
"""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

lowercase		:    str			=       logging.getLogger(__name__)




@require_tensorboard
class  _snake_case	(    unittest.TestCase							):






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       """test_project_with_config"""
												with tempfile.TemporaryDirectory() as dirpath:
																	__a:    Optional[int]		     =       Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase_     )
																	__a:    int		     =       {"""num_iterations""": 1_2, """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("""*/**"""     ):
																						__a:    Any		     =       list(filter(lambda snake_case__     : x.is_file()    ,			child.iterdir()     )     )[0]
																	self.assertNotEqual(str(lowercase_     )    ,			""""""     )






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Dict		     =       """test_project_with_log"""
												with tempfile.TemporaryDirectory() as dirpath:
																	__a:    Tuple		     =       Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase_     )
																	accelerator.init_trackers(lowercase_     )
																	__a:    Optional[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
																	__a:    Dict		     =       list(filter(lambda snake_case__     : x.is_file()    ,			Path(f"""{dirpath}/{project_name}"""     ).iterdir()     )     )[0]
																	self.assertNotEqual(str(lowercase_     )    ,			""""""     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       """test_project_with_log"""
												with tempfile.TemporaryDirectory() as dirpath:
																	__a:    int		     =       Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase_     )
																	accelerator.init_trackers(lowercase_     )
																	__a:    Tuple		     =       {"""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
																	__a:    Dict		     =       list(filter(lambda snake_case__     : 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:
																						__a:    Optional[int]		     =       f.read()
																	__a:    Any		     =       False
																	while data:
																						__a:    Tuple		     =       struct.unpack("""Q"""    ,			data[:8]     )

																						__a:    Dict		     =       data[1_2 : 1_2 + int(header[0]     )]  # 8+4
																						__a:    Union[str, Any]		     =       data[1_2 + int(header[0]     ) + 4 :]
																						__a:    Union[str, 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":
																																					__a:    Dict		     =       True
																	self.assertTrue(lowercase_    ,			"""Converted tensor was not found in the log file!"""     )






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



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






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												__a:    Tuple		     =       ProjectConfiguration(total_limit=3_0     )
												with tempfile.TemporaryDirectory() as dirpath:
																	__a:    int		     =       Accelerator(log_with="""tensorboard"""    ,			project_dir=lowercase_    ,			project_config=lowercase_     )






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






							def 				__lowercase	(       self    :      Dict     ):






												"""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 				__lowercase	(       snake_case__    :      str    ,			snake_case__    :      str    ,			snake_case__    :      bool = True     ):






												"""simple docstring"""



												__a:    int		     =       Rf"""{section} ([\S\s]*?)\n\n"""
												if record:
																	__a:    Optional[Any]		     =       Rf"""Record: {pattern}"""
												__a:    Any		     =       re.findall(lowercase_    ,			lowercase_     )[0]
												# A config
												if section == "config" or section == "history":
																	__a:    str		     =       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 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    List[Any]		     =       """test_project_with_config"""
												__a:    Dict		     =       Accelerator(log_with="""wandb"""     )
												__a:    int		     =       {"""num_iterations""": 1_2, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
												__a:    Optional[int]		     =       {"""wandb""": {"""tags""": ["""my_tag"""]}}
												accelerator.init_trackers(lowercase_    ,			lowercase_    ,			lowercase_     )
												__a:    Tuple		     =       {"""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":
																						__a:    List[str]		     =       subprocess.check_output(
																						    ["""wandb""", """sync""", """--view""", """--verbose""", str(lowercase_     )]    ,			env=os.environ.copy()     ).decode("""utf8"""    ,			"""ignore"""     )
																						break

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

												# Actual logging
												__a:    Optional[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      lowerCamelCase_     (						self						,					lowerCamelCase_						,					lowerCamelCase_			)  ->      Tuple:


					'''simple docstring'''
					__a:    Optional[int]		     =       run_name
					__a:    int		     =       OfflineExperiment(project_name=lowercase_						,					offline_directory=lowercase_			)
					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  _snake_case	(    unittest.TestCase							):






							@staticmethod
							def 				__lowercase	(       snake_case__    :      Union[str, Any]    ,			snake_case__    :      str    ,			snake_case__    :      bool = False     ):






												"""simple docstring"""



												for log in log_list:
																	__a:    Optional[Any]		     =       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 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												with tempfile.TemporaryDirectory() as d:
																	__a:    int		     =       CometMLTracker("""test_project_with_config"""    ,			lowercase_     )
																	__a:    Optional[Any]		     =       Accelerator(log_with=lowercase_     )
																	__a:    Dict		     =       {"""num_iterations""": 1_2, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
																	accelerator.init_trackers(lowercase_    ,			lowercase_     )
																	accelerator.end_training()
																	__a:    Any		     =       os.listdir(lowercase_     )[0]  # Comet is nice, it's just a zip file here
																	# We parse the raw logs
																	__a:    Dict		     =       os.path.join(lowercase_    ,			lowercase_     )
																	__a:    Optional[int]		     =       zipfile.ZipFile(lowercase_    ,			"""r"""     )
																	__a:    str		     =       archive.open("""messages.json"""     ).read().decode("""utf-8"""     )
												__a:    List[Any]		     =       log.split("""\n"""     )[:-1]
												self.assertEqual(self.get_value_from_key(lowercase_    ,			"""num_iterations"""    ,			lowercase_     )    ,			1_2     )
												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 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



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

							SCREAMING_SNAKE_CASE		  =    '''my_custom_tracker'''
							SCREAMING_SNAKE_CASE		  =    False






							def __init__(       self    :      Optional[int]    ,			snake_case__    :      str     ):






												"""simple docstring"""



												__a:    Any		     =       open(f"""{dir}/log.csv"""    ,			"""w+"""     )
												__a:    Tuple		     =       csv.DictWriter(self.f    ,			fieldnames=self._col_names     )
												self.writer.writeheader()






							@property
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												return self.writer






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      dict     ):






												"""simple docstring"""



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






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      dict    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



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






							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												self.f.close()






class  _snake_case	(    unittest.TestCase							):






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												with tempfile.TemporaryDirectory() as d:
																	__a:    Optional[int]		     =       MyCustomTracker(lowercase_     )
																	__a:    Any		     =       Accelerator(log_with=lowercase_     )
																	__a:    Optional[Any]		     =       {"""num_iterations""": 1_2, """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:
																						__a:    str		     =       csv.DictReader(lowercase_     )
																						__a:    Dict		     =       next(lowercase_     )
																						__a:    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 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



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





"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





