




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
       import numpy as np
       import torch

if is_vision_available():
       from PIL import Image




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   str=False    ,     A__			:   Tuple=False    ,     A__			:   Optional[int]=False      ):





       '''simple docstring'''



       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       __lowerCamelCase  =		[]
       for i in range(image_processor_tester.batch_size      ):
              if equal_resolution:
                     __lowerCamelCase  =		__lowerCamelCase  =		image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     __lowerCamelCase  =		image_processor_tester.min_resolution
                     if getattr(A__    ,     """size_divisor"""    ,     A__      ):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            __lowerCamelCase  =		max(image_processor_tester.size_divisor    ,     A__      )
                     __lowerCamelCase,  __lowerCamelCase  =		np.random.choice(np.arange(A__    ,     image_processor_tester.max_resolution      )    ,     2      )
              image_inputs.append(
                  np.random.randint(255    ,     size=(image_processor_tester.num_channels, width, height)    ,     dtype=np.uinta      )      )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __lowerCamelCase  =		[Image.fromarray(np.moveaxis(A__    ,     0    ,     -1      )      ) for image in image_inputs]

       if torchify:
              __lowerCamelCase  =		[torch.from_numpy(A__      ) for image in image_inputs]

       return image_inputs




def    lowerCamelCase__			(		A__			:   int    ,     A__			:   Any=10    ,     A__			:   Optional[int]=10    ,     A__			:   str=False    ,     A__			:   str=False      ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       for i in range(image_processor_tester.num_frames      ):
              video.append(np.random.randint(255    ,     size=(image_processor_tester.num_channels, width, height)    ,     dtype=np.uinta      )      )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __lowerCamelCase  =		[Image.fromarray(np.moveaxis(A__    ,     0    ,     -1      )      ) for frame in video]

       if torchify:
              __lowerCamelCase  =		[torch.from_numpy(A__      ) for frame in video]

       return video




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   Tuple=False    ,     A__			:   List[Any]=False    ,     A__			:   Dict=False      ):





       '''simple docstring'''



       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       __lowerCamelCase  =		[]
       for i in range(image_processor_tester.batch_size      ):
              if equal_resolution:
                     __lowerCamelCase  =		__lowerCamelCase  =		image_processor_tester.max_resolution
              else:
                     __lowerCamelCase,  __lowerCamelCase  =		np.random.choice(
                         np.arange(image_processor_tester.min_resolution    ,     image_processor_tester.max_resolution      )    ,     2      )
                     __lowerCamelCase  =		prepare_video(
                         image_processor_tester=A__    ,     width=A__    ,     height=A__    ,     numpify=A__    ,     torchify=A__    ,     )
              video_inputs.append(A__      )

       return video_inputs





class        lowerCamelCase__:
      UpperCAmelCase__ :      List[Any]       						=  None
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		self.image_processing_class(**self.image_processor_dict  )
             __lowerCamelCase  =		json.loads(image_processor.to_json_string()  )
             for key, value in self.image_processor_dict.items():
                    self.assertEqual(obj[key]       ,			UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		self.image_processing_class(**self.image_processor_dict  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    __lowerCamelCase  =		os.path.join(UpperCamelCase_       ,			"""image_processor.json"""  )
                    image_processor_first.to_json_file(UpperCamelCase_  )
                    __lowerCamelCase  =		self.image_processing_class.from_json_file(UpperCamelCase_  )

             self.assertEqual(image_processor_second.to_dict()       ,			image_processor_first.to_dict()  )
      def      lowerCAmelCase__		(							self:					int  ):
             __lowerCamelCase  =		self.image_processing_class(**self.image_processor_dict  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    __lowerCamelCase  =		image_processor_first.save_pretrained(UpperCamelCase_  )[0]
                    check_json_file_has_correct_format(UpperCamelCase_  )
                    __lowerCamelCase  =		self.image_processing_class.from_pretrained(UpperCamelCase_  )

             self.assertEqual(image_processor_second.to_dict()       ,			image_processor_first.to_dict()  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		self.image_processing_class()
             self.assertIsNotNone(UpperCamelCase_  )




      @require_torch
      @require_vision
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             if self.test_cast_dtype is not None:
                    # Initialize image_processor
                    __lowerCamelCase  =		self.image_processing_class(**self.image_processor_dict  )

                    # create random PyTorch tensors
                    __lowerCamelCase  =		prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=UpperCamelCase_       ,			torchify=UpperCamelCase_  )

                    __lowerCamelCase  =		image_processor(UpperCamelCase_       ,			return_tensors="""pt"""  )
                    # for layoutLM compatiblity
                    self.assertEqual(encoding.pixel_values.device       ,			torch.device("""cpu"""  )  )
                    self.assertEqual(encoding.pixel_values.dtype       ,			torch.floataa  )

                    __lowerCamelCase  =		image_processor(UpperCamelCase_       ,			return_tensors="""pt"""  ).to(torch.floataa  )
                    self.assertEqual(encoding.pixel_values.device       ,			torch.device("""cpu"""  )  )
                    self.assertEqual(encoding.pixel_values.dtype       ,			torch.floataa  )

                    __lowerCamelCase  =		image_processor(UpperCamelCase_       ,			return_tensors="""pt"""  ).to("""cpu"""       ,			torch.bfloataa  )
                    self.assertEqual(encoding.pixel_values.device       ,			torch.device("""cpu"""  )  )
                    self.assertEqual(encoding.pixel_values.dtype       ,			torch.bfloataa  )

                    with self.assertRaises(UpperCamelCase_  ):
                           __lowerCamelCase  =		image_processor(UpperCamelCase_       ,			return_tensors="""pt"""  ).to(torch.bfloataa       ,			"""cpu"""  )

                    # Try with text + image feature
                    __lowerCamelCase  =		image_processor(UpperCamelCase_       ,			return_tensors="""pt"""  )
                    encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]  )}  )
                    __lowerCamelCase  =		encoding.to(torch.floataa  )

                    self.assertEqual(encoding.pixel_values.device       ,			torch.device("""cpu"""  )  )
                    self.assertEqual(encoding.pixel_values.dtype       ,			torch.floataa  )
                    self.assertEqual(encoding.input_ids.dtype       ,			torch.long  )







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__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

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

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

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

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

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

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

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

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

       # Seq Relationship Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_kernel"""]      )
       __lowerCamelCase  =		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__":
       UpperCAmelCase_   							=      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.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

