



"""simple docstring"""







import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
     from transformers.models.dpr.modeling_dpr import (
         DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )







class  UpperCamelCase__  :




      """simple docstring"""




      def __init__( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=1_3       ,     SCREAMING_SNAKE_CASE_							: str=7       ,     SCREAMING_SNAKE_CASE_							: Optional[int]=False       ,     SCREAMING_SNAKE_CASE_							: str=True       ,     SCREAMING_SNAKE_CASE_							: str=True       ,     SCREAMING_SNAKE_CASE_							: str=True       ,     SCREAMING_SNAKE_CASE_							: List[str]=9_9       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=3_2       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]=5       ,     SCREAMING_SNAKE_CASE_							: Dict=4       ,     SCREAMING_SNAKE_CASE_							: int=3_7       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]="gelu"       ,     SCREAMING_SNAKE_CASE_							: str=0.1       ,     SCREAMING_SNAKE_CASE_							: Dict=0.1       ,     SCREAMING_SNAKE_CASE_							: Any=5_1_2       ,     SCREAMING_SNAKE_CASE_							: Any=1_6       ,     SCREAMING_SNAKE_CASE_							: int=2       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=0.02       ,     SCREAMING_SNAKE_CASE_							: Any=3       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=4       ,     SCREAMING_SNAKE_CASE_							: List[str]=None       ,     SCREAMING_SNAKE_CASE_							: str=0       ,     ):
        lowerCAmelCase_  :							List[str]								=					parent
        lowerCAmelCase_  :							List[Any]								=					batch_size
        lowerCAmelCase_  :							Optional[Any]								=					seq_length
        lowerCAmelCase_  :							Any								=					is_training
        lowerCAmelCase_  :							List[Any]								=					use_input_mask
        lowerCAmelCase_  :							int								=					use_token_type_ids
        lowerCAmelCase_  :							List[Any]								=					use_labels
        lowerCAmelCase_  :							str								=					vocab_size
        lowerCAmelCase_  :							Dict								=					hidden_size
        lowerCAmelCase_  :							str								=					num_hidden_layers
        lowerCAmelCase_  :							Optional[Any]								=					num_attention_heads
        lowerCAmelCase_  :							Optional[Any]								=					intermediate_size
        lowerCAmelCase_  :							Optional[int]								=					hidden_act
        lowerCAmelCase_  :							Union[str, Any]								=					hidden_dropout_prob
        lowerCAmelCase_  :							Optional[Any]								=					attention_probs_dropout_prob
        lowerCAmelCase_  :							Optional[Any]								=					max_position_embeddings
        lowerCAmelCase_  :							str								=					type_vocab_size
        lowerCAmelCase_  :							List[Any]								=					type_sequence_label_size
        lowerCAmelCase_  :							Optional[int]								=					initializer_range
        lowerCAmelCase_  :							List[str]								=					num_labels
        lowerCAmelCase_  :							List[str]								=					num_choices
        lowerCAmelCase_  :							Optional[int]								=					scope
        lowerCAmelCase_  :							Any								=					projection_dim



      def 	SCREAMING_SNAKE_CASE__					( self							: str			):
        lowerCAmelCase_  :							str								=					ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size			)

        lowerCAmelCase_  :							Optional[int]								=					None
        if self.use_input_mask:
          lowerCAmelCase_  :							List[str]								=					random_attention_mask([self.batch_size, self.seq_length]			)

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

        lowerCAmelCase_  :							Optional[Any]								=					None
        lowerCAmelCase_  :							Tuple								=					None
        lowerCAmelCase_  :							Union[str, Any]								=					None
        if self.use_labels:
          lowerCAmelCase_  :							Tuple								=					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
          lowerCAmelCase_  :							List[str]								=					ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels			)
          lowerCAmelCase_  :							Optional[int]								=					ids_tensor([self.batch_size]       ,     self.num_choices			)

        lowerCAmelCase_  :							str								=					self.get_config()

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



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        return DPRConfig(
            projection_dim=self.projection_dim       ,     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       ,     initializer_range=self.initializer_range       ,     )



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: List[str]       ,     SCREAMING_SNAKE_CASE_							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]			):
        lowerCAmelCase_  :							Tuple								=					DPRContextEncoder(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        lowerCAmelCase_  :							Tuple								=					model(SCREAMING_SNAKE_CASE_       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     token_type_ids=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							str								=					model(SCREAMING_SNAKE_CASE_       ,     token_type_ids=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model(SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: str       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Dict			):
        lowerCAmelCase_  :							str								=					DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        lowerCAmelCase_  :							Optional[int]								=					model(SCREAMING_SNAKE_CASE_       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     token_type_ids=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Union[str, Any]								=					model(SCREAMING_SNAKE_CASE_       ,     token_type_ids=SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[Any]								=					model(SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)			)



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Dict       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Any       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: int			):
        lowerCAmelCase_  :							Union[str, Any]								=					DPRReader(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        lowerCAmelCase_  :							Optional[Any]								=					model(
            SCREAMING_SNAKE_CASE_       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     )

        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)			)
        self.parent.assertEqual(result.relevance_logits.shape       ,     (self.batch_size,)			)





      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        lowerCAmelCase_  :							List[Any]								=					self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,(
            lowerCAmelCase_
        )		,
        )  :							Dict								=					config_and_inputs
        lowerCAmelCase_  :							Dict								=					{'input_ids': input_ids}
        return config, inputs_dict







@require_torch
class  UpperCamelCase__  (      lowercase_,   lowercase_,   unittest.TestCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	(
          (
              DPRContextEncoder,
              DPRQuestionEncoder,
              DPRReader,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE    							=	{"""feature-extraction""": DPRQuestionEncoder} if is_torch_available() else {}

      _SCREAMING_SNAKE_CASE    							=	False
      _SCREAMING_SNAKE_CASE    							=	False  # why?
      _SCREAMING_SNAKE_CASE    							=	False
      _SCREAMING_SNAKE_CASE    							=	False



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        lowerCAmelCase_  :							Tuple								=					DPRModelTester(self			)
        lowerCAmelCase_  :							List[str]								=					ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE_       ,     hidden_size=3_7			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict			):
        self.config_tester.run_common_tests()



      def 	SCREAMING_SNAKE_CASE__					( self							: str			):
        lowerCAmelCase_  :							Tuple								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_context_encoder(*SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        lowerCAmelCase_  :							Optional[int]								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_question_encoder(*SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        lowerCAmelCase_  :							Union[str, Any]								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_reader(*SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        lowerCAmelCase_  :							Union[str, Any]								=					self.model_tester.prepare_config_and_inputs()[0]

        lowerCAmelCase_  :							Optional[int]								=					DPRQuestionEncoder(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()

        with tempfile.TemporaryDirectory() as tmp_dirname:
          model.save_pretrained(SCREAMING_SNAKE_CASE_			)
          lowerCAmelCase_  :							Dict								=					DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE_       ,     projection_dim=5_1_2			)

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_  :							List[Any]								=					DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE_			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

        for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_  :							Any								=					DPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE_			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

        for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_  :							int								=					DPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE_			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

        for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_  :							Optional[Any]								=					DPRReader.from_pretrained(SCREAMING_SNAKE_CASE_			)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)







@require_torch
class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Union[str, Any]								=					DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'       ,     return_dict=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[int]								=					torch.tensor(
            [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]       ,     dtype=torch.long       ,     device=SCREAMING_SNAKE_CASE_			)  # [CLS] hello, is my dog cute? [SEP]
        lowerCAmelCase_  :							int								=					model(SCREAMING_SNAKE_CASE_			)[0]  # embedding shape = (1, 768)
        # compare the actual values for a slice.
        lowerCAmelCase_  :							int								=					torch.tensor(
            [
                [
                    0.03_23_62_53,
                    0.12_75_33_35,
                    0.16_81_85_09,
                    0.00_27_97_86,
                    0.3_89_69_33,
                    0.24_26_49_45,
                    0.2_17_89_71,
                    -0.02_33_52_27,
                    -0.08_48_19_59,
                    -0.14_32_41_17,
                ]
            ]       ,     dtype=torch.float       ,     device=SCREAMING_SNAKE_CASE_       ,     )
        self.assertTrue(torch.allclose(output[:, :1_0]       ,     SCREAMING_SNAKE_CASE_       ,     atol=1E-4			)			)





      @slow
      def 	SCREAMING_SNAKE_CASE__					( self							: str			):
        lowerCAmelCase_  :							Optional[Any]								=					DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'			)
        lowerCAmelCase_  :							int								=					DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'			)
        model.to(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Union[str, Any]								=					tokenizer(
            questions='What is love ?'       ,     titles='Haddaway'       ,     texts='What Is Love is a song recorded by the artist Haddaway'       ,     padding=SCREAMING_SNAKE_CASE_       ,     return_tensors='pt'       ,     )
        encoded_inputs.to(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							str								=					model(**SCREAMING_SNAKE_CASE_			)

        # compare the actual values for a slice.
        lowerCAmelCase_  :							Union[str, Any]								=					torch.tensor(
            [[-10.30_05, -10.77_65, -11.48_72, -11.68_41, -11.93_12, -10.30_02, -9.85_44, -11.73_78, -12.08_21, -10.29_75]]       ,     dtype=torch.float       ,     device=SCREAMING_SNAKE_CASE_       ,     )

        lowerCAmelCase_  :							List[str]								=					torch.tensor(
            [[-11.06_84, -11.70_41, -11.53_97, -10.34_65, -10.87_91, -6.84_43, -11.99_59, -11.03_64, -10.00_96, -6.84_05]]       ,     dtype=torch.float       ,     device=SCREAMING_SNAKE_CASE_       ,     )
        self.assertTrue(torch.allclose(outputs.start_logits[:, :1_0]       ,     SCREAMING_SNAKE_CASE_       ,     atol=1E-4			)			)
        self.assertTrue(torch.allclose(outputs.end_logits[:, :1_0]       ,     SCREAMING_SNAKE_CASE_       ,     atol=1E-4			)			)










"""simple docstring"""







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
     print("""#################### AVAILABLE DEVICES ####################""")
     print(jax.devices())
     print("""###########################################################""")

     # setup for wandb sweep
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
         """weight_decay""": args.weight_decay,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      Trainer(
         args=args,
         data_collator=data_collator,
         model_save_fn=model.save_pretrained,
         train_step_fn=train_step,
         val_step_fn=val_step,
         logger=logger,
         scheduler_fn=lr,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
     try:
          trainer.train(state, tr_dataset, val_dataset)
     except KeyboardInterrupt:
          print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

     print("""SAVING WEIGHTS IN `final-weights`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





