
from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase__							:							Optional[Any]													=     logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__							:							str													=     "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def 		A_	(							snake_case							:				List[str]	, snake_case							:				Dict	, snake_case							:				List[Any]=8	) ->      List[str]:



						'''simple docstring'''

						__UpperCamelCase       					=     height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__UpperCamelCase       					=     width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor




def 		A_	(							snake_case							:				Dict	, snake_case							:				List[str]=512	, snake_case							:				Optional[Any]=512	) ->      Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     pil_image.resize((w, h)	, resample=Image.BICUBIC	, reducing_gap=1	)
						__UpperCamelCase       					=     np.array(pil_image.convert('''RGB'''	)	)
						__UpperCamelCase       					=     arr.astype(np.floataa	) / 127.5 - 1
						__UpperCamelCase       					=     np.transpose(snake_case	, [2, 0, 1]	)
						__UpperCamelCase       					=     torch.from_numpy(snake_case	).unsqueeze(0	)
						return image






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		)->				Optional[Any]:





													'''simple docstring'''






													super().__init__()

													self.register_modules(
													    unet=SCREAMING_SNAKE_CASE_       ,		scheduler=SCREAMING_SNAKE_CASE_       ,		movq=SCREAMING_SNAKE_CASE_       ,		)
													__UpperCamelCase       					=     2 ** (len(self.movq.config.block_out_channels     ) - 1)



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     min(int(num_inference_steps * strength     )       ,		SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     max(num_inference_steps - init_timestep       ,		0     )
													__UpperCamelCase       					=     self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				Union[str, Any]:





													'''simple docstring'''






													if not isinstance(SCREAMING_SNAKE_CASE_       ,		(torch.Tensor, PIL.Image.Image, list)     ):
																			raise ValueError(
																			    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE_     )}"     )

													__UpperCamelCase       					=     image.to(device=SCREAMING_SNAKE_CASE_       ,		dtype=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																			__UpperCamelCase       					=     image

													else:
																			if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) and len(SCREAMING_SNAKE_CASE_     ) != batch_size:
																									raise ValueError(
																									    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_     )}, but requested an effective batch"
																									    F" size of {batch_size}. Make sure the batch size matches the length of the generators."     )

																			elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																									__UpperCamelCase       					=     [
																									    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(SCREAMING_SNAKE_CASE_     )
																									]
																									__UpperCamelCase       					=     torch.cat(SCREAMING_SNAKE_CASE_       ,		dim=0     )
																			else:
																									__UpperCamelCase       					=     self.movq.encode(SCREAMING_SNAKE_CASE_     ).latent_dist.sample(SCREAMING_SNAKE_CASE_     )

																			__UpperCamelCase       					=     self.movq.config.scaling_factor * init_latents

													__UpperCamelCase       					=     torch.cat([init_latents]       ,		dim=0     )

													__UpperCamelCase       					=     init_latents.shape
													__UpperCamelCase       					=     randn_tensor(SCREAMING_SNAKE_CASE_       ,		generator=SCREAMING_SNAKE_CASE_       ,		device=SCREAMING_SNAKE_CASE_       ,		dtype=SCREAMING_SNAKE_CASE_     )

													# get latents
													__UpperCamelCase       					=     self.scheduler.add_noise(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     init_latents

													return latents



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=0     )->				Union[str, Any]:





													'''simple docstring'''






													if is_accelerate_available():
																			from accelerate import cpu_offload
													else:
																			raise ImportError('''Please install accelerate via `pip install accelerate`'''     )

													__UpperCamelCase       					=     torch.device(F"cuda:{gpu_id}"     )

													__UpperCamelCase       					=     [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																									cpu_offload(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=0     )->				Optional[int]:





													'''simple docstring'''






													if is_accelerate_available() and is_accelerate_version('''>='''       ,		'''0.17.0.dev0'''     ):
																			from accelerate import cpu_offload_with_hook
													else:
																			raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''     )

													__UpperCamelCase       					=     torch.device(F"cuda:{gpu_id}"     )

													if self.device.type != "cpu":
																			self.to('''cpu'''       ,		silence_dtype_warnings=SCREAMING_SNAKE_CASE_     )
																			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													__UpperCamelCase       					=     None
													for cpu_offloaded_model in [self.unet, self.movq]:
																			__UpperCamelCase					,							__UpperCamelCase       					=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		prev_module_hook=SCREAMING_SNAKE_CASE_     )

													# We'll offload the last model manually.
													__UpperCamelCase       					=     hook



							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													if not hasattr(self.unet       ,		'''_hf_hook'''     ):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(SCREAMING_SNAKE_CASE_       ,		'''_hf_hook'''     )
																			    and hasattr(module._hf_hook       ,		'''execution_device'''     )
																			    and module._hf_hook.execution_device is not None
																			):
																									return torch.device(module._hf_hook.execution_device     )
													return self.device



							@torch.no_grad()
							@replace_example_docstring(SCREAMING_SNAKE_CASE_     )
							def __call__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 512       ,		SCREAMING_SNAKE_CASE_ = 512       ,		SCREAMING_SNAKE_CASE_ = 100       ,		SCREAMING_SNAKE_CASE_ = 4.0       ,		SCREAMING_SNAKE_CASE_ = 0.3       ,		SCREAMING_SNAKE_CASE_ = 1       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = "pil"       ,		SCREAMING_SNAKE_CASE_ = True       ,		)->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self._execution_device

													__UpperCamelCase       					=     guidance_scale > 1.0

													if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     torch.cat(SCREAMING_SNAKE_CASE_       ,		dim=0     )
													if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     torch.cat(SCREAMING_SNAKE_CASE_       ,		dim=0     )
													if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     torch.cat(SCREAMING_SNAKE_CASE_       ,		dim=0     )

													__UpperCamelCase       					=     image_embeds.shape[0]

													if do_classifier_free_guidance:
																			__UpperCamelCase       					=     image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_       ,		dim=0     )
																			__UpperCamelCase       					=     negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_       ,		dim=0     )
																			__UpperCamelCase       					=     hint.repeat_interleave(SCREAMING_SNAKE_CASE_       ,		dim=0     )

													__UpperCamelCase       					=     torch.cat([negative_image_embeds, image_embeds]       ,		dim=0     ).to(dtype=self.unet.dtype       ,		device=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     torch.cat([hint, hint]       ,		dim=0     ).to(dtype=self.unet.dtype       ,		device=SCREAMING_SNAKE_CASE_     )

													if not isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     [image]
													if not all(isinstance(SCREAMING_SNAKE_CASE_       ,		(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
																			raise ValueError(
																			    F"Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"     )

													__UpperCamelCase       					=     torch.cat([prepare_image(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) for i in image]       ,		dim=0     )
													__UpperCamelCase       					=     image.to(dtype=image_embeds.dtype       ,		device=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.movq.encode(SCREAMING_SNAKE_CASE_     )['''latents''']
													__UpperCamelCase       					=     latents.repeat_interleave(SCREAMING_SNAKE_CASE_       ,		dim=0     )
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,		device=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase					,							__UpperCamelCase       					=     self.get_timesteps(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     timesteps[:1].repeat(batch_size * num_images_per_prompt     )
													__UpperCamelCase					,							__UpperCamelCase       					=     downscale_height_and_width(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		self.movq_scale_factor     )
													__UpperCamelCase       					=     self.prepare_latents(
													    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		image_embeds.dtype       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_     )     ):
																			# expand the latents if we are doing classifier free guidance
																			__UpperCamelCase       					=     torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

																			__UpperCamelCase       					=     {'''image_embeds''': image_embeds, '''hint''': hint}
																			__UpperCamelCase       					=     self.unet(
																			    sample=SCREAMING_SNAKE_CASE_       ,		timestep=SCREAMING_SNAKE_CASE_       ,		encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,		added_cond_kwargs=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       ,		)[0]

																			if do_classifier_free_guidance:
																									__UpperCamelCase					,							__UpperCamelCase       					=     noise_pred.split(latents.shape[1]       ,		dim=1     )
																									__UpperCamelCase					,							__UpperCamelCase       					=     noise_pred.chunk(2     )
																									__UpperCamelCase					,							__UpperCamelCase       					=     variance_pred.chunk(2     )
																									__UpperCamelCase       					=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									__UpperCamelCase       					=     torch.cat([noise_pred, variance_pred_text]       ,		dim=1     )

																			if not (
																			    hasattr(self.scheduler.config       ,		'''variance_type'''     )
																			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																			):
																									__UpperCamelCase					,							__UpperCamelCase       					=     noise_pred.split(latents.shape[1]       ,		dim=1     )

																			# compute the previous noisy sample x_t -> x_t-1

																			__UpperCamelCase       					=     self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		generator=SCREAMING_SNAKE_CASE_       ,		)[0]

													# post-processing
													__UpperCamelCase       					=     self.movq.decode(SCREAMING_SNAKE_CASE_       ,		force_not_quantize=SCREAMING_SNAKE_CASE_     )['''sample''']

													if output_type not in ["pt", "np", "pil"]:
																			raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"     )

													if output_type in ["np", "pil"]:
																			__UpperCamelCase       					=     image * 0.5 + 0.5
																			__UpperCamelCase       					=     image.clamp(0       ,		1     )
																			__UpperCamelCase       					=     image.cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

													if output_type == "pil":
																			__UpperCamelCase       					=     self.numpy_to_pil(SCREAMING_SNAKE_CASE_     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_     )






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__							:							Optional[int]													=     logging.getLogger(__name__)






@require_tensorboard
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






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



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		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 SCREAMING_SNAKE_CASE_     : x.is_file()       ,		Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''tensor''': torch.tensor(1     )}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		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 SCREAMING_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(SCREAMING_SNAKE_CASE_       ,		'''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(SCREAMING_SNAKE_CASE_     )
																									if event.HasField('''summary'''     ):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											__UpperCamelCase       					=     True
																			self.assertTrue(SCREAMING_SNAKE_CASE_       ,		'''Converted tensor was not found in the log file!'''     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_       ,		'''Logging with `tensorboard` requires a `logging_dir`'''     ):
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     ProjectConfiguration(total_limit=30     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_       ,		project_config=SCREAMING_SNAKE_CASE_     )






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

							"""simple docstring"""






							def       A__						(					self     )->				Optional[int]:





													'''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       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = True     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     rF"{section} ([\S\s]*?)\n\n"
													if record:
																			__UpperCamelCase       					=     rF"Record: {pattern}"
													__UpperCamelCase       					=     re.findall(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )[0]
													# A config
													if section == "config" or section == "history":
																			__UpperCamelCase       					=     re.findall(r'''"([a-zA-Z0-9_.,]+)'''       ,		SCREAMING_SNAKE_CASE_     )
																			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]+)"'''       ,		SCREAMING_SNAKE_CASE_     )     )



							@skip
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__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(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
													accelerator.log(SCREAMING_SNAKE_CASE_       ,		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(SCREAMING_SNAKE_CASE_     )]       ,		env=os.environ.copy()     ).decode('''utf8'''       ,		'''ignore'''     )
																									break

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

													# Actual logging
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''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 		A_	(							self							:				str	, snake_case							:				str	, snake_case							:				str	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     run_name
						__UpperCamelCase       					=     OfflineExperiment(project_name=snake_case	, offline_directory=snake_case	)
						logger.info(f"Initialized offline CometML project {self.run_name}"	)
						logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''	)






@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE_				,						'__init__'				,						SCREAMING_SNAKE_CASE_      )
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[str]:





													'''simple docstring'''






													for log in log_list:
																			__UpperCamelCase       					=     json.loads(SCREAMING_SNAKE_CASE_     )['''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     )->				List[Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     log.split('''\n'''     )[:-1]
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''num_iterations'''       ,		SCREAMING_SNAKE_CASE_     )       ,		12     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''learning_rate'''       ,		SCREAMING_SNAKE_CASE_     )       ,		0.0_1     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_boolean'''       ,		SCREAMING_SNAKE_CASE_     )       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_       ,		'''some_string'''       ,		SCREAMING_SNAKE_CASE_     )       ,		'''some_value'''     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					[
							    'total_loss',
							    'iteration',
							    'my_text',
							    'learning_rate',
							    'num_iterations',
							    'some_boolean',
							    'some_string',
							]

							_snake_case       						=					'my_custom_tracker'
							_snake_case       						=					False



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     open(F"{dir}/log.csv"       ,		'''w+'''     )
													__UpperCamelCase       					=     csv.DictWriter(self.f       ,		fieldnames=self._col_names     )
													self.writer.writeheader()



							@property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return self.writer



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call init'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													logger.info('''Call log'''     )
													self.writer.writerow(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													self.f.close()






class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers('''Some name'''       ,		SCREAMING_SNAKE_CASE_     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''''',
																									    '''iteration''': '''''',
																									    '''my_text''': '''''',
																									    '''learning_rate''': '''0.01''',
																									    '''num_iterations''': '''12''',
																									    '''some_boolean''': '''False''',
																									    '''some_string''': '''some_value''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers('''Some name'''     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			with open(F"{d}/log.csv"       ,		'''r'''     ) as f:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''total_loss''': '''0.1''',
																									    '''iteration''': '''1''',
																									    '''my_text''': '''some_value''',
																									    '''learning_rate''': '''''',
																									    '''num_iterations''': '''''',
																									    '''some_boolean''': '''''',
																									    '''some_string''': '''''',
																									}
																									self.assertDictEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )




