
"""simple docstring"""





import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
    from comet_ml import OfflineExperiment

if is_tensorboard_available():
    import struct

    import tensorboard.compat.proto.event_pba as event_pba

_a									=				logging.getLogger(__name__)


@require_tensorboard
class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       "test_project_with_config"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   int     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       {"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_   :   Union[str, Any]     	=       list(filter(lambda lowercase_    : x.is_file()							,  child.iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Union[str, Any]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   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
        UpperCAmelCase_   :   int     	=       list(filter(lambda lowercase_    : x.is_file()							,  Path(F"""{dirpath}/{project_name}"""    ).iterdir()    )    )[0]
        self.assertNotEqual(str(lowercase_    )							,  ""    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       "test_project_with_log"
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   str     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_    )
        accelerator.init_trackers(lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"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_   :   Optional[Any]     	=       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_   :   Any     	=       f.read()
        UpperCAmelCase_   :   List[Any]     	=       False
        while data:
           UpperCAmelCase_   :   Dict     	=       struct.unpack("Q"							,  data[:8]    )

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







  def 			UpperCamelCase__   (						self    ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       ProjectConfiguration(total_limit=30    )
     with tempfile.TemporaryDirectory() as dirpath:
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with="tensorboard"							,  project_dir=lowercase_							,  project_config=lowercase_    )




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




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """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 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = True    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       RF"""{section} ([\S\s]*?)\n\n"""
     if record:
        UpperCAmelCase_   :   Dict     	=       RF"""Record: {pattern}"""
     UpperCAmelCase_   :   Optional[int]     	=       re.findall(lowercase_							,  lowercase_    )[0]
     # A config
     if section == "config" or section == "history":
        UpperCAmelCase_   :   Any     	=       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    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       "test_project_with_config"
     UpperCAmelCase_   :   List[Any]     	=       Accelerator(log_with="wandb"    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
     UpperCAmelCase_   :   List[str]     	=       {"wandb": {"tags": ["my_tag"]}}
     accelerator.init_trackers(lowercase_							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Optional[Any]     	=       {"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_   :   Optional[int]     	=       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_   :   List[str]     	=       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_   :   Optional[int]     	=       self.parse_log(lowercase_							,  "run"							,  lowercase_    )
     self.assertEqual(logged_items["tags"]							,  "my_tag"    )

     # Actual logging
     UpperCAmelCase_   :   Union[str, 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       __a	(						self,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       run_name
   UpperCAmelCase_   :   str     	=       OfflineExperiment(project_name=__lowerCamelCase,						offline_directory=__lowerCamelCase    )
   logger.info(f"""Initialized offline CometML project {self.run_name}"""    )
   logger.info("Make sure to log any initial configurations with `self.store_init_configuration` before training!"    )


@require_comet_ml
@mock.patch.object(lowercase__							,"""__init__"""							,lowercase__ )
class A_      (unittest.TestCase ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_ = False    ):

     """simple docstring"""
     for log in log_list:
        UpperCAmelCase_   :   str     	=       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    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   str     	=       CometMLTracker("test_project_with_config"							,  lowercase_    )
        UpperCAmelCase_   :   Dict     	=       Accelerator(log_with=lowercase_    )
        UpperCAmelCase_   :   List[Any]     	=       {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
        accelerator.init_trackers(lowercase_							,  lowercase_    )
        accelerator.end_training()
        UpperCAmelCase_   :   Any     	=       os.listdir(lowercase_    )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        UpperCAmelCase_   :   List[Any]     	=       os.path.join(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       zipfile.ZipFile(lowercase_							,  "r"    )
        UpperCAmelCase_   :   List[Any]     	=       archive.open("messages.json"    ).read().decode("utf-8"    )
     UpperCAmelCase_   :   List[str]     	=       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    ):

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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  SCREAMING_SNAKE_CASE__     :		Dict    	=						"""my_custom_tracker"""
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						False







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       open(F"""{dir}/log.csv"""							,  "w+"    )
     UpperCAmelCase_   :   Optional[int]     	=       csv.DictWriter(self.f							,  fieldnames=self._col_names    )
     self.writer.writeheader()







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.writer







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.f.close()




class A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       Accelerator(log_with=lowercase_    )
        UpperCAmelCase_   :   Any     	=       {"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_   :   Dict     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   List[Any]     	=       next(lowercase_    )
           UpperCAmelCase_   :   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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     with tempfile.TemporaryDirectory() as d:
        UpperCAmelCase_   :   Any     	=       MyCustomTracker(lowercase_    )
        UpperCAmelCase_   :   Dict     	=       Accelerator(log_with=lowercase_    )
        accelerator.init_trackers("Some name"    )
        UpperCAmelCase_   :   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:
           UpperCAmelCase_   :   Optional[int]     	=       csv.DictReader(lowercase_    )
           UpperCAmelCase_   :   Any     	=       next(lowercase_    )
           UpperCAmelCase_   :   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"""
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput



class 						a_ (      nn.Module					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_=1.0   ):

       '''simple docstring'''



       super().__init__()

       lowerCamelCase__	:     Union[str, Any]								=		cut_size
       lowerCamelCase__	:     Optional[int]								=		cut_power





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		pixel_values.shape[2:4]
       lowerCamelCase__	:     str								=		min(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		min(lowerCamelCase_,	lowerCamelCase_,	self.cut_size   )
       lowerCamelCase__	:     int								=		[]
       for _ in range(lowerCamelCase_   ):
        lowerCamelCase__	:     int								=		int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
        lowerCamelCase__	:     List[str]								=		torch.randint(0,	sideX - size + 1,	()   )
        lowerCamelCase__	:     Union[str, Any]								=		torch.randint(0,	sideY - size + 1,	()   )
        lowerCamelCase__	:     Tuple								=		pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
        cutouts.append(F.adaptive_avg_poolad(lowerCamelCase_,	self.cut_size   )   )
       return torch.cat(lowerCamelCase_   )






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     List[str]								=		F.normalize(_lowerCamelCase     ,  dim=-1							)
 lowerCamelCase__	:     Optional[int]								=		F.normalize(_lowerCamelCase     ,  dim=-1							)
 return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 for param in model.parameters():
  lowerCamelCase__	:     Dict								=		value





class 						a_ (      snake_case_					):







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	):

       '''simple docstring'''



       super().__init__()
       self.register_modules(
           vae=lowerCamelCase_,	text_encoder=lowerCamelCase_,	clip_model=lowerCamelCase_,	tokenizer=lowerCamelCase_,	unet=lowerCamelCase_,	scheduler=lowerCamelCase_,	feature_extractor=lowerCamelCase_,	)

       lowerCamelCase__	:     Union[str, Any]								=		transforms.Normalize(mean=feature_extractor.image_mean,	std=feature_extractor.image_std   )
       lowerCamelCase__	:     Dict								=		(
           feature_extractor.size
           if isinstance(feature_extractor.size,	lowerCamelCase_   )
           else feature_extractor.size['shortest_edge']
       )
       lowerCamelCase__	:     Any								=		MakeCutouts(self.cut_out_size   )

       set_requires_grad(self.text_encoder,	lowerCamelCase_   )
       set_requires_grad(self.clip_model,	lowerCamelCase_   )




      def      a__				(self,	lowerCamelCase_ = "auto"   ):

       '''simple docstring'''



       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowerCamelCase__	:     Tuple								=		self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       self.enable_attention_slicing(lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.vae,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.vae,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.unet,	lowerCamelCase_   )




      def      a__				(self   ):

       '''simple docstring'''



       set_requires_grad(self.unet,	lowerCamelCase_   )




      @torch.enable_grad()
      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_=True,	):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		latents.detach().requires_grad_()

       lowerCamelCase__	:     int								=		self.scheduler.scale_model_input(lowerCamelCase_,	lowerCamelCase_   )

       # predict the noise residual
       lowerCamelCase__	:     Tuple								=		self.unet(lowerCamelCase_,	lowerCamelCase_,	encoder_hidden_states=lowerCamelCase_   ).sample

       if isinstance(self.scheduler,	(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
        lowerCamelCase__	:     int								=		self.scheduler.alphas_cumprod[timestep]
        lowerCamelCase__	:     Union[str, Any]								=		1 - alpha_prod_t
        # compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCamelCase__	:     int								=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

        lowerCamelCase__	:     Optional[int]								=		torch.sqrt(lowerCamelCase_   )
        lowerCamelCase__	:     Optional[Any]								=		pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler,	lowerCamelCase_   ):
        lowerCamelCase__	:     Optional[Any]								=		self.scheduler.sigmas[index]
        lowerCamelCase__	:     List[Any]								=		latents - sigma * noise_pred
       else:
        raise ValueError(f'''scheduler type {type(self.scheduler   )} not supported'''   )

       lowerCamelCase__	:     Tuple								=		1 / self.vae.config.scaling_factor * sample
       lowerCamelCase__	:     List[str]								=		self.vae.decode(lowerCamelCase_   ).sample
       lowerCamelCase__	:     Union[str, Any]								=		(image / 2 + 0.5).clamp(0,	1   )

       if use_cutouts:
        lowerCamelCase__	:     int								=		self.make_cutouts(lowerCamelCase_,	lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Dict								=		transforms.Resize(self.cut_out_size   )(lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		self.normalize(lowerCamelCase_   ).to(latents.dtype   )

       lowerCamelCase__	:     str								=		self.clip_model.get_image_features(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		image_embeddings_clip / image_embeddings_clip.norm(p=2,	dim=-1,	keepdim=lowerCamelCase_   )

       if use_cutouts:
        lowerCamelCase__	:     List[Any]								=		spherical_dist_loss(lowerCamelCase_,	lowerCamelCase_   )
        lowerCamelCase__	:     Dict								=		dists.view([num_cutouts, sample.shape[0], -1]   )
        lowerCamelCase__	:     List[Any]								=		dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
       else:
        lowerCamelCase__	:     str								=		spherical_dist_loss(lowerCamelCase_,	lowerCamelCase_   ).mean() * clip_guidance_scale

       lowerCamelCase__	:     Dict								=		-torch.autograd.grad(lowerCamelCase_,	lowerCamelCase_   )[0]

       if isinstance(self.scheduler,	lowerCamelCase_   ):
        lowerCamelCase__	:     Any								=		latents.detach() + grads * (sigma**2)
        lowerCamelCase__	:     Any								=		noise_pred_original
       else:
        lowerCamelCase__	:     Optional[int]								=		noise_pred_original - torch.sqrt(lowerCamelCase_   ) * grads
       return noise_pred, latents





      @torch.no_grad()
      def __call__(self,	lowerCamelCase_,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_1_2,	lowerCamelCase_ = 5_0,	lowerCamelCase_ = 7.5,	lowerCamelCase_ = 1,	lowerCamelCase_ = 0.0,	lowerCamelCase_ = 1_0_0,	lowerCamelCase_ = None,	lowerCamelCase_ = 4,	lowerCamelCase_ = True,	lowerCamelCase_ = None,	lowerCamelCase_ = None,	lowerCamelCase_ = "pil",	lowerCamelCase_ = True,	):

       '''simple docstring'''



       if isinstance(lowerCamelCase_,	lowerCamelCase_   ):
        lowerCamelCase__	:     Any								=		1
       elif isinstance(lowerCamelCase_,	lowerCamelCase_   ):
        lowerCamelCase__	:     Optional[int]								=		len(lowerCamelCase_   )
       else:
        raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_   )}'''   )

       if height % 8 != 0 or width % 8 != 0:
        raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

       # get prompt text embeddings
       lowerCamelCase__	:     List[Any]								=		self.tokenizer(
           lowerCamelCase_,	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	)
       lowerCamelCase__	:     Optional[int]								=		self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
       # duplicate text embeddings for each generation per prompt
       lowerCamelCase__	:     Optional[Any]								=		text_embeddings.repeat_interleave(lowerCamelCase_,	dim=0   )

       if clip_guidance_scale > 0:
        if clip_prompt is not None:
         lowerCamelCase__	:     str								=		self.tokenizer(
             lowerCamelCase_,	padding='max_length',	max_length=self.tokenizer.model_max_length,	truncation=lowerCamelCase_,	return_tensors='pt',	).input_ids.to(self.device   )
        else:
         lowerCamelCase__	:     List[Any]								=		text_input.input_ids.to(self.device   )
        lowerCamelCase__	:     Any								=		self.clip_model.get_text_features(lowerCamelCase_   )
        lowerCamelCase__	:     List[Any]								=		text_embeddings_clip / text_embeddings_clip.norm(p=2,	dim=-1,	keepdim=lowerCamelCase_   )
        # duplicate text embeddings clip for each generation per prompt
        lowerCamelCase__	:     Union[str, Any]								=		text_embeddings_clip.repeat_interleave(lowerCamelCase_,	dim=0   )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       lowerCamelCase__	:     Optional[Any]								=		guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        lowerCamelCase__	:     str								=		text_input.input_ids.shape[-1]
        lowerCamelCase__	:     Union[str, Any]								=		self.tokenizer([''],	padding='max_length',	max_length=lowerCamelCase_,	return_tensors='pt'   )
        lowerCamelCase__	:     Optional[int]								=		self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
        # duplicate unconditional embeddings for each generation per prompt
        lowerCamelCase__	:     Union[str, Any]								=		uncond_embeddings.repeat_interleave(lowerCamelCase_,	dim=0   )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowerCamelCase__	:     Optional[Any]								=		torch.cat([uncond_embeddings, text_embeddings]   )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       lowerCamelCase__	:     str								=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       lowerCamelCase__	:     Any								=		text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not work reproducibly on mps
         lowerCamelCase__	:     Optional[Any]								=		torch.randn(lowerCamelCase_,	generator=lowerCamelCase_,	device='cpu',	dtype=lowerCamelCase_   ).to(
             self.device   )
        else:
         lowerCamelCase__	:     Optional[Any]								=		torch.randn(lowerCamelCase_,	generator=lowerCamelCase_,	device=self.device,	dtype=lowerCamelCase_   )
       else:
        if latents.shape != latents_shape:
         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
        lowerCamelCase__	:     Dict								=		latents.to(self.device   )

       # set timesteps
       lowerCamelCase__	:     int								=		'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
       lowerCamelCase__	:     int								=		{}
       if accepts_offset:
        lowerCamelCase__	:     Optional[int]								=		1

       self.scheduler.set_timesteps(lowerCamelCase_,	**lowerCamelCase_   )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       lowerCamelCase__	:     int								=		self.scheduler.timesteps.to(self.device   )

       # scale the initial noise by the standard deviation required by the scheduler
       lowerCamelCase__	:     Tuple								=		latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       lowerCamelCase__	:     Optional[Any]								=		'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       lowerCamelCase__	:     Any								=		{}
       if accepts_eta:
        lowerCamelCase__	:     Any								=		eta

       # check if the scheduler accepts generator
       lowerCamelCase__	:     Tuple								=		'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       if accepts_generator:
        lowerCamelCase__	:     Union[str, Any]								=		generator

       for i, t in enumerate(self.progress_bar(lowerCamelCase_   )   ):
        # expand the latents if we are doing classifier free guidance
        lowerCamelCase__	:     Any								=		torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
        lowerCamelCase__	:     List[Any]								=		self.scheduler.scale_model_input(lowerCamelCase_,	lowerCamelCase_   )

        # predict the noise residual
        lowerCamelCase__	:     Tuple								=		self.unet(lowerCamelCase_,	lowerCamelCase_,	encoder_hidden_states=lowerCamelCase_   ).sample

        # perform classifier free guidance
        if do_classifier_free_guidance:
         lowerCamelCase__							,	lowerCamelCase__	:     int								=		noise_pred.chunk(2   )
         lowerCamelCase__	:     Any								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # perform clip guidance
        if clip_guidance_scale > 0:
         lowerCamelCase__	:     Optional[Any]								=		(
             text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
         )
         lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		self.cond_fn(
             lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	)

        # compute the previous noisy sample x_t -> x_t-1
        lowerCamelCase__	:     Any								=		self.scheduler.step(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	**lowerCamelCase_   ).prev_sample

       # scale and decode the image latents with vae
       lowerCamelCase__	:     Dict								=		1 / self.vae.config.scaling_factor * latents
       lowerCamelCase__	:     List[Any]								=		self.vae.decode(lowerCamelCase_   ).sample

       lowerCamelCase__	:     List[Any]								=		(image / 2 + 0.5).clamp(0,	1   )
       lowerCamelCase__	:     Any								=		image.cpu().permute(0,	2,	3,	1   ).numpy()

       if output_type == "pil":
        lowerCamelCase__	:     List[str]								=		self.numpy_to_pil(lowerCamelCase_   )

       if not return_dict:
        return (image, None)

       return StableDiffusionPipelineOutput(images=lowerCamelCase_,	nsfw_content_detected=lowerCamelCase_   )

