




'''simple docstring'''





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a		:  int   			=			logging.getLogger()
a		:  int   			=			torch.cuda.is_available()
a		:  Optional[Any]   			=			{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 4_2,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Dict       					=			"""\n""".join(UpperCAmelCase_	)
    Path(UpperCAmelCase_	).open("""w"""	).writelines(UpperCAmelCase_	)


a		:  Optional[Any]   			=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
a		:  List[str]   			=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
a		:  Dict   			=			'''patrickvonplaten/t5-tiny-random'''
a		:  str   			=			'''sshleifer/t5-tinier-random'''
a		:  Any   			=			'''sshleifer/bart-tiny-random'''
a		:  List[str]   			=			'''sshleifer/tiny-mbart'''
a		:  int   			=			'''sshleifer/tiny-marian-en-de'''
a		:  List[Any]   			=			'''stas/tiny-wmt19-en-de'''


a		:  Optional[int]   			=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def UpperCAmelCase						(					UpperCAmelCase_	):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCAmelCase_      ,  F'''{split}.source'''	)      ,  UpperCAmelCase_	)
        _dump_articles(os.path.join(UpperCAmelCase_      ,  F'''{split}.target'''	)      ,  UpperCAmelCase_	)
    return tmp_dir




class     UpperCamelCase					(  snake_case__					):



  @classmethod
  def 						a_       ( cls   ):







      '''simple docstring'''


      logging.disable(logging.CRITICAL   )  # remove noisy download output from tracebacks
      return cls



  @slow
  @require_torch_gpu
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			list_models()
      _a							:       Tuple       					=			"""sshleifer"""
      _a							:       List[str]       					=			[x.modelId for x in model_list if x.modelId.startswith(__lowerCAmelCase   )]
      _a							:       List[str]       					=			["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
      _a							:       Optional[int]       					=			[]
      for m in model_ids:
          if m in allowed_to_be_broken:
              continue
          try:
              AutoConfig.from_pretrained(__lowerCAmelCase   )
          except Exception:
              failures.append(__lowerCAmelCase   )
      assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       str       					=			{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
      self._test_distiller_cli(__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			{
          """student_encoder_layers""": 2,
          """student_decoder_layers""": 1,
          """max_epochs""": 4,
          """val_check_interval""": 0.25,
          """alpha_hid""": 2.0,
          """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
      }
      _a							:       str       					=			self._test_distiller_cli(__lowerCAmelCase		,    check_contents=__lowerCAmelCase   )

      _a							:       Tuple       					=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
      self.assertEqual(1		,    len(__lowerCAmelCase   )   )
      _a							:       List[Any]       					=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
      self.assertEqual(len(__lowerCAmelCase   )		,    2   )
      _a							:       str       					=			lmap(str.strip		,    Path(model.hparams.data_dir   ).joinpath("""test.source"""   ).open().readlines()   )
      _a							:       Any       					=			tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(__lowerCAmelCase		,    __lowerCAmelCase		,    str(model.output_dir / """best_tfmr"""   )   )
      self.assertTrue(Path(__lowerCAmelCase   ).exists()   )

      _a							:       Optional[Any]       					=			self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]		,    transformer_ckpts[0].parent		,    __lowerCAmelCase   )
      assert os.path.exists(os.path.join(__lowerCAmelCase		,    """pytorch_model.bin"""   )   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Tuple       					=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase   )
      _a					,   _a							:       int       					=			model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
      _a							:       Any       					=			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]		,    dtype=torch.long		,    device=model.device   )
      _a							:       List[Any]       					=			target_ids[:, :-1].contiguous()  # Why this line?
      _a							:       Dict       					=			target_ids[:, 1:].clone()  # why clone?
      _a							:       List[Any]       					=			model(
          __lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    decoder_input_ids=__lowerCAmelCase		,    labels=__lowerCAmelCase		,    use_cache=__lowerCAmelCase   ).loss

      _a							:       str       					=			model(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    decoder_input_ids=__lowerCAmelCase		,    use_cache=__lowerCAmelCase   ).logits

      _a							:       Any       					=			nn.functional.log_softmax(__lowerCAmelCase		,    dim=-1   )
      _a					,   _a							:       int       					=			label_smoothed_nll_loss(
          __lowerCAmelCase		,    __lowerCAmelCase		,    0.1		,    ignore_index=model.config.pad_token_id   )
      with self.assertRaises(__lowerCAmelCase   ):
          # TODO: understand why this breaks
          self.assertEqual(__lowerCAmelCase		,    __lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			{
          """student_encoder_layers""": 2,
          """student_decoder_layers""": 1,
          """num_train_epochs""": 4,
          """val_check_interval""": 0.25,
          """alpha_hid""": 2.0,
          """task""": """translation""",
          """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
          """tokenizer_name""": MBART_TINY,
          """teacher""": MBART_TINY,
          """src_lang""": """en_XX""",
          """tgt_lang""": """ro_RO""",
      }
      _a							:       Optional[int]       					=			self._test_distiller_cli(__lowerCAmelCase		,    check_contents=__lowerCAmelCase   )
      assert model.model.config.model_type == "mbart"

      _a							:       Optional[int]       					=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
      self.assertEqual(1		,    len(__lowerCAmelCase   )   )
      _a							:       List[str]       					=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
      _a							:       int       					=			list(Path(model.output_dir   ).glob("""best_tfmr/*"""   )   )
      assert len(__lowerCAmelCase   ) > 2
      self.assertEqual(len(__lowerCAmelCase   )		,    2   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       int       					=			{
          """student_encoder_layers""": 1,
          """student_decoder_layers""": 1,
          """alpha_hid""": 2.0,
          """teacher""": T5_TINY,
          """model_name_or_path""": T5_TINY,
          """tokenizer_name""": T5_TINY,
      }
      self._test_distiller_cli(__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			{
          """teacher""": T5_TINY,
          """student""": T5_TINIER,
          """model_name_or_path""": T5_TINIER,
          """tokenizer_name""": T5_TINIER,
      }
      self._test_distiller_cli(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=True   ):







      '''simple docstring'''


      _a							:       str       					=			{
          """label_smoothing""": 0.0,
          """early_stopping_patience""": -1,
          """train_batch_size""": 1,
          """eval_batch_size""": 2,
          """max_epochs""": 2,
          """alpha_mlm""": 0.2,
          """alpha_ce""": 0.8,
          """do_predict""": True,
          """model_name_or_path""": """sshleifer/tinier_bart""",
          """teacher""": CHEAP_ARGS["""model_name_or_path"""],
          """val_check_interval""": 0.5,
      }
      default_updates.update(__lowerCAmelCase   )
      _a							:       dict       					=			CHEAP_ARGS.copy()
      _a							:       Optional[int]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
      _a							:       int       					=			self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    **__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			distill_main(argparse.Namespace(**__lowerCAmelCase   )   )
      if not check_contents:
          return model
      _a							:       Optional[Any]       					=			os.listdir(__lowerCAmelCase   )
      _a							:       List[str]       					=			{os.path.basename(__lowerCAmelCase   ) for p in contents}
      _a							:       List[Any]       					=			[p for p in contents if p.endswith("""ckpt"""   )]
      assert len(__lowerCAmelCase   ) > 0

      self.assertIn("""test_generations.txt"""		,    __lowerCAmelCase   )
      self.assertIn("""test_results.txt"""		,    __lowerCAmelCase   )

      _a							:       List[Any]       					=			load_json(model.metrics_save_path   )
      _a							:       List[Any]       					=			metrics["""val"""][-1]
      self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]		,    0.01   )
      self.assertGreaterEqual(1.0		,    last_step_stats["""val_avg_gen_time"""]   )
      self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']		,    __lowerCAmelCase   )
      _a							:       Union[str, Any]       					=			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1   )
      self.assertEqual(len(metrics["""val"""]   )		,    __lowerCAmelCase   )
      self.assertEqual(len(metrics["""test"""]   )		,    1   )
      return model






class     UpperCamelCase					(  snake_case__					):



  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]		,    )
  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       dict       					=			CHEAP_ARGS.copy()
      _a							:       Any       					=			"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
      _a							:       int       					=			0.1 if task == """translation""" else 0

      _a							:       Any       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
      _a							:       List[str]       					=			self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=__lowerCAmelCase		,    model_name_or_path=__lowerCAmelCase		,    tokenizer_name=__lowerCAmelCase		,    train_batch_size=2		,    eval_batch_size=2		,    output_dir=__lowerCAmelCase		,    do_predict=__lowerCAmelCase		,    task=__lowerCAmelCase		,    src_lang="""en_XX"""		,    tgt_lang="""ro_RO"""		,    freeze_encoder=__lowerCAmelCase		,    freeze_embeds=__lowerCAmelCase		,    )
      assert "n_train" in args_d
      _a							:       Union[str, Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
      _a							:       Any       					=			main(__lowerCAmelCase   )

      _a							:       Dict       					=			module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
          _a							:       Optional[Any]       					=			module.model.lm_head
          assert not lm_head.weight.requires_grad
          assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
          _a							:       Optional[Any]       					=			module.model.model
          _a							:       Any       					=			fsmt.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not fsmt.decoder.embed_tokens.weight.requires_grad
          # check that embeds are not the same
          assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
          _a							:       Optional[Any]       					=			module.model.model
          _a							:       Tuple       					=			bart.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not bart.shared.weight.requires_grad
          # check that embeds are the same
          assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
          assert bart.decoder.embed_tokens == bart.shared

      _a							:       Tuple       					=			load_json(module.output_dir / """text_batch.json"""   )
      assert isinstance(__lowerCAmelCase		,    __lowerCAmelCase   )
      assert len(__lowerCAmelCase   ) >= 4



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       dict       					=			CHEAP_ARGS.copy()

      _a							:       Optional[int]       					=			"""summarization"""
      _a							:       Optional[int]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

      args_d.update(
          data_dir=__lowerCAmelCase		,    tokenizer_name=__lowerCAmelCase		,    train_batch_size=2		,    eval_batch_size=2		,    do_predict=__lowerCAmelCase		,    task=__lowerCAmelCase		,    src_lang="""en_XX"""		,    tgt_lang="""ro_RO"""		,    freeze_encoder=__lowerCAmelCase		,    freeze_embeds=__lowerCAmelCase		,    )

      # test models whose config includes the extra_model_args
      _a							:       Optional[Any]       					=			BART_TINY
      _a							:       Any       					=			self.get_auto_remove_tmp_dir()
      _a							:       str       					=			args_d.copy()
      args_da.update(
          model_name_or_path=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    )
      _a							:       List[Any]       					=			("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
          _a							:       List[str]       					=			0.5
      _a							:       Optional[Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
      _a							:       Dict       					=			main(__lowerCAmelCase   )
      for p in extra_model_params:
          assert getattr(model.config		,    __lowerCAmelCase   ) == 0.5, f'''failed to override the model config for param {p}'''

      # test models whose config doesn't include the extra_model_args
      _a							:       Optional[int]       					=			T5_TINY
      _a							:       int       					=			self.get_auto_remove_tmp_dir()
      _a							:       List[Any]       					=			args_d.copy()
      args_da.update(
          model_name_or_path=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    )
      _a							:       Tuple       					=			"""encoder_layerdrop"""
      _a							:       Optional[Any]       					=			0.5
      _a							:       int       					=			argparse.Namespace(**__lowerCAmelCase   )
      with pytest.raises(__lowerCAmelCase   ) as excinfo:
          _a							:       int       					=			main(__lowerCAmelCase   )
      assert str(excinfo.value   ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       dict       					=			CHEAP_ARGS.copy()

      _a							:       str       					=			"""summarization"""
      _a							:       Optional[Any]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

      _a							:       Dict       					=			BART_TINY
      _a							:       Union[str, Any]       					=			self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=__lowerCAmelCase		,    model_name_or_path=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    tokenizer_name=__lowerCAmelCase		,    train_batch_size=2		,    eval_batch_size=2		,    do_predict=__lowerCAmelCase		,    task=__lowerCAmelCase		,    src_lang="""en_XX"""		,    tgt_lang="""ro_RO"""		,    freeze_encoder=__lowerCAmelCase		,    freeze_embeds=__lowerCAmelCase		,    )

      # emulate finetune.py
      _a							:       Optional[Any]       					=			argparse.ArgumentParser()
      _a							:       int       					=			pl.Trainer.add_argparse_args(__lowerCAmelCase   )
      _a							:       int       					=			SummarizationModule.add_model_specific_args(__lowerCAmelCase		,    os.getcwd()   )
      _a							:       int       					=			{"""--help""": True}

      # --help test
      with pytest.raises(__lowerCAmelCase   ) as excinfo:
          with CaptureStdout() as cs:
              _a							:       str       					=			parser.parse_args(__lowerCAmelCase   )
          assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      _a							:       Optional[int]       					=			lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      _a							:       Union[str, Any]       					=			"""non_existing_scheduler"""
      _a							:       List[Any]       					=			{f'''--lr_scheduler={unsupported_param}'''}
      with pytest.raises(__lowerCAmelCase   ) as excinfo:
          with CaptureStderr() as cs:
              _a							:       Optional[Any]       					=			parser.parse_args(__lowerCAmelCase   )
          assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      _a							:       Any       					=			f'''invalid choice: \'{unsupported_param}\''''
      assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

      # --lr_scheduler=existing_scheduler test
      _a							:       Dict       					=			"""cosine"""
      _a							:       List[Any]       					=			args_d.copy()
      _a							:       List[Any]       					=			supported_param
      _a							:       Union[str, Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			main(__lowerCAmelCase   )
      assert (
          getattr(model.hparams		,    """lr_scheduler"""   ) == supported_param
      ), f'''lr_scheduler={supported_param} shouldn\'t fail'''








'''simple docstring'''





import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


a		:  List[str]   			=			logging.get_logger(__name__)

a		:  Optional[Any]   			=			[
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]




class     UpperCamelCase					(  snake_case__					):
  a    :List[Any]      					=				RetriBertConfig
  a    :int      					=				None
  a    :List[Any]      					=				'retribert'



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    nn.Linear   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(__lowerCAmelCase		,    nn.Embedding   ):
          module.weight.data.normal_(mean=0.0		,    std=self.config.initializer_range   )
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
      elif isinstance(__lowerCAmelCase		,    nn.LayerNorm   ):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0   )






a		:  Union[str, Any]   			=			R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''




@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'			,		snake_case__			,		)
class     UpperCamelCase					(  snake_case__					):



  def __init__( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      super().__init__(__lowerCAmelCase   )
      _a							:       Tuple       					=			config.projection_dim

      _a							:       int       					=			BertModel(__lowerCAmelCase   )
      _a							:       int       					=			None if config.share_encoders else BertModel(__lowerCAmelCase   )
      _a							:       Optional[int]       					=			nn.Dropout(config.hidden_dropout_prob   )
      _a							:       Dict       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )
      _a							:       Any       					=			nn.Linear(config.hidden_size		,    config.projection_dim		,    bias=__lowerCAmelCase   )

      _a							:       Tuple       					=			nn.CrossEntropyLoss(reduction="""mean"""   )

      # Initialize weights and apply final processing
      self.post_init()



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase   )[1]
      else:
          # prepare implicit variables
          _a							:       Optional[int]       					=			input_ids.device
          _a							:       Optional[Any]       					=			input_ids.size()
          _a							:       str       					=			torch.zeros(__lowerCAmelCase		,    dtype=torch.long		,    device=__lowerCAmelCase   )
          _a							:       Optional[int]       					=			[None] * sent_encoder.config.num_hidden_layers
          _a							:       torch.Tensor       					=			sent_encoder.get_extended_attention_mask(
              __lowerCAmelCase		,    __lowerCAmelCase   )

          # define function for checkpointing
          def partial_encode(*__lowerCAmelCase   ):
              _a							:       Tuple       					=			sent_encoder.encoder(
                  inputs[0]		,    attention_mask=inputs[1]		,    head_mask=__lowerCAmelCase		,    )
              _a							:       List[str]       					=			encoder_outputs[0]
              _a							:       List[str]       					=			sent_encoder.pooler(__lowerCAmelCase   )
              return pooled_output

          # run embedding layer on everything at once
          _a							:       Optional[Any]       					=			sent_encoder.embeddings(
              input_ids=__lowerCAmelCase		,    position_ids=__lowerCAmelCase		,    token_type_ids=__lowerCAmelCase		,    inputs_embeds=__lowerCAmelCase   )
          # run encoding and pooling on one mini-batch at a time
          _a							:       Optional[int]       					=			[]
          for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
              _a							:       Union[str, Any]       					=			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Any       					=			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              _a							:       Optional[int]       					=			checkpoint.checkpoint(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
              pooled_output_list.append(__lowerCAmelCase   )
          return torch.cat(__lowerCAmelCase		,    dim=0   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       Any       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query		,    __lowerCAmelCase		,    )
      return self.project_query(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=-1		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			self.embed_sentences_checkpointed(
          __lowerCAmelCase		,    __lowerCAmelCase		,    self.bert_query if self.bert_doc is None else self.bert_doc		,    __lowerCAmelCase		,    )
      return self.project_doc(__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    ):







      '''simple docstring'''


      _a							:       str       					=			input_ids_query.device
      _a							:       Tuple       					=			self.embed_questions(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       List[str]       					=			self.embed_answers(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       Dict       					=			torch.mm(__lowerCAmelCase		,    a_reps.t()   )
      _a							:       str       					=			self.ce_loss(__lowerCAmelCase		,    torch.arange(compare_scores.shape[1]   ).to(__lowerCAmelCase   )   )
      _a							:       Any       					=			self.ce_loss(compare_scores.t()		,    torch.arange(compare_scores.shape[0]   ).to(__lowerCAmelCase   )   )
      _a							:       List[str]       					=			(loss_qa + loss_aq) / 2
      return loss


