






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_					:		str           = logging.getLogger(__name__)

@require_tensorboard
class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Union[str, Any]		=      """test_project_with_config"""
		with tempfile.TemporaryDirectory() as dirpath:
			UpperCamelCase	:Tuple		=      Accelerator(log_with="""tensorboard"""					,    project_dir=lowerCAmelCase_      )
			UpperCamelCase	:List[Any]		=      {"""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("""*/**"""      ):
				UpperCamelCase	:str		=      list(filter(lambda __lowerCamelCase      : x.is_file()					,    child.iterdir()      )      )[0]
			self.assertNotEqual(str(lowerCAmelCase_      )					,    """"""      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Union[str, Any]		=      """test_project_with_log"""
		with tempfile.TemporaryDirectory() as dirpath:
			UpperCamelCase	:List[str]		=      Accelerator(log_with="""tensorboard"""					,    project_dir=lowerCAmelCase_      )
			accelerator.init_trackers(lowerCAmelCase_      )
			UpperCamelCase	:Tuple		=      {"""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
			UpperCamelCase	:List[str]		=      list(filter(lambda __lowerCamelCase      : x.is_file()					,    Path(F"""{dirpath}/{project_name}"""      ).iterdir()      )      )[0]
			self.assertNotEqual(str(lowerCAmelCase_      )					,    """"""      )





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Optional[Any]		=      """test_project_with_log"""
		with tempfile.TemporaryDirectory() as dirpath:
			UpperCamelCase	:str		=      Accelerator(log_with="""tensorboard"""					,    project_dir=lowerCAmelCase_      )
			accelerator.init_trackers(lowerCAmelCase_      )
			UpperCamelCase	:Optional[Any]		=      {"""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
			UpperCamelCase	:Optional[Any]		=      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:
				UpperCamelCase	:Any		=      f.read()
			UpperCamelCase	:Union[str, Any]		=      False
			while data:
				UpperCamelCase	:Union[str, Any]		=      struct.unpack("""Q"""					,    data[:8]      )

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





	def 		_A					(						self							:   Dict      ):
		with self.assertRaisesRegex(lowerCAmelCase_					,    """Logging with `tensorboard` requires a `logging_dir`"""      ):
			UpperCamelCase	:Any		=      Accelerator(log_with="""tensorboard"""      )
		with tempfile.TemporaryDirectory() as dirpath:
			UpperCamelCase	:List[Any]		=      Accelerator(log_with="""tensorboard"""					,    project_dir=lowerCAmelCase_      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Tuple		=      ProjectConfiguration(total_limit=30      )
		with tempfile.TemporaryDirectory() as dirpath:
			UpperCamelCase	:Tuple		=      Accelerator(log_with="""tensorboard"""					,    project_dir=lowerCAmelCase_					,    project_config=lowerCAmelCase_      )

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





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





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

        # Check HPS through careful parsing and cleaning
		UpperCamelCase	:List[Any]		=      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
		UpperCamelCase	:Optional[Any]		=      self.parse_log(lowerCAmelCase_					,    """run"""					,    lowerCAmelCase_      )
		self.assertEqual(logged_items["""tags"""]					,    """my_tag"""      )

		# Actual logging
		UpperCamelCase	:Any		=      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   SCREAMING_SNAKE_CASE_				(				self   :    int      ,     __magic_name__   :    str      ,     __magic_name__   :    str   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      run_name
	UpperCamelCase	:Union[str, Any]		=      OfflineExperiment(project_name=UpperCamelCase__      ,     offline_directory=UpperCamelCase__   )
	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(_lowerCamelCase						,  """__init__"""						,  _lowerCamelCase )
class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	@staticmethod
	def 		_A					(						__lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Any = False      ):
		for log in log_list:
			UpperCamelCase	:Union[str, Any]		=      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 		_A					(						self							:   Dict      ):
		with tempfile.TemporaryDirectory() as d:
			UpperCamelCase	:List[str]		=      CometMLTracker("""test_project_with_config"""					,    lowerCAmelCase_      )
			UpperCamelCase	:Optional[Any]		=      Accelerator(log_with=lowerCAmelCase_      )
			UpperCamelCase	:int		=      {"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
			accelerator.init_trackers(lowerCAmelCase_					,    lowerCAmelCase_      )
			accelerator.end_training()
			UpperCamelCase	:int		=      os.listdir(lowerCAmelCase_      )[0]  # Comet is nice, it's just a zip file here
			# We parse the raw logs
			UpperCamelCase	:Optional[Any]		=      os.path.join(lowerCAmelCase_					,    lowerCAmelCase_      )
			UpperCamelCase	:int		=      zipfile.ZipFile(lowerCAmelCase_					,    """r"""      )
			UpperCamelCase	:Optional[Any]		=      archive.open("""messages.json"""      ).read().decode("""utf-8"""      )
		UpperCamelCase	:str		=      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 		_A					(						self							:   Optional[Any]      ):
		with tempfile.TemporaryDirectory() as d:
			UpperCamelCase	:Optional[Any]		=      CometMLTracker("""test_project_with_config"""					,    lowerCAmelCase_      )
			UpperCamelCase	:Union[str, Any]		=      Accelerator(log_with=lowerCAmelCase_      )
			accelerator.init_trackers(lowerCAmelCase_      )
			UpperCamelCase	:Optional[int]		=      {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
			accelerator.log(lowerCAmelCase_					,    step=0      )
			accelerator.end_training()
			UpperCamelCase	:Dict		=      os.listdir(lowerCAmelCase_      )[0]  # Comet is nice, it's just a zip file here
			# We parse the raw logs
			UpperCamelCase	:Optional[Any]		=      os.path.join(lowerCAmelCase_					,    lowerCAmelCase_      )
			UpperCamelCase	:Dict		=      zipfile.ZipFile(lowerCAmelCase_					,    """r"""      )
			UpperCamelCase	:int		=      archive.open("""messages.json"""      ).read().decode("""utf-8"""      )
		UpperCamelCase	:Optional[Any]		=      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			(     _lowerCamelCase ):
	snake_case__				:	List[str]	      =    [
	    """total_loss""",
	    """iteration""",
	    """my_text""",
	    """learning_rate""",
	    """num_iterations""",
	    """some_boolean""",
	    """some_string""",
	]

	snake_case__				:	Optional[int]	      =    """my_custom_tracker"""
	snake_case__				:	Union[str, Any]	      =    False





	def __init__(						self							:   Optional[int]					,    __lowerCamelCase							:   List[Any]      ):
		UpperCamelCase	:List[Any]		=      open(F"""{dir}/log.csv"""					,    """w+"""      )
		UpperCamelCase	:Union[str, Any]		=      csv.DictWriter(self.f					,    fieldnames=self._col_names      )
		self.writer.writeheader()





	@property
	def 		_A					(						self							:   Dict      ):
		return self.writer





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   str      ):
		logger.info("""Call init"""      )
		self.writer.writerow(lowerCAmelCase_      )





	def 		_A					(						self							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   Optional[int]      ):
		logger.info("""Call log"""      )
		self.writer.writerow(lowerCAmelCase_      )





	def 		_A					(						self							:   Dict      ):
		self.f.close()

class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	def 		_A					(						self							:   int      ):
		with tempfile.TemporaryDirectory() as d:
			UpperCamelCase	:Union[str, Any]		=      MyCustomTracker(lowerCAmelCase_      )
			UpperCamelCase	:int		=      Accelerator(log_with=lowerCAmelCase_      )
			UpperCamelCase	:Any		=      {"""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:
				UpperCamelCase	:Dict		=      csv.DictReader(lowerCAmelCase_      )
				UpperCamelCase	:Union[str, Any]		=      next(lowerCAmelCase_      )
				UpperCamelCase	:Dict		=      {
				    """total_loss""": """""",
				    """iteration""": """""",
				    """my_text""": """""",
				    """learning_rate""": """0.01""",
				    """num_iterations""": """12""",
				    """some_boolean""": """False""",
				    """some_string""": """some_value""",
				}
				self.assertDictEqual(lowerCAmelCase_					,    lowerCAmelCase_      )





	def 		_A					(						self							:   str      ):
		with tempfile.TemporaryDirectory() as d:
			UpperCamelCase	:int		=      MyCustomTracker(lowerCAmelCase_      )
			UpperCamelCase	:Optional[int]		=      Accelerator(log_with=lowerCAmelCase_      )
			accelerator.init_trackers("""Some name"""      )
			UpperCamelCase	:Union[str, Any]		=      {"""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:
				UpperCamelCase	:Union[str, Any]		=      csv.DictReader(lowerCAmelCase_      )
				UpperCamelCase	:str		=      next(lowerCAmelCase_      )
				UpperCamelCase	:Union[str, Any]		=      {
				    """total_loss""": """0.1""",
				    """iteration""": """1""",
				    """my_text""": """some_value""",
				    """learning_rate""": """""",
				    """num_iterations""": """""",
				    """some_boolean""": """""",
				    """some_string""": """""",
				}
				self.assertDictEqual(lowerCAmelCase_					,    lowerCAmelCase_      )







import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
					import torch
class       UpperCamelCase_		(	unittest.TestCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					load_tool('speech-to-text'	)
										self.tool.setup()




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.tool(torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.tool(audio=torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)



