




import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
 from comet_ml import OfflineExperiment

if is_tensorboard_available():
 import struct

 import tensorboard.compat.proto.event_pba as event_pba

a_ 		=  logging.getLogger(__name__)

@require_tensorboard
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    '''test_project_with_config'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)
              __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers(snake_case			,							snake_case					)
              accelerator.end_training()
              for child in Path(F"{dirpath}/{project_name}"					).glob('''*/**'''					):
                  __UpperCamelCase			=    list(filter(lambda snake_case					: x.is_file()			,							child.iterdir()					)					)[0]
              self.assertNotEqual(str(snake_case					)			,							''''''					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    '''test_project_with_log'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)
              accelerator.init_trackers(snake_case					)
              __UpperCamelCase			=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
              accelerator.log(snake_case			,							step=0					)
              accelerator.end_training()
              # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
              # Names are randomly generated each time
              __UpperCamelCase			=    list(filter(lambda snake_case					: x.is_file()			,							Path(F"{dirpath}/{project_name}"					).iterdir()					)					)[0]
              self.assertNotEqual(str(snake_case					)			,							''''''					)







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    '''test_project_with_log'''
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)
              accelerator.init_trackers(snake_case					)
              __UpperCamelCase			=    {'''tensor''': torch.tensor(1					)}
              accelerator.log(snake_case			,							step=0					)
              accelerator.end_training()
              # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
              # Names are randomly generated each time
              __UpperCamelCase			=    list(filter(lambda snake_case					: x.is_file()			,							Path(F"{dirpath}/{project_name}"					).iterdir()					)					)[0]
              # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
              with open(snake_case			,							'''rb'''					) as f:
                  __UpperCamelCase			=    f.read()
              __UpperCamelCase			=    False
              while data:
                  __UpperCamelCase			=    struct.unpack('''Q'''			,							data[:8]					)

                  __UpperCamelCase			=    data[12 : 12 + int(header[0]					)]  # 8+4
                  __UpperCamelCase			=    data[12 + int(header[0]					) + 4 :]
                  __UpperCamelCase			=    event_pba.Event()

                  event.ParseFromString(snake_case					)
                  if event.HasField('''summary'''					):
                      for value in event.summary.value:
                          if value.simple_value == 1.0 and value.tag == "tensor":
                              __UpperCamelCase			=    True
              self.assertTrue(snake_case			,							'''Converted tensor was not found in the log file!'''					)







      def    snake_case						(  self       :			Union[str, Any]					):
          with self.assertRaisesRegex(snake_case			,							'''Logging with `tensorboard` requires a `logging_dir`'''					):
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''					)
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    ProjectConfiguration(total_limit=30					)
          with tempfile.TemporaryDirectory() as dirpath:
              __UpperCamelCase			=    Accelerator(log_with='''tensorboard'''			,							project_dir=snake_case			,							project_config=snake_case					)







@require_wandb
@mock.patch.dict(os.environ				,      {"WANDB_MODE": "offline"} )
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def    snake_case						(  self       :			Union[str, Any]					):
          super().setUp()
          # wandb let's us override where logs are stored to via the WANDB_DIR env var
          self.add_mocks(mock.patch.dict(os.environ			,							{'''WANDB_DIR''': self.tmpdir}					)					)







      @staticmethod
      def    snake_case						(  snake_case       :			str			,							snake_case       :			str			,							snake_case       :			bool = True					):
          __UpperCamelCase			=    RF"{section} ([\S\s]*?)\n\n"
          if record:
              __UpperCamelCase			=    RF"Record: {pattern}"
          __UpperCamelCase			=    re.findall(snake_case			,							snake_case					)[0]
          # A config
          if section == "config" or section == "history":
              __UpperCamelCase			=    re.findall(R'''"([a-zA-Z0-9_.,]+)'''			,							snake_case					)
              return {key: val for key, val in zip(cleaned_record[0::2]			,							cleaned_record[1::2]					)}
          # Everything else
          else:
              return dict(re.findall(R'''(\w+): "([^\s]+)"'''			,							snake_case					)					)







      @skip
      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    '''test_project_with_config'''
          __UpperCamelCase			=    Accelerator(log_with='''wandb'''					)
          __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
          __UpperCamelCase			=    {'''wandb''': {'''tags''': ['''my_tag''']}}
          accelerator.init_trackers(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
          accelerator.log(snake_case			,							step=0					)
          accelerator.end_training()
          # The latest offline log is stored at wandb/latest-run/*.wandb
          for child in Path(F"{self.tmpdir}/wandb/latest-run"					).glob('''*'''					):
              if child.is_file() and child.suffix == ".wandb":
                  __UpperCamelCase			=    subprocess.check_output(
                      ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(snake_case					)]			,							env=os.environ.copy()					).decode('''utf8'''			,							'''ignore'''					)
                  break

        # Check HPS through careful parsing and cleaning
          __UpperCamelCase			=    self.parse_log(snake_case			,							'''config'''					)
          self.assertEqual(logged_items['''num_iterations''']			,							'''12'''					)
          self.assertEqual(logged_items['''learning_rate''']			,							'''0.01'''					)
          self.assertEqual(logged_items['''some_boolean''']			,							'''false'''					)
          self.assertEqual(logged_items['''some_string''']			,							'''some_value'''					)
          self.assertEqual(logged_items['''some_string''']			,							'''some_value'''					)

          # Run tags
          __UpperCamelCase			=    self.parse_log(snake_case			,							'''run'''			,							snake_case					)
          self.assertEqual(logged_items['''tags''']			,							'''my_tag'''					)

          # Actual logging
          __UpperCamelCase			=    self.parse_log(snake_case			,							'''history'''					)
          self.assertEqual(logged_items['''total_loss''']			,							'''0.1'''					)
          self.assertEqual(logged_items['''iteration''']			,							'''1'''					)
          self.assertEqual(logged_items['''my_text''']			,							'''some_value'''					)
          self.assertEqual(logged_items['''_step''']			,							'''0'''					)







def        __SCREAMING_SNAKE_CASE (	self       ,  lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    run_name
    __UpperCamelCase			=    OfflineExperiment(project_name=lowercase_       ,  offline_directory=lowercase_   )
    logger.info(F"Initialized offline CometML project {self.run_name}"   )
    logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''   )

@require_comet_ml
@mock.patch.object(_SCREAMING_SNAKE_CASE				,      "__init__"				,      _SCREAMING_SNAKE_CASE )
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]			,							snake_case       :			str			,							snake_case       :			bool = False					):
          for log in log_list:
              __UpperCamelCase			=    json.loads(snake_case					)['''payload''']
              if is_param and "param" in j.keys():
                  if j["param"]["paramName"] == key:
                      return j["param"]["paramValue"]
              if "log_other" in j.keys():
                  if j["log_other"]["key"] == key:
                      return j["log_other"]["val"]
              if "metric" in j.keys():
                  if j["metric"]["metricName"] == key:
                      return j["metric"]["metricValue"]







      def    snake_case						(  self       :			Any					):
          with tempfile.TemporaryDirectory() as d:
              __UpperCamelCase			=    CometMLTracker('''test_project_with_config'''			,							snake_case					)
              __UpperCamelCase			=    Accelerator(log_with=snake_case					)
              __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers(snake_case			,							snake_case					)
              accelerator.end_training()
              __UpperCamelCase			=    os.listdir(snake_case					)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              __UpperCamelCase			=    os.path.join(snake_case			,							snake_case					)
              __UpperCamelCase			=    zipfile.ZipFile(snake_case			,							'''r'''					)
              __UpperCamelCase			=    archive.open('''messages.json'''					).read().decode('''utf-8'''					)
          __UpperCamelCase			=    log.split('''\n'''					)[:-1]
          self.assertEqual(self.get_value_from_key(snake_case			,							'''num_iterations'''			,							snake_case					)			,							12					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''learning_rate'''			,							snake_case					)			,							0.01					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''some_boolean'''			,							snake_case					)			,							snake_case					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''some_string'''			,							snake_case					)			,							'''some_value'''					)







      def    snake_case						(  self       :			List[Any]					):
          with tempfile.TemporaryDirectory() as d:
              __UpperCamelCase			=    CometMLTracker('''test_project_with_config'''			,							snake_case					)
              __UpperCamelCase			=    Accelerator(log_with=snake_case					)
              accelerator.init_trackers(snake_case					)
              __UpperCamelCase			=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
              accelerator.log(snake_case			,							step=0					)
              accelerator.end_training()
              __UpperCamelCase			=    os.listdir(snake_case					)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              __UpperCamelCase			=    os.path.join(snake_case			,							snake_case					)
              __UpperCamelCase			=    zipfile.ZipFile(snake_case			,							'''r'''					)
              __UpperCamelCase			=    archive.open('''messages.json'''					).read().decode('''utf-8'''					)
          __UpperCamelCase			=    log.split('''\n'''					)[:-1]
          self.assertEqual(self.get_value_from_key(snake_case			,							'''curr_step'''			,							snake_case					)			,							0					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''total_loss'''					)			,							0.1					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''iteration'''					)			,							1					)
          self.assertEqual(self.get_value_from_key(snake_case			,							'''my_text'''					)			,							'''some_value'''					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[int]     =       [
          "total_loss",
          "iteration",
          "my_text",
          "learning_rate",
          "num_iterations",
          "some_boolean",
          "some_string",
      ]

      lowerCAmelCase__      :			Any     =       "my_custom_tracker"
      lowerCAmelCase__      :			Optional[int]     =       False







      def __init__(  self       :			Dict			,							snake_case       :			str					):
          __UpperCamelCase			=    open(F"{dir}/log.csv"			,							'''w+'''					)
          __UpperCamelCase			=    csv.DictWriter(self.f			,							fieldnames=self._col_names					)
          self.writer.writeheader()







      @property
      def    snake_case						(  self       :			Optional[Any]					):
          return self.writer







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			dict					):
          logger.info('''Call init'''					)
          self.writer.writerow(snake_case					)







      def    snake_case						(  self       :			int			,							snake_case       :			dict			,							snake_case       :			Optional[int]					):
          logger.info('''Call log'''					)
          self.writer.writerow(snake_case					)







      def    snake_case						(  self       :			int					):
          self.f.close()







class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Any					):
          with tempfile.TemporaryDirectory() as d:
              __UpperCamelCase			=    MyCustomTracker(snake_case					)
              __UpperCamelCase			=    Accelerator(log_with=snake_case					)
              __UpperCamelCase			=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers('''Some name'''			,							snake_case					)
              accelerator.end_training()
              with open(F"{d}/log.csv"			,							'''r'''					) as f:
                  __UpperCamelCase			=    csv.DictReader(snake_case					)
                  __UpperCamelCase			=    next(snake_case					)
                  __UpperCamelCase			=    {
                      '''total_loss''': '''''',
                      '''iteration''': '''''',
                      '''my_text''': '''''',
                      '''learning_rate''': '''0.01''',
                      '''num_iterations''': '''12''',
                      '''some_boolean''': '''False''',
                      '''some_string''': '''some_value''',
                  }
                  self.assertDictEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[Any]					):
          with tempfile.TemporaryDirectory() as d:
              __UpperCamelCase			=    MyCustomTracker(snake_case					)
              __UpperCamelCase			=    Accelerator(log_with=snake_case					)
              accelerator.init_trackers('''Some name'''					)
              __UpperCamelCase			=    {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
              accelerator.log(snake_case			,							step=0					)
              accelerator.end_training()
              with open(F"{d}/log.csv"			,							'''r'''					) as f:
                  __UpperCamelCase			=    csv.DictReader(snake_case					)
                  __UpperCamelCase			=    next(snake_case					)
                  __UpperCamelCase			=    {
                      '''total_loss''': '''0.1''',
                      '''iteration''': '''1''',
                      '''my_text''': '''some_value''',
                      '''learning_rate''': '''''',
                      '''num_iterations''': '''''',
                      '''some_boolean''': '''''',
                      '''some_string''': '''''',
                  }
                  self.assertDictEqual(snake_case			,							snake_case					)












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_ 		=  logging.get_logger(__name__)

a_ 		=  [
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       RetriBertConfig
      lowerCAmelCase__      :			Optional[Any]     =       None
      lowerCAmelCase__      :			Dict     =       "retribert"







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[Any]					):
          if isinstance(snake_case			,							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(snake_case			,							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(snake_case			,							nn.LayerNorm					):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0					)







a_ 		=  R"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."				,      _SCREAMING_SNAKE_CASE				,      )
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[Any]			,							snake_case       :			RetriBertConfig					):
          super().__init__(snake_case					)
          __UpperCamelCase			=    config.projection_dim

          __UpperCamelCase			=    BertModel(snake_case					)
          __UpperCamelCase			=    None if config.share_encoders else BertModel(snake_case					)
          __UpperCamelCase			=    nn.Dropout(config.hidden_dropout_prob					)
          __UpperCamelCase			=    nn.Linear(config.hidden_size			,							config.projection_dim			,							bias=snake_case					)
          __UpperCamelCase			=    nn.Linear(config.hidden_size			,							config.projection_dim			,							bias=snake_case					)

          __UpperCamelCase			=    nn.CrossEntropyLoss(reduction='''mean'''					)

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			Dict			,							snake_case       :			List[str]			,							snake_case       :			Dict			,							snake_case       :			Any=-1			,							):
          # reproduces BERT forward pass with checkpointing
          if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
              return sent_encoder(snake_case			,							attention_mask=snake_case					)[1]
          else:
              # prepare implicit variables
              __UpperCamelCase			=    input_ids.device
              __UpperCamelCase			=    input_ids.size()
              __UpperCamelCase			=    torch.zeros(snake_case			,							dtype=torch.long			,							device=snake_case					)
              __UpperCamelCase			=    [None] * sent_encoder.config.num_hidden_layers
              __UpperCamelCase			=    sent_encoder.get_extended_attention_mask(
                  snake_case			,							snake_case					)

              # define function for checkpointing
              def partial_encode(*snake_case       :			Optional[Any]					):
                  __UpperCamelCase			=    sent_encoder.encoder(
                      inputs[0]			,							attention_mask=inputs[1]			,							head_mask=snake_case			,							)
                  __UpperCamelCase			=    encoder_outputs[0]
                  __UpperCamelCase			=    sent_encoder.pooler(snake_case					)
                  return pooled_output

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			List[str]=-1			,							):
          __UpperCamelCase			=    self.embed_sentences_checkpointed(
              snake_case			,							snake_case			,							self.bert_query			,							snake_case			,							)
          return self.project_query(snake_case					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Optional[int]=None			,							snake_case       :			Optional[Any]=-1			,							):
          __UpperCamelCase			=    self.embed_sentences_checkpointed(
              snake_case			,							snake_case			,							self.bert_query if self.bert_doc is None else self.bert_doc			,							snake_case			,							)
          return self.project_doc(snake_case					)







      def    snake_case						(  self       :			List[str]			,							snake_case       :			torch.LongTensor			,							snake_case       :			Optional[torch.FloatTensor]			,							snake_case       :			torch.LongTensor			,							snake_case       :			Optional[torch.FloatTensor]			,							snake_case       :			int = -1			,							):
          __UpperCamelCase			=    input_ids_query.device
          __UpperCamelCase			=    self.embed_questions(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    self.embed_answers(snake_case			,							snake_case			,							snake_case					)
          __UpperCamelCase			=    torch.mm(snake_case			,							a_reps.t()					)
          __UpperCamelCase			=    self.ce_loss(snake_case			,							torch.arange(compare_scores.shape[1]					).to(snake_case					)					)
          __UpperCamelCase			=    self.ce_loss(compare_scores.t()			,							torch.arange(compare_scores.shape[0]					).to(snake_case					)					)
          __UpperCamelCase			=    (loss_qa + loss_aq) / 2
          return loss






