




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


UpperCamelCase								=					logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					CLIPSegForImageSegmentation       ,     SCREAMING_SNAKE_CASE__					:					CLIPSegProcessor       ,     SCREAMING_SNAKE_CASE__					:					AutoencoderKL       ,     SCREAMING_SNAKE_CASE__					:					CLIPTextModel       ,     SCREAMING_SNAKE_CASE__					:					CLIPTokenizer       ,     SCREAMING_SNAKE_CASE__					:					UNetaDConditionModel       ,     SCREAMING_SNAKE_CASE__					:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,     SCREAMING_SNAKE_CASE__					:					StableDiffusionSafetyChecker       ,     SCREAMING_SNAKE_CASE__					:					CLIPImageProcessor       ,     )				->	str:
         super().__init__()

         if hasattr(scheduler.config       ,     "steps_offset"       ) and scheduler.config.steps_offset != 1:
              lowerCAmelCase__          =			(
                  f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                  f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                  "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                  " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                  " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                  " file"
              )
              deprecate("steps_offset!=1"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			dict(scheduler.config       )
              lowerCAmelCase__          =			1
              lowerCAmelCase__          =			FrozenDict(SCREAMING_SNAKE_CASE__       )

         if hasattr(scheduler.config       ,     "skip_prk_steps"       ) and scheduler.config.skip_prk_steps is False:
              lowerCAmelCase__          =			(
                  f'The configuration file of this scheduler: {scheduler} has not set the configuration'
                  " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
                  " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
                  " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
                  " Hub, it would be very nice if you could open a Pull request for the"
                  " `scheduler/scheduler_config.json` file"
              )
              deprecate("skip_prk_steps not set"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			dict(scheduler.config       )
              lowerCAmelCase__          =			True
              lowerCAmelCase__          =			FrozenDict(SCREAMING_SNAKE_CASE__       )

         if safety_checker is None:
              logger.warning(
                  f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
                  " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                  " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                  " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                  " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                  " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."       )

         self.register_modules(
             segmentation_model=SCREAMING_SNAKE_CASE__       ,     segmentation_processor=SCREAMING_SNAKE_CASE__       ,     vae=SCREAMING_SNAKE_CASE__       ,     text_encoder=SCREAMING_SNAKE_CASE__       ,     tokenizer=SCREAMING_SNAKE_CASE__       ,     unet=SCREAMING_SNAKE_CASE__       ,     scheduler=SCREAMING_SNAKE_CASE__       ,     safety_checker=SCREAMING_SNAKE_CASE__       ,     feature_extractor=SCREAMING_SNAKE_CASE__       ,     )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, int]] = "auto"       )				->	List[str]:
         if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              lowerCAmelCase__          =			self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[int]       )				->	Any:
         self.enable_attention_slicing(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Optional[int]:
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError("Please install accelerate via `pip install accelerate`"       )

         lowerCAmelCase__          =			torch.device("cuda"       )

         for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
              if cpu_offloaded_model is not None:
                   cpu_offload(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def        a				(	self					:					List[str]       )				->	List[Any]:
         if self.device != torch.device("meta"       ) or 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()
    def __call__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, List[str]]       ,     SCREAMING_SNAKE_CASE__					:					Union[torch.FloatTensor, PIL.Image.Image]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					int = 512       ,     SCREAMING_SNAKE_CASE__					:					int = 512       ,     SCREAMING_SNAKE_CASE__					:					int = 50       ,     SCREAMING_SNAKE_CASE__					:					float = 7.5       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, List[str]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = 1       ,     SCREAMING_SNAKE_CASE__					:					float = 0.0       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = "pil"       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,     SCREAMING_SNAKE_CASE__					:					int = 1       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	Optional[Any]:
         lowerCAmelCase__          =			self.segmentation_processor(
             text=[text]       ,     images=[image]       ,     padding="max_length"       ,     return_tensors="pt"       ).to(self.device       )
         lowerCAmelCase__          =			self.segmentation_model(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
         lowerCAmelCase__          =			self.numpy_to_pil(SCREAMING_SNAKE_CASE__       )[0].resize(image.size       )

         # Run inpainting pipeline with the generated mask
         lowerCAmelCase__          =			StableDiffusionInpaintPipeline(
             vae=self.vae       ,     text_encoder=self.text_encoder       ,     tokenizer=self.tokenizer       ,     unet=self.unet       ,     scheduler=self.scheduler       ,     safety_checker=self.safety_checker       ,     feature_extractor=self.feature_extractor       ,     )
         return inpainting_pipeline(
             prompt=SCREAMING_SNAKE_CASE__       ,     image=SCREAMING_SNAKE_CASE__       ,     mask_image=SCREAMING_SNAKE_CASE__       ,     height=SCREAMING_SNAKE_CASE__       ,     width=SCREAMING_SNAKE_CASE__       ,     num_inference_steps=SCREAMING_SNAKE_CASE__       ,     guidance_scale=SCREAMING_SNAKE_CASE__       ,     negative_prompt=SCREAMING_SNAKE_CASE__       ,     num_images_per_prompt=SCREAMING_SNAKE_CASE__       ,     eta=SCREAMING_SNAKE_CASE__       ,     generator=SCREAMING_SNAKE_CASE__       ,     latents=SCREAMING_SNAKE_CASE__       ,     output_type=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     callback=SCREAMING_SNAKE_CASE__       ,     callback_steps=SCREAMING_SNAKE_CASE__       ,     )






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Can't do half precision"       )
    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ,     log_print=SCREAMING_SNAKE_CASE__       ,     trace_memory_line_by_line=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			benchmark.run()
              _check_summary_is_not_empty(result.inference_summary       )
              _check_summary_is_not_empty(result.train_summary       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
