
import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,      a__  :  Optional[int]	,      a__  :  Optional[int]     )		->	int:
       # Initialise PyTorch model
       UpperCamelCase_							       =       FNetConfig.from_json_file(a__     )
       print(f'''Building PyTorch model from configuration: {config}'''     )
       UpperCamelCase_							       =       FNetForPreTraining(a__     )

       UpperCamelCase_							       =       restore_checkpoint(a__	,      a__     )
       UpperCamelCase_							       =       checkpoint_dict["""target"""]

       # Embeddings
       # Position IDs
       UpperCamelCase_							       =       fnet_pretraining_model.state_dict()

       UpperCamelCase_							       =       state_dict["""fnet.embeddings.position_ids"""]
       UpperCamelCase_							       =       {"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]     ).T
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]     )

       # Encoder Layers
       for layer in range(config.num_hidden_layers     ):
              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]     )
              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]     )

              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]     ).T
              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]     )

              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]     ).T
              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]     )

              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]     )
              UpperCamelCase_							       =       torch.tensor(
                  pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]     )

       # Pooler Layers
       UpperCamelCase_							       =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]     ).T
       UpperCamelCase_							       =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]     )

       # Masked LM Layers
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]     ).T
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]     )
       UpperCamelCase_							       =       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]     )

       # Seq Relationship Layers
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""classification"""]["""output_kernel"""]     )
       UpperCamelCase_							       =       torch.tensor(
           pretrained_model_params["""classification"""]["""output_bias"""]     )

       # Load State Dict
       fnet_pretraining_model.load_state_dict(a__     )

       # Save PreTrained
       print(f'''Saving pretrained model to {save_path}'''     )
       fnet_pretraining_model.save_pretrained(a__     )


if __name__ == "__main__":
      _A	  =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--fnet_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained FNet model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
      _A	  =					parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





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 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[str]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	__UpperCamelCase = False     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [images]
        UpperCamelCase_							       =       [np.array(__UpperCamelCase     ).astype(np.floataa     ) / 255.0 for image in images]
        UpperCamelCase_							       =       np.stack(__UpperCamelCase     ,	axis=0     )

        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[..., None]

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

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       images.cpu().permute(0     ,	2     ,	3     ,	1     ).float().numpy()
        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return 2.0 * images - 1.0

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return (images / 2 + 0.5).clamp(0     ,	1     )

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       image.convert("""RGB"""     )
        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if height is None:
               UpperCamelCase_							       =       image.height
        if width is None:
               UpperCamelCase_							       =       image.width

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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [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:
                      UpperCamelCase_							       =       [self.convert_to_rgb(__UpperCamelCase     ) for i in image]
               if self.config.do_resize:
                      UpperCamelCase_							       =       [self.resize(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ) for i in image]
               UpperCamelCase_							       =       self.pil_to_numpy(__UpperCamelCase     )  # to np
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )  # to pt

        elif isinstance(image[0]     ,	np.ndarray     ):
               UpperCamelCase_							       =       np.concatenate(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else np.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       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     ):
               UpperCamelCase_							       =       torch.cat(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       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]
        UpperCamelCase_							       =       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     ,	)
               UpperCamelCase_							       =       False

        if do_normalize:
               UpperCamelCase_							       =       self.normalize(__UpperCamelCase     )

        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        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"]:
               UpperCamelCase_							       =       (
                   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     )
               UpperCamelCase_							       =       """np"""

        if output_type == "latent":
               return image

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

        UpperCamelCase_							       =       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

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

        if output_type == "np":
               return image

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



class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[Any]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(image[:, :, :3]     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return image[:, :, 1] * 2**8 + image[:, :, 2]

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       images[:, :, :, 3:]
        if images.shape[-1] == 6:
               UpperCamelCase_							       =       (images_depth * 2_5_5).round().astype("""uint8"""     )
               UpperCamelCase_							       =       [
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase     )     ,	mode="""I;16"""     ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               UpperCamelCase_							       =       (images_depth * 65_535.0).astype(np.uintaa     )
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ,	mode="""I;16"""     ) for image_depth in images_depth]
        else:
               raise Exception("""Not supported"""     )

        return pil_images

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        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"]:
               UpperCamelCase_							       =       (
                   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     )
               UpperCamelCase_							       =       """np"""

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

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

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

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



