






"""simple docstring"""
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


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







class      UpperCamelCase_			(__A	):





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

				if hasattr(scheduler.config      ,      "steps_offset"		) and scheduler.config.steps_offset != 1:
							UpperCAmelCase_	:					Optional[int]     	=   (
							    f"""The configuration file of this scheduler: {scheduler} is outdated.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"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_		)
							UpperCAmelCase_	:					str     	=   dict(scheduler.config		)
							UpperCAmelCase_	:					Tuple     	=   1
							UpperCAmelCase_	:					Optional[int]     	=   FrozenDict(lowerCAmelCase_		)

				if hasattr(scheduler.config      ,      "skip_prk_steps"		) and scheduler.config.skip_prk_steps is False:
							UpperCAmelCase_	:					Tuple     	=   (
							    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"      ,      lowerCAmelCase_      ,      standard_warn=lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   dict(scheduler.config		)
							UpperCAmelCase_	:					int     	=   True
							UpperCAmelCase_	:					Optional[Any]     	=   FrozenDict(lowerCAmelCase_		)

				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=lowerCAmelCase_      ,      segmentation_processor=lowerCAmelCase_      ,      vae=lowerCAmelCase_      ,      text_encoder=lowerCAmelCase_      ,      tokenizer=lowerCAmelCase_      ,      unet=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_      ,      safety_checker=lowerCAmelCase_      ,      feature_extractor=lowerCAmelCase_      ,      )





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     str:
				self.enable_attention_slicing(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Dict:
				if is_accelerate_available():
							from accelerate import cpu_offload
				else:
							raise ImportError("Please install accelerate via `pip install accelerate`"		)

				UpperCAmelCase_	:					Optional[int]     	=   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(lowerCAmelCase_      ,      lowerCAmelCase_		)





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

				# Run inpainting pipeline with the generated mask
				UpperCAmelCase_	:					str     	=   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=lowerCAmelCase_      ,      image=lowerCAmelCase_      ,      mask_image=lowerCAmelCase_      ,      height=lowerCAmelCase_      ,      width=lowerCAmelCase_      ,      num_inference_steps=lowerCAmelCase_      ,      guidance_scale=lowerCAmelCase_      ,      negative_prompt=lowerCAmelCase_      ,      num_images_per_prompt=lowerCAmelCase_      ,      eta=lowerCAmelCase_      ,      generator=lowerCAmelCase_      ,      latents=lowerCAmelCase_      ,      output_type=lowerCAmelCase_      ,      return_dict=lowerCAmelCase_      ,      callback=lowerCAmelCase_      ,      callback_steps=lowerCAmelCase_      ,      )








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)