




'''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

_SCREAMING_SNAKE_CASE							:     Any   	=				logging.getLogger(__name__)




@require_tensorboard
class 				_snake_case	(			unittest.TestCase							):




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




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




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

										'''simple docstring'''




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

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

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




			def      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   ProjectConfiguration(total_limit=30							)
										with tempfile.TemporaryDirectory() as dirpath:
																	snake_case_			      =   Accelerator(log_with="tensorboard"       ,							project_dir=lowerCamelCase__       ,							project_config=lowerCamelCase__							)




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




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''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      lowerCAmelCase__       (      a__       ,							a__       ,							a__ = True							)       ->       Optional[int]:

										'''simple docstring'''




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




			@skip
			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




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

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

										# Actual logging
										snake_case_			      =   self.parse_log(lowerCamelCase__       ,							"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							: Dict							,       snake_case							: Optional[Any]							,       snake_case							: Any  ):

							'''simple docstring'''






							snake_case_			      =   run_name
							snake_case_			      =   OfflineExperiment(project_name=snake_case							,       offline_directory=snake_case  )
							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 				_snake_case	(			unittest.TestCase							):




			@staticmethod
			def      lowerCAmelCase__       (      a__       ,							a__       ,							a__ = False							)       ->       List[Any]:

										'''simple docstring'''




										for log in log_list:
																	snake_case_			      =   json.loads(lowerCamelCase__							)["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      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




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




class 				_snake_case	(			_a							):
			lowerCAmelCase_  :   Union[str, Any]		 =					[
			    "total_loss",
			    "iteration",
			    "my_text",
			    "learning_rate",
			    "num_iterations",
			    "some_boolean",
			    "some_string",
			]

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




			def __init__(      self       ,							a__							)       ->       str:

										'''simple docstring'''




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




			@property
			def      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										return self.writer




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Dict:

										'''simple docstring'''




										logger.info("Call init"							)
										self.writer.writerow(lowerCamelCase__							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Tuple:

										'''simple docstring'''




										logger.info("Call log"							)
										self.writer.writerow(lowerCamelCase__							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										self.f.close()




class 				_snake_case	(			unittest.TestCase							):




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




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










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




