





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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"""test_project_with_config"""
										with tempfile.TemporaryDirectory() as dirpath:
															lowerCamelCase__      =							Accelerator(log_with="tensorboard"						,	project_dir=_lowerCAmelCase						)
															lowerCamelCase__      =							{"""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("*/**"						):
																				lowerCamelCase__      =							list(filter(lambda _lowerCAmelCase						: x.is_file()						,	child.iterdir()						)						)[0]
															self.assertNotEqual(str(_lowerCAmelCase						)						,	""						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"""test_project_with_log"""
										with tempfile.TemporaryDirectory() as dirpath:
															lowerCamelCase__      =							Accelerator(log_with="tensorboard"						,	project_dir=_lowerCAmelCase						)
															accelerator.init_trackers(_lowerCAmelCase						)
															lowerCamelCase__      =							{"""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
															lowerCamelCase__      =							list(filter(lambda _lowerCAmelCase						: x.is_file()						,	Path(F"{dirpath}/{project_name}"						).iterdir()						)						)[0]
															self.assertNotEqual(str(_lowerCAmelCase						)						,	""						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"""test_project_with_log"""
										with tempfile.TemporaryDirectory() as dirpath:
															lowerCamelCase__      =							Accelerator(log_with="tensorboard"						,	project_dir=_lowerCAmelCase						)
															accelerator.init_trackers(_lowerCAmelCase						)
															lowerCamelCase__      =							{"""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
															lowerCamelCase__      =							list(filter(lambda _lowerCAmelCase						: 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:
																				lowerCamelCase__      =							f.read()
															lowerCamelCase__      =							False
															while data:
																				lowerCamelCase__      =							struct.unpack("Q"						,	data[:8]						)

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





					def        __magic_name__			(			self						):





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






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

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





					"""simple docstring"""


					def        __magic_name__			(			self						):
										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        __magic_name__			(			_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = True						):
										lowerCamelCase__      =							RF"{section} ([\S\s]*?)\n\n"
										if record:
															lowerCamelCase__      =							RF"Record: {pattern}"
										lowerCamelCase__      =							re.findall(_lowerCAmelCase						,	_lowerCAmelCase						)[0]
										# A config
										if section == "config" or section == "history":
															lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"""test_project_with_config"""
										lowerCamelCase__      =							Accelerator(log_with="wandb"						)
										lowerCamelCase__      =							{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
										lowerCamelCase__      =							{"""wandb""": {"""tags""": ["""my_tag"""]}}
										accelerator.init_trackers(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)
										lowerCamelCase__      =							{"""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":
																				lowerCamelCase__      =							subprocess.check_output(
																				    ["wandb", "sync", "--view", "--verbose", str(_lowerCAmelCase						)]						,	env=os.environ.copy()						).decode("utf8"						,	"ignore"						)
																				break

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

										# Actual logging
										lowerCamelCase__      =							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,  a,  a)			->Optional[int]:
					lowerCamelCase__      =							run_name
					lowerCamelCase__      =							OfflineExperiment(project_name=__SCREAMING_SNAKE_CASE,  offline_directory=__SCREAMING_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(_SCREAMING_SNAKE_CASE				,							"__init__"				,							_SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@staticmethod
					def        __magic_name__			(			_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = False						):





										for log in log_list:
															lowerCamelCase__      =							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        __magic_name__			(			self						):





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





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





					"""simple docstring"""


					A__								=     [
					    "total_loss",
					    "iteration",
					    "my_text",
					    "learning_rate",
					    "num_iterations",
					    "some_boolean",
					    "some_string",
					]

					A__								=     "my_custom_tracker"
					A__								=     False





					def __init__(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							open(F"{dir}/log.csv"						,	"w+"						)
										lowerCamelCase__      =							csv.DictWriter(self.f						,	fieldnames=self._col_names						)
										self.writer.writeheader()





					@property
					def        __magic_name__			(			self						):
										return self.writer





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										logger.info("Call init"						)
										self.writer.writerow(_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										logger.info("Call log"						)
										self.writer.writerow(_lowerCAmelCase						)






					def        __magic_name__			(			self						):
										self.f.close()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):





										with tempfile.TemporaryDirectory() as d:
															lowerCamelCase__      =							MyCustomTracker(_lowerCAmelCase						)
															lowerCamelCase__      =							Accelerator(log_with=_lowerCAmelCase						)
															lowerCamelCase__      =							{"""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:
																				lowerCamelCase__      =							csv.DictReader(_lowerCAmelCase						)
																				lowerCamelCase__      =							next(_lowerCAmelCase						)
																				lowerCamelCase__      =							{
																				    """total_loss""": """""",
																				    """iteration""": """""",
																				    """my_text""": """""",
																				    """learning_rate""": """0.01""",
																				    """num_iterations""": """12""",
																				    """some_boolean""": """False""",
																				    """some_string""": """some_value""",
																				}
																				self.assertDictEqual(_lowerCAmelCase						,	_lowerCAmelCase						)






					def        __magic_name__			(			self						):





										with tempfile.TemporaryDirectory() as d:
															lowerCamelCase__      =							MyCustomTracker(_lowerCAmelCase						)
															lowerCamelCase__      =							Accelerator(log_with=_lowerCAmelCase						)
															accelerator.init_trackers("Some name"						)
															lowerCamelCase__      =							{"""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:
																				lowerCamelCase__      =							csv.DictReader(_lowerCAmelCase						)
																				lowerCamelCase__      =							next(_lowerCAmelCase						)
																				lowerCamelCase__      =							{
																				    """total_loss""": """0.1""",
																				    """iteration""": """1""",
																				    """my_text""": """some_value""",
																				    """learning_rate""": """""",
																				    """num_iterations""": """""",
																				    """some_boolean""": """""",
																				    """some_string""": """""",
																				}
																				self.assertDictEqual(_lowerCAmelCase						,	_lowerCAmelCase						)



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



