

'''simple docstring'''

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

UpperCAmelCase				   =  logging.getLogger(__name__)


@require_tensorboard
class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase					='test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowercase						,      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
                 __lowercase					=list(filter(lambda __lowercase      : x.is_file()						,      Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
                 self.assertNotEqual(str(__lowercase      )						,      ''      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					='test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'tensor': torch.tensor(1      )}
                 accelerator.log(__lowercase						,      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
                 __lowercase					=list(filter(lambda __lowercase      : 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(__lowercase						,      'rb'      ) as f:
                      __lowercase					=f.read()
                 __lowercase					=False
                 while data:
                      __lowercase					=struct.unpack('Q'						,      data[:8]      )

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

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







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            with self.assertRaisesRegex(__lowercase						,      'Logging with `tensorboard` requires a `logging_dir`'      ):
                 __lowercase					=Accelerator(log_with='tensorboard'      )
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=ProjectConfiguration(total_limit=30      )
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase						,      project_config=__lowercase      )






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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            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   (							__lowercase			: str						,      __lowercase			: str						,      __lowercase			: bool = True      ):







            """simple docstring"""







            __lowercase					=rf'''{section} ([\S\s]*?)\n\n'''
            if record:
                 __lowercase					=rf'''Record: {pattern}'''
            __lowercase					=re.findall(__lowercase						,      __lowercase      )[0]
            # A config
            if section == "config" or section == "history":
                 __lowercase					=re.findall(r'"([a-zA-Z0-9_.,]+)'						,      __lowercase      )
                 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]+)"'						,      __lowercase      )      )







       @skip
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					='test_project_with_config'
            __lowercase					=Accelerator(log_with='wandb'      )
            __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
            __lowercase					={'wandb': {'tags': ['my_tag']}}
            accelerator.init_trackers(__lowercase						,      __lowercase						,      __lowercase      )
            __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
            accelerator.log(__lowercase						,      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":
                      __lowercase					=subprocess.check_output(
                          ['wandb', 'sync', '--view', '--verbose', str(__lowercase      )]						,      env=os.environ.copy()      ).decode('utf8'						,      'ignore'      )
                      break

        # Check HPS through careful parsing and cleaning
            __lowercase					=self.parse_log(__lowercase						,      '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
            __lowercase					=self.parse_log(__lowercase						,      'run'						,      __lowercase      )
            self.assertEqual(logged_items['tags']						,      'my_tag'      )

            # Actual logging
            __lowercase					=self.parse_log(__lowercase						,      '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 				__UpperCamelCase							(	self						:  Union[str, Any],					lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=run_name
     __lowercase					=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(A   ,			"__init__"   ,			A   )
class   lowerCAmelCase     (      unittest.TestCase   ):







       @staticmethod
       def 			snake_case   (							__lowercase			: List[Any]						,      __lowercase			: str						,      __lowercase			: bool = False      ):







            """simple docstring"""







            for log in log_list:
                 __lowercase					=json.loads(__lowercase      )['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			: int      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=CometMLTracker('test_project_with_config'						,      __lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowercase						,      __lowercase      )
                 accelerator.end_training()
                 __lowercase					=os.listdir(__lowercase      )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 __lowercase					=os.path.join(__lowercase						,      __lowercase      )
                 __lowercase					=zipfile.ZipFile(__lowercase						,      'r'      )
                 __lowercase					=archive.open('messages.json'      ).read().decode('utf-8'      )
            __lowercase					=log.split('\n'      )[:-1]
            self.assertEqual(self.get_value_from_key(__lowercase						,      'num_iterations'						,      __lowercase      )						,      12      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'learning_rate'						,      __lowercase      )						,      0.0_1      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'some_boolean'						,      __lowercase      )						,      __lowercase      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'some_string'						,      __lowercase      )						,      'some_value'      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







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






class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= [
           "total_loss",
           "iteration",
           "my_text",
           "learning_rate",
           "num_iterations",
           "some_boolean",
           "some_string",
       ]

       lowerCAmelCase_		= "my_custom_tracker"
       lowerCAmelCase_		= False







       def __init__(							self			: List[str]						,      __lowercase			: str      ):







            """simple docstring"""







            __lowercase					=open(f'''{dir}/log.csv'''						,      'w+'      )
            __lowercase					=csv.DictWriter(self.f						,      fieldnames=self._col_names      )
            self.writer.writeheader()







       @property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return self.writer







       def 			snake_case   (							self			: Tuple						,      __lowercase			: dict      ):







            """simple docstring"""







            logger.info('Call init'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: List[Any]						,      __lowercase			: dict						,      __lowercase			: Optional[int]      ):







            """simple docstring"""







            logger.info('Call log'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            self.f.close()






class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







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





'''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   lowerCAmelCase     :







       def __init__(							self			: List[str]						,      __lowercase			: List[Any]						,      __lowercase			: Union[str, Any]=13						,      __lowercase			: Optional[int]=7						,      __lowercase			: Union[str, Any]=False						,      __lowercase			: str=True						,      __lowercase			: Dict=True						,      __lowercase			: Optional[Any]=True						,      __lowercase			: int=99						,      __lowercase			: Optional[Any]=32						,      __lowercase			: Optional[int]=5						,      __lowercase			: List[str]=4						,      __lowercase			: Any=37						,      __lowercase			: str="gelu"						,      __lowercase			: str=0.1						,      __lowercase			: str=0.1						,      __lowercase			: str=512						,      __lowercase			: Dict=16						,      __lowercase			: Dict=2						,      __lowercase			: List[str]=0.0_2						,      __lowercase			: Union[str, Any]=3						,      __lowercase			: List[Any]=4						,      __lowercase			: List[str]=None						,      __lowercase			: List[Any]=0						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=seq_length
            __lowercase					=is_training
            __lowercase					=use_input_mask
            __lowercase					=use_token_type_ids
            __lowercase					=use_labels
            __lowercase					=vocab_size
            __lowercase					=hidden_size
            __lowercase					=num_hidden_layers
            __lowercase					=num_attention_heads
            __lowercase					=intermediate_size
            __lowercase					=hidden_act
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=max_position_embeddings
            __lowercase					=type_vocab_size
            __lowercase					=type_sequence_label_size
            __lowercase					=initializer_range
            __lowercase					=num_labels
            __lowercase					=num_choices
            __lowercase					=scope
            __lowercase					=projection_dim







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

            __lowercase					=None
            if self.use_input_mask:
                 __lowercase					=random_attention_mask([self.batch_size, self.seq_length]      )

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

            __lowercase					=None
            __lowercase					=None
            __lowercase					=None
            if self.use_labels:
                 __lowercase					=ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
                 __lowercase					=ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )
                 __lowercase					=ids_tensor([self.batch_size]						,      self.num_choices      )

            __lowercase					=self.get_config()

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







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            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 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: int						,      __lowercase			: Tuple						,      __lowercase			: Any						,      __lowercase			: Union[str, Any]						,      __lowercase			: List[Any]						,      __lowercase			: Tuple						,      __lowercase			: List[str]      ):







            """simple docstring"""







            __lowercase					=DPRContextEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      attention_mask=__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.pooler_output.shape						,      (self.batch_size, self.projection_dim or self.hidden_size)      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[int]						,      __lowercase			: Union[str, Any]						,      __lowercase			: Any						,      __lowercase			: Optional[Any]						,      __lowercase			: Tuple						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=DPRQuestionEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      attention_mask=__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.pooler_output.shape						,      (self.batch_size, self.projection_dim or self.hidden_size)      )







       def 			snake_case   (							self			: List[str]						,      __lowercase			: Optional[Any]						,      __lowercase			: Tuple						,      __lowercase			: str						,      __lowercase			: Tuple						,      __lowercase			: str						,      __lowercase			: List[Any]						,      __lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=DPRReader(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(
                __lowercase						,      attention_mask=__lowercase						,      )

            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 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.prepare_config_and_inputs()
            (
                (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     
            )					=config_and_inputs
            __lowercase					={'input_ids': input_ids}
            return config, inputs_dict






@require_torch
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (
               DPRContextEncoder,
               DPRQuestionEncoder,
               DPRReader,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_		= {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False  # why?
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=DPRModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=__lowercase						,      hidden_size=37      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_context_encoder(*__lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_question_encoder(*__lowercase      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_reader(*__lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()[0]

            __lowercase					=DPRQuestionEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 model.save_pretrained(__lowercase      )
                 __lowercase					=DPRQuestionEncoder.from_pretrained(__lowercase						,      projection_dim=512      )

            self.assertIsNotNone(__lowercase      )







       @slow
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







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

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

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

            for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=DPRReader.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )






@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ):







       @slow
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'						,      return_dict=__lowercase      )
            model.to(__lowercase      )

            __lowercase					=torch.tensor(
                [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]						,      dtype=torch.long						,      device=__lowercase      )  # [CLS] hello, is my dog cute? [SEP]
            __lowercase					=model(__lowercase      )[0]  # embedding shape = (1, 768)
            # compare the actual values for a slice.
            __lowercase					=torch.tensor(
                [
                    [
                        0.0_3_2_3_6_2_5_3,
                        0.1_2_7_5_3_3_3_5,
                        0.1_6_8_1_8_5_0_9,
                        0.0_0_2_7_9_7_8_6,
                        0.3_8_9_6_9_3_3,
                        0.2_4_2_6_4_9_4_5,
                        0.2_1_7_8_9_7_1,
                        -0.0_2_3_3_5_2_2_7,
                        -0.0_8_4_8_1_9_5_9,
                        -0.1_4_3_2_4_1_1_7,
                    ]
                ]						,      dtype=torch.float						,      device=__lowercase						,      )
            self.assertTrue(torch.allclose(output[:, :10]						,      __lowercase						,      atol=1E-4      )      )







       @slow
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'      )
            __lowercase					=DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'      )
            model.to(__lowercase      )

            __lowercase					=tokenizer(
                questions='What is love ?'						,      titles='Haddaway'						,      texts='What Is Love is a song recorded by the artist Haddaway'						,      padding=__lowercase						,      return_tensors='pt'						,      )
            encoded_inputs.to(__lowercase      )

            __lowercase					=model(**__lowercase      )

            # compare the actual values for a slice.
            __lowercase					=torch.tensor(
                [[-1_0.3_0_0_5, -1_0.7_7_6_5, -1_1.4_8_7_2, -1_1.6_8_4_1, -1_1.9_3_1_2, -1_0.3_0_0_2, -9.8_5_4_4, -1_1.7_3_7_8, -1_2.0_8_2_1, -1_0.2_9_7_5]]						,      dtype=torch.float						,      device=__lowercase						,      )

            __lowercase					=torch.tensor(
                [[-1_1.0_6_8_4, -1_1.7_0_4_1, -1_1.5_3_9_7, -1_0.3_4_6_5, -1_0.8_7_9_1, -6.8_4_4_3, -1_1.9_9_5_9, -1_1.0_3_6_4, -1_0.0_0_9_6, -6.8_4_0_5]]						,      dtype=torch.float						,      device=__lowercase						,      )
            self.assertTrue(torch.allclose(outputs.start_logits[:, :10]						,      __lowercase						,      atol=1E-4      )      )
            self.assertTrue(torch.allclose(outputs.end_logits[:, :10]						,      __lowercase						,      atol=1E-4      )      )


