






'''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      :     Any       	=logging.getLogger(__name__)

@require_tensorboard
class _lowercase      (							unittest.TestCase						):




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




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




							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      Optional[Any]			    =			"""test_project_with_log"""
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCamelCase__   :      Tuple			    =			Accelerator(log_with="""tensorboard"""     ,				project_dir=UpperCamelCase__	)
																					accelerator.init_trackers(UpperCamelCase__	)
																					lowerCamelCase__   :      Any			    =			{"""tensor""": torch.tensor(1	)}
																					accelerator.log(UpperCamelCase__     ,				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__   :      Union[str, Any]			    =			list(filter(lambda UpperCamelCase__	: 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(UpperCamelCase__     ,				"""rb"""	) as f:
																												lowerCamelCase__   :      List[str]			    =			f.read()
																					lowerCamelCase__   :      List[Any]			    =			False
																					while data:
																												lowerCamelCase__   :      List[str]			    =			struct.unpack("""Q"""     ,				data[:8]	)

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

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




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														with self.assertRaisesRegex(UpperCamelCase__     ,				"""Logging with `tensorboard` requires a `logging_dir`"""	):
																					lowerCamelCase__   :      Dict			    =			Accelerator(log_with="""tensorboard"""	)
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCamelCase__   :      Optional[Any]			    =			Accelerator(log_with="""tensorboard"""     ,				project_dir=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			ProjectConfiguration(total_limit=30	)
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCamelCase__   :      int			    =			Accelerator(log_with="""tensorboard"""     ,				project_dir=UpperCamelCase__     ,				project_config=UpperCamelCase__	)



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




							def 						lowerCamelCase_							(				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 						lowerCamelCase_							(				UpperCamelCase__:							str     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							bool = True	):
														lowerCamelCase__   :      List[str]			    =			RF'''{section} ([\S\s]*?)\n\n'''
														if record:
																					lowerCamelCase__   :      Any			    =			RF'''Record: {pattern}'''
														lowerCamelCase__   :      Tuple			    =			re.findall(UpperCamelCase__     ,				UpperCamelCase__	)[0]
														# A config
														if section == "config" or section == "history":
																					lowerCamelCase__   :      Optional[Any]			    =			re.findall(R"""\"([a-zA-Z0-9_.,]+)"""     ,				UpperCamelCase__	)
																					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]+)\""""     ,				UpperCamelCase__	)	)




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

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

														# Actual logging
														lowerCamelCase__   :      Dict			    =			self.parse_log(UpperCamelCase__     ,				"""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							,     UpperCamelCase							,     UpperCamelCase ) ->			str:
							lowerCamelCase__   :      int			    =			run_name
							lowerCamelCase__   :      str			    =			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(_lowercase							, """__init__"""							, _lowercase						)
class _lowercase      (							unittest.TestCase						):




							@staticmethod
							def 						lowerCamelCase_							(				UpperCamelCase__:							Tuple     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							bool = False	):
														for log in log_list:
																					lowerCamelCase__   :      Union[str, Any]			    =			json.loads(UpperCamelCase__	)["""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:							Union[str, Any]	):
														with tempfile.TemporaryDirectory() as d:
																					lowerCamelCase__   :      Tuple			    =			CometMLTracker("""test_project_with_config"""     ,				UpperCamelCase__	)
																					lowerCamelCase__   :      str			    =			Accelerator(log_with=UpperCamelCase__	)
																					lowerCamelCase__   :      Optional[int]			    =			{"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
																					accelerator.init_trackers(UpperCamelCase__     ,				UpperCamelCase__	)
																					accelerator.end_training()
																					lowerCamelCase__   :      Optional[int]			    =			os.listdir(UpperCamelCase__	)[0]  # Comet is nice, it's just a zip file here
																					# We parse the raw logs
																					lowerCamelCase__   :      Dict			    =			os.path.join(UpperCamelCase__     ,				UpperCamelCase__	)
																					lowerCamelCase__   :      Union[str, Any]			    =			zipfile.ZipFile(UpperCamelCase__     ,				"""r"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			archive.open("""messages.json"""	).read().decode("""utf-8"""	)
														lowerCamelCase__   :      Optional[Any]			    =			log.split("""\n"""	)[:-1]
														self.assertEqual(self.get_value_from_key(UpperCamelCase__     ,				"""num_iterations"""     ,				UpperCamelCase__	)     ,				12	)
														self.assertEqual(self.get_value_from_key(UpperCamelCase__     ,				"""learning_rate"""     ,				UpperCamelCase__	)     ,				0.01	)
														self.assertEqual(self.get_value_from_key(UpperCamelCase__     ,				"""some_boolean"""     ,				UpperCamelCase__	)     ,				UpperCamelCase__	)
														self.assertEqual(self.get_value_from_key(UpperCamelCase__     ,				"""some_string"""     ,				UpperCamelCase__	)     ,				"""some_value"""	)




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



class _lowercase      (							_lowercase						):
							a							 =			[
							    """total_loss""",
							    """iteration""",
							    """my_text""",
							    """learning_rate""",
							    """num_iterations""",
							    """some_boolean""",
							    """some_string""",
							]

							a							 =			"""my_custom_tracker"""
							a							 =			False




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




							@property
							def 						lowerCamelCase_							(				self:							Any	):
														return self.writer




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							dict	):
														logger.info("""Call init"""	)
														self.writer.writerow(UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Any     ,				UpperCamelCase__:							dict     ,				UpperCamelCase__:							Optional[int]	):
														logger.info("""Call log"""	)
														self.writer.writerow(UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							int	):
														self.f.close()



class _lowercase      (							unittest.TestCase						):




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




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









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

			_A      :     str       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

