


"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Optional[Any]     =		CONFIG_NAME


     @register_to_config
     def __init__(   self     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 8     ,					__UpperCAmelCase = "lanczos"     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = False     ,					)							->				Optional[Any]:
          super().__init__()


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				PIL.Image.Image:
          if images.ndim == 3:
               _a     						=					images[None, ...]
          _a     						=					(images * 255).round().astype('''uint8'''				)
          if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               _a     						=					[Image.fromarray(image.squeeze()     ,					mode='''L'''				) for image in images]
          else:
               _a     						=					[Image.fromarray(__UpperCAmelCase				) for image in images]

          return pil_images


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				np.ndarray:
          if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
               _a     						=					[images]
          _a     						=					[np.array(__UpperCAmelCase				).astype(np.floataa				) / 255.0 for image in images]
          _a     						=					np.stack(__UpperCAmelCase     ,					axis=0				)

          return images


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				torch.FloatTensor:
          if images.ndim == 3:
               _a     						=					images[..., None]

          _a     						=					torch.from_numpy(images.transpose(0     ,					3     ,					1     ,					2				)				)
          return images


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				np.ndarray:
          _a     						=					images.cpu().permute(0     ,					2     ,					3     ,					1				).float().numpy()
          return images


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				Dict:
          return 2.0 * images - 1.0


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				int:
          return (images / 2 + 0.5).clamp(0     ,					1				)


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				PIL.Image.Image:
          _a     						=					image.convert('''RGB'''				)
          return image


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					)							->				PIL.Image.Image:
          if height is None:
               _a     						=					image.height
          if width is None:
               _a     						=					image.width

          _a						,   _a     						=					(
              x - x % self.config.vae_scale_factor for x in (width, height)
          )  # resize to integer multiple of vae_scale_factor
          _a     						=					image.resize((width, height)     ,					resample=PIL_INTERPOLATION[self.config.resample]				)
          return image


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					)							->				torch.Tensor:
          _a     						=					(PIL.Image.Image, np.ndarray, torch.Tensor)
          if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
               _a     						=					[image]
          elif not (isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) and all(isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) for i in image				)):
               raise ValueError(
                   F'Input is in incorrect format: {[type(__UpperCAmelCase				) for i in image]}. Currently, we only support {", ".join(__UpperCAmelCase				)}'				)

          if isinstance(image[0]     ,					PIL.Image.Image				):
               if self.config.do_convert_rgb:
                    _a     						=					[self.convert_to_rgb(__UpperCAmelCase				) for i in image]
               if self.config.do_resize:
                    _a     						=					[self.resize(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				) for i in image]
               _a     						=					self.pil_to_numpy(__UpperCAmelCase				)  # to np
               _a     						=					self.numpy_to_pt(__UpperCAmelCase				)  # to pt

          elif isinstance(image[0]     ,					np.ndarray				):
               _a     						=					np.concatenate(__UpperCAmelCase     ,					axis=0				) if image[0].ndim == 4 else np.stack(__UpperCAmelCase     ,					axis=0				)
               _a     						=					self.numpy_to_pt(__UpperCAmelCase				)
               _a						,   _a						,   _a						,   _a     						=					image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                    raise ValueError(
                        F'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                        F'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'				)

          elif isinstance(image[0]     ,					torch.Tensor				):
               _a     						=					torch.cat(__UpperCAmelCase     ,					axis=0				) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase     ,					axis=0				)
               _a						,   _a						,   _a						,   _a     						=					image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                    return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                    raise ValueError(
                        F'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                        F'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'				)

        # expected range [0,1], normalize to [-1,1]
          _a     						=					self.config.do_normalize
          if image.min() < 0:
               warnings.warn(
                   '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                   F'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'     ,					__UpperCAmelCase     ,					)
               _a     						=					False

          if do_normalize:
               _a     						=					self.normalize(__UpperCAmelCase				)

          return image





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = "pil"     ,					__UpperCAmelCase = None     ,					)							->				Dict:
          if not isinstance(__UpperCAmelCase     ,					torch.Tensor				):
               raise ValueError(
                   F'Input for postprocessing is in incorrect format: {type(__UpperCAmelCase				)}. We only support pytorch tensor'				)
          if output_type not in ["latent", "pt", "np", "pil"]:
               _a     						=					(
                   F'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                   '''`pil`, `np`, `pt`, `latent`'''
               )
               deprecate('''Unsupported output_type'''     ,					'''1.0.0'''     ,					__UpperCAmelCase     ,					standard_warn=__UpperCAmelCase				)
               _a     						=					'''np'''

          if output_type == "latent":
               return image

          if do_denormalize is None:
               _a     						=					[self.config.do_normalize] * image.shape[0]

          _a     						=					torch.stack(
              [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

          if output_type == "pt":
               return image

          _a     						=					self.pt_to_numpy(__UpperCAmelCase				)

          if output_type == "np":
               return image

          if output_type == "pil":
               return self.numpy_to_pil(__UpperCAmelCase				)







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		CONFIG_NAME


     @register_to_config
     def __init__(   self     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 8     ,					__UpperCAmelCase = "lanczos"     ,					__UpperCAmelCase = True     ,					)							->				Tuple:
          super().__init__()


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				List[str]:
          if images.ndim == 3:
               _a     						=					images[None, ...]
          _a     						=					(images * 255).round().astype('''uint8'''				)
          if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               _a     						=					[Image.fromarray(image.squeeze()     ,					mode='''L'''				) for image in images]
          else:
               _a     						=					[Image.fromarray(image[:, :, :3]				) for image in images]

          return pil_images


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				List[Any]:
          return image[:, :, 1] * 2**8 + image[:, :, 2]


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          if images.ndim == 3:
               _a     						=					images[None, ...]
          _a     						=					images[:, :, :, 3:]
          if images.shape[-1] == 6:
               _a     						=					(images_depth * 255).round().astype('''uint8'''				)
               _a     						=					[
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCAmelCase				)     ,					mode='''I;16'''				) for image_depth in images_depth
               ]
          elif images.shape[-1] == 4:
               _a     						=					(images_depth * 65535.0).astype(np.uintaa				)
               _a     						=					[Image.fromarray(__UpperCAmelCase     ,					mode='''I;16'''				) for image_depth in images_depth]
          else:
               raise Exception('''Not supported'''				)

          return pil_images





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = "pil"     ,					__UpperCAmelCase = None     ,					)							->				Optional[Any]:
          if not isinstance(__UpperCAmelCase     ,					torch.Tensor				):
               raise ValueError(
                   F'Input for postprocessing is in incorrect format: {type(__UpperCAmelCase				)}. We only support pytorch tensor'				)
          if output_type not in ["latent", "pt", "np", "pil"]:
               _a     						=					(
                   F'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                   '''`pil`, `np`, `pt`, `latent`'''
               )
               deprecate('''Unsupported output_type'''     ,					'''1.0.0'''     ,					__UpperCAmelCase     ,					standard_warn=__UpperCAmelCase				)
               _a     						=					'''np'''

          if do_denormalize is None:
               _a     						=					[self.config.do_normalize] * image.shape[0]

          _a     						=					torch.stack(
              [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

          _a     						=					self.pt_to_numpy(__UpperCAmelCase				)

          if output_type == "np":
               if image.shape[-1] == 6:
                    _a     						=					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]				) for im in image]     ,					axis=0				)
               else:
                    _a     						=					image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

          if output_type == "pil":
               return self.numpy_to_pil(__UpperCAmelCase				), self.numpy_to_depth(__UpperCAmelCase				)
          else:
               raise Exception(F'This type {output_type} is not supported'				)



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


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