



"""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

snake_case_								=			logging.getLogger(__name__)







@require_tensorboard
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     int:
												UpperCAmelCase			 =					'test_project_with_config'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, '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('*/**'   ):
																								UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    child.iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase_   )  ,    ''   )






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






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase			 =					'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'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
																		UpperCAmelCase			 =					list(filter(lambda lowercase_   : 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:
																								UpperCAmelCase			 =					f.read()
																		UpperCAmelCase			 =					False
																		while data:
																								UpperCAmelCase			 =					struct.unpack('Q'  ,    data[:8]   )

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






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     int:
												with self.assertRaisesRegex(lowercase_  ,    'Logging with `tensorboard` requires a `logging_dir`'   ):
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					ProjectConfiguration(total_limit=30   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_  ,    project_config=lowercase_   )







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



						"""simple docstring"""






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

        # Check HPS through careful parsing and cleaning
												UpperCAmelCase			 =					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
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'run'  ,    lowercase_   )
												self.assertEqual(logged_items['tags']  ,    'my_tag'   )

												# Actual logging
												UpperCAmelCase			 =					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						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					run_name
						UpperCAmelCase			 =					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  A_     (	unittest.TestCase							):



						"""simple docstring"""






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



						"""simple docstring"""
						__UpperCamelCase													=				[
						    """total_loss""",
						    """iteration""",
						    """my_text""",
						    """learning_rate""",
						    """num_iterations""",
						    """some_boolean""",
						    """some_string""",
						]

						__UpperCamelCase													=				"""my_custom_tracker"""
						__UpperCamelCase													=				False






						def __init__(   self					:Optional[int]  ,    lowercase_					:str   )							->     Optional[int]:
												UpperCAmelCase			 =					open(f"""{dir}/log.csv"""  ,    'w+'   )
												UpperCAmelCase			 =					csv.DictWriter(self.f  ,    fieldnames=self._col_names   )
												self.writer.writeheader()






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												return self.writer






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:dict   )							->     Optional[int]:
												logger.info('Call init'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:dict  ,    lowercase_					:Optional[int]   )							->     Optional[int]:
												logger.info('Call log'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												self.f.close()







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, '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:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '',
																								    'iteration': '',
																								    'my_text': '',
																								    'learning_rate': '0.01',
																								    'num_iterations': '12',
																								    'some_boolean': 'False',
																								    'some_string': 'some_value',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Union[str, Any]:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		accelerator.init_trackers('Some name'   )
																		UpperCAmelCase			 =					{'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:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '0.1',
																								    'iteration': '1',
																								    'my_text': 'some_value',
																								    'learning_rate': '',
																								    'num_iterations': '',
																								    'some_boolean': '',
																								    'some_string': '',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )










"""simple docstring"""


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    x - x % self.config.vae_scale_factor for x in (width, height)
												)  # resize to integer multiple of vae_scale_factor
												UpperCAmelCase			 =					image.resize((width, height)  ,    resample=PIL_INTERPOLATION[self.config.resample]   )
												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		if self.config.do_convert_rgb:
																								UpperCAmelCase			 =					[self.convert_to_rgb(lowercase_   ) for i in image]
																		if self.config.do_resize:
																								UpperCAmelCase			 =					[self.resize(lowercase_  ,    lowercase_  ,    lowercase_   ) for i in image]
																		UpperCAmelCase			 =					self.pil_to_numpy(lowercase_   )  # to np
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )  # to pt

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape
																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

												elif isinstance(image[0]  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape

																		# don't need any preprocess if the image is latents
																		if channel == 4:
																								return image

																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

        # expected range [0,1], normalize to [-1,1]
												UpperCAmelCase			 =					self.config.do_normalize
												if image.min() < 0:
																		warnings.warn(
																		    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																		    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												if output_type == "pt":
																		return image

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		if image.shape[-1] == 6:
																								UpperCAmelCase			 =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]  ,    axis=0   )
																		else:
																								UpperCAmelCase			 =					image[:, :, :, 3:]
																		return image[:, :, :, :3], image_depth

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   ), self.numpy_to_depth(lowercase_   )
												else:
																		raise Exception(f"""This type {output_type} is not supported"""   )





