'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       RemBertConfig.from_json_file(__UpperCamelCase		)
     print('Building PyTorch model from configuration: {}'.format(str(__UpperCamelCase		)		)		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       RemBertModel(__UpperCamelCase		)

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

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


if __name__ == "__main__":
 _lowerCAmelCase  :List[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(
     """--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."""
 )
 _lowerCAmelCase  :int  				=    parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     warnings.warn(
         'The preprocess method is deprecated and will be removed in a future version. Please'
         ' use VaeImageProcessor.preprocess instead'       ,      a_       ,      )
     if isinstance(a_       ,      torch.Tensor		):
          return image
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			str    	=       [image]

     if isinstance(image[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       image[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          SCREAMING_SNAKE_CASE					:			Any    	=       [np.array(i.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       np.array(a_		).astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Tuple    	=       image.transpose(0       ,      3       ,      1       ,      2		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       2.0 * image - 1.0
          SCREAMING_SNAKE_CASE					:			int    	=       torch.from_numpy(a_		)
     elif isinstance(image[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat(a_       ,      dim=0		)
     return image


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

     if isinstance(mask[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          SCREAMING_SNAKE_CASE					:			Tuple    	=       [np.array(m.convert('L'		).resize((w, h)       ,      resample=PIL_INTERPOLATION['nearest']		)		)[None, :] for m in mask]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask.astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1
          SCREAMING_SNAKE_CASE					:			str    	=       torch.from_numpy(a_		)
     elif isinstance(mask[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.cat(a_       ,      dim=0		)
     return mask






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       original_image.shape[0]

         # sample gaussian noise to begin the loop
         if isinstance(lowercase__    ,							lowercase__ ) and len(lowercase__ ) != batch_size:
              raise ValueError(
                  F"""You have passed a list of generators of length {len(lowercase__ )}, but requested an effective batch"""
                  F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       generator[0] if isinstance(lowercase__    ,							lowercase__ ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
              if t < t_last:
                   # predict the noise residual
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



