






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				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__()

							if hasattr(scheduler.config    ,		'steps_offset'       ) and scheduler.config.steps_offset != 1:
												UpperCAmelCase_   :				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												    f"The configuration file of this scheduler: {scheduler} is outdated.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'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase       )
												UpperCAmelCase_   :				Union[str, Any]						=						dict(scheduler.config       )
												UpperCAmelCase_   :				List[str]						=						1
												UpperCAmelCase_   :				str						=						FrozenDict(_UpperCamelCase       )

							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'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase       )
												UpperCAmelCase_   :				Any						=						dict(scheduler.config       )
												UpperCAmelCase_   :				Optional[int]						=						True
												UpperCAmelCase_   :				int						=						FrozenDict(_UpperCamelCase       )

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



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



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							self.enable_attention_slicing(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'       )

							UpperCAmelCase_   :				Dict						=						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(_UpperCamelCase    ,		_UpperCamelCase       )



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							if self.device != torch.device('meta'       ) or not hasattr(self.unet    ,		'_hf_hook'       ):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(_UpperCamelCase    ,		'_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    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = 5_1_2    ,		_UpperCamelCase = 5_1_2    ,		_UpperCamelCase = 5_0    ,		_UpperCamelCase = 7.5    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = 0.0    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		**_UpperCamelCase    ,		)     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						self.segmentation_processor(
							    text=[text]    ,		images=[image]    ,		padding='max_length'    ,		return_tensors='pt'       ).to(self.device       )
							UpperCAmelCase_   :				int						=						self.segmentation_model(**_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
							UpperCAmelCase_   :				Any						=						self.numpy_to_pil(_UpperCamelCase       )[0].resize(image.size       )

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














import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(__snake_case					,	__snake_case					,	__snake_case  )

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(__snake_case  )  )
					torch.save(model.state_dict()					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						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(
		    '--rembert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained RemBERT 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.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






