						)





@require_flax
class  UpperCamelCase				( unittest.TestCase ):

					'''simple docstring'''

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






											"""simple docstring"""
											_lowerCamelCase			    =				FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''						)
											_lowerCamelCase			    =				np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]						)
											_lowerCamelCase			    =				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											_lowerCamelCase			    =				model(UpperCamelCase__				,				attention_mask=UpperCamelCase__						)[0]
											_lowerCamelCase			    =				(1, 11, 7_68)
											self.assertEqual(output.shape				,				UpperCamelCase__						)
											_lowerCamelCase			    =				np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]						)

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]				,				UpperCamelCase__				,				atol=1E-4						)						)
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				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:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()