
"""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

_a									=				logging.getLogger(__name__)


@require_tensorboard
class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       "test_project_with_config"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   int     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       {"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("*/**"    ):
           UpperCAmelCase_   :   Union[str, Any]     	=       list(filter(lambda lowercase_    : x.is_file()							,  child.iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   int     	=       {"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
        UpperCAmelCase_   :   int     	=       list(filter(lambda lowercase_    : x.is_file()							,  Path(F"""{dirpath}/{project_name}"""    ).iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   str     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"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
        UpperCAmelCase_   :   Optional[Any]     	=       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:
           UpperCAmelCase_   :   Any     	=       f.read()
        UpperCAmelCase_   :   List[Any]     	=       False
        while data:
           UpperCAmelCase_   :   Dict     	=       struct.unpack("Q"							,  data[:8]    )

           UpperCAmelCase_   :   Optional[Any]     	=       data[12 : 12 + int(header[0]    )]  # 8+4
           UpperCAmelCase_   :   int     	=       data[12 + int(header[0]    ) + 4 :]
           UpperCAmelCase_   :   Optional[Any]     	=       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":
                    UpperCAmelCase_   :   Optional[Any]     	=       True
        self.assertTrue(lowercase_							,  "Converted tensor was not found in the log file!"    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with self.assertRaisesRegex(lowercase_							,  "Logging with `tensorboard` requires a `logging_dir`"    ):
        UpperCAmelCase_   :   Optional[Any]     	=       Accelerator(log_with="tensorboard"    )
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       ProjectConfiguration(total_limit=30    )
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_							,  project_config=lowercase_    )




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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """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 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = True    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       RF"""{section} ([\S\s]*?)\n\n"""
     if record:
        UpperCAmelCase_   :   Dict     	=       RF"""Record: {pattern}"""
     UpperCAmelCase_   :   Optional[int]     	=       re.findall(lowercase_							,  lowercase_    )[0]
     # A config
     if section == "config" or section == "history":
        UpperCAmelCase_   :   Any     	=       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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_config"
     UpperCAmelCase_   :   List[Any]     	=       Accelerator(log_with="wandb"    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
     UpperCAmelCase_   :   List[str]     	=       {"wandb": {"tags": ["my_tag"]}}
     accelerator.init_trackers(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"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":
           UpperCAmelCase_   :   Optional[int]     	=       subprocess.check_output(
               ["wandb", "sync", "--view", "--verbose", str(lowercase_    )]							,  env=os.environ.copy()    ).decode("utf8"							,  "ignore"    )
           break

        # Check HPS through careful parsing and cleaning
     UpperCAmelCase_   :   List[str]     	=       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
     UpperCAmelCase_   :   Optional[int]     	=       self.parse_log(lowercase_							,  "run"							,  lowercase_    )
     self.assertEqual(logged_items["tags"]							,  "my_tag"    )

     # Actual logging
     UpperCAmelCase_   :   Union[str, Any]     	=       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       __a	(						self,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       run_name
   UpperCAmelCase_   :   str     	=       OfflineExperiment(project_name=__lowerCamelCase,						offline_directory=__lowerCamelCase    )
   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(lowercase__							,"""__init__"""							,lowercase__ )
class A_      (unittest.TestCase ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = False    ):

     """simple docstring"""
     for log in log_list:
        UpperCAmelCase_   :   str     	=       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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   str     	=       CometMLTracker("test_project_with_config"							,  lowercase_    )
        UpperCAmelCase_   :   Dict     	=       Accelerator(log_with=lowercase_    )
        UpperCAmelCase_   :   List[Any]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers(lowercase_							,  lowercase_    )
        accelerator.end_training()
        UpperCAmelCase_   :   Any     	=       os.listdir(lowercase_    )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        UpperCAmelCase_   :   List[Any]     	=       os.path.join(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       zipfile.ZipFile(lowercase_							,  "r"    )
        UpperCAmelCase_   :   List[Any]     	=       archive.open("messages.json"    ).read().decode("utf-8"    )
     UpperCAmelCase_   :   List[str]     	=       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_    )							,  0.01    )
     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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   List[Any]     	=       CometMLTracker("test_project_with_config"							,  lowercase_    )
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(lowercase_							,  step=0    )
        accelerator.end_training()
        UpperCAmelCase_   :   Tuple     	=       os.listdir(lowercase_    )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        UpperCAmelCase_   :   Dict     	=       os.path.join(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   str     	=       zipfile.ZipFile(lowercase_							,  "r"    )
        UpperCAmelCase_   :   Optional[Any]     	=       archive.open("messages.json"    ).read().decode("utf-8"    )
     UpperCAmelCase_   :   Optional[int]     	=       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 A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  SCREAMING_SNAKE_CASE__     :		Dict    	=						"""my_custom_tracker"""
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						False







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       open(F"""{dir}/log.csv"""							,  "w+"    )
     UpperCAmelCase_   :   Optional[int]     	=       csv.DictWriter(self.f							,  fieldnames=self._col_names    )
     self.writer.writeheader()







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.writer







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     logger.info("Call init"    )
     self.writer.writerow(lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     logger.info("Call log"    )
     self.writer.writerow(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.f.close()




class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with=lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"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:
           UpperCAmelCase_   :   Dict     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   List[Any]     	=       next(lowercase_    )
           UpperCAmelCase_   :   int     	=       {
               "total_loss": "",
               "iteration": "",
               "my_text": "",
               "learning_rate": "0.01",
               "num_iterations": "12",
               "some_boolean": "False",
               "some_string": "some_value",
           }
           self.assertDictEqual(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Dict     	=       Accelerator(log_with=lowercase_    )
        accelerator.init_trackers("Some name"    )
        UpperCAmelCase_   :   Any     	=       {"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:
           UpperCAmelCase_   :   Optional[int]     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   Any     	=       next(lowercase_    )
           UpperCAmelCase_   :   Tuple     	=       {
               "total_loss": "0.1",
               "iteration": "1",
               "my_text": "some_value",
               "learning_rate": "",
               "num_iterations": "",
               "some_boolean": "",
               "some_string": "",
           }
           self.assertDictEqual(lowercase_							,  lowercase_    )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: int												=	"splinter"







	def __init__( self:			str	,			__UpperCamelCase:			Any=3_05_22	,			__UpperCamelCase:			List[Any]=7_68	,			__UpperCamelCase:			str=12	,			__UpperCamelCase:			Optional[Any]=12	,			__UpperCamelCase:			Optional[int]=30_72	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Union[str, Any]=0.1	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			str=5_12	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			str=0.02	,			__UpperCamelCase:			Tuple=1E-12	,			__UpperCamelCase:			Optional[int]=True	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Optional[int]=1_04	,			**__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     initializer_range
						__magic_name__				=     type_vocab_size
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     use_cache
						__magic_name__				=     question_token_id




