

'''simple docstring'''
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       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			Tuple:

   '''simple docstring'''





   _lowercase    :   Optional[Any]      =     FNetConfig.from_json_file(snake_case  )
   print(F'''Building PyTorch model from configuration: {config}'''  )
   _lowercase    :   Tuple      =     FNetForPreTraining(snake_case  )

   _lowercase    :   Optional[int]      =     restore_checkpoint(snake_case	,							snake_case  )
   _lowercase    :   int      =     checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   _lowercase    :   int      =     fnet_pretraining_model.state_dict()

   _lowercase    :   Union[str, Any]      =     state_dict["fnet.embeddings.position_ids"]
   _lowercase    :   Optional[Any]      =     {"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   _lowercase    :   Dict      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]  )
   _lowercase    :   Tuple      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]  )
   _lowercase    :   Union[str, Any]      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]  )
   _lowercase    :   Union[str, Any]      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]  ).T
   _lowercase    :   Union[str, Any]      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]  )
   _lowercase    :   Optional[int]      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]  )
   _lowercase    :   str      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]  )

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

      _lowercase    :   Any      =     torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["intermediate"]["kernel"]  ).T
      _lowercase    :   Union[str, Any]      =     torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["intermediate"]["bias"]  )

      _lowercase    :   Union[str, Any]      =     torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["output"]["kernel"]  ).T
      _lowercase    :   str      =     torch.tensor(
          pretrained_model_params["encoder"][F'''feed_forward_{layer}''']["output"]["bias"]  )

      _lowercase    :   Dict      =     torch.tensor(
          pretrained_model_params["encoder"][F'''encoder_{layer}''']["output_layer_norm"]["scale"]  )
      _lowercase    :   Any      =     torch.tensor(
          pretrained_model_params["encoder"][F'''encoder_{layer}''']["output_layer_norm"]["bias"]  )

   # Pooler Layers
   _lowercase    :   List[str]      =     torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]  ).T
   _lowercase    :   Optional[Any]      =     torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]  )

   # Masked LM Layers
   _lowercase    :   int      =     torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]  ).T
   _lowercase    :   List[str]      =     torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]  )
   _lowercase    :   Dict      =     torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]  )
   _lowercase    :   List[Any]      =     torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]  )
   _lowercase    :   int      =     torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]  )
   _lowercase    :   List[str]      =     torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]  )
   _lowercase    :   List[Any]      =     torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]  )

   # Seq Relationship Layers
   _lowercase    :   Optional[Any]      =     torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]  )
   _lowercase    :   Union[str, Any]      =     torch.tensor(
       pretrained_model_params["classification"]["output_bias"]  )

   # Load State Dict
   fnet_pretraining_model.load_state_dict(snake_case  )

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


if __name__ == "__main__":
    _snake_case     						=						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.')
    _snake_case     						=						parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)









'''simple docstring'''
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import TvltImageProcessor







def       _A	(			snake_case	,							snake_case=10	,							snake_case=10	,							snake_case=False	,							snake_case=False  )    ->			List[Any]:
   _lowercase    :   Dict      =     []
   for i in range(image_processor_tester.num_frames  ):
      video.append(np.random.randint(2_55	,							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
      _lowercase    :   Union[str, Any]      =     [Image.fromarray(np.moveaxis(snake_case	,							0	,							-1  )  ) for frame in video]

   if torchify:
      _lowercase    :   Any      =     [torch.from_numpy(snake_case  ) for frame in video]

   return video







def       _A	(			snake_case	,							snake_case=False	,							snake_case=False	,							snake_case=False  )    ->			Tuple:
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   _lowercase    :   Optional[Any]      =     []
   for i in range(image_processor_tester.batch_size  ):
      if equal_resolution:
         _lowercase    :   List[Any]      =     image_processor_tester.max_resolution
      else:
         _lowercase  ,   _lowercase    :   Dict      =     np.random.choice(
             np.arange(image_processor_tester.min_resolution	,							image_processor_tester.max_resolution  )	,							2  )
         _lowercase    :   List[Any]      =     prepare_video(
             image_processor_tester=snake_case	,							width=snake_case	,							height=snake_case	,							numpify=snake_case	,							torchify=snake_case	,							)
      video_inputs.append(snake_case  )

   return video_inputs


class      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=4    ,       _UpperCamelCase=18    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"shortest_edge": 18}
      _lowercase    :   Any      =     crop_size if crop_size is not None else {"height": 18, "width": 18}

      _lowercase    :   Dict      =     parent
      _lowercase    :   Optional[Any]      =     batch_size
      _lowercase    :   List[Any]      =     num_channels
      _lowercase    :   Optional[Any]      =     num_frames
      _lowercase    :   int      =     image_size
      _lowercase    :   Optional[int]      =     min_resolution
      _lowercase    :   Optional[int]      =     max_resolution
      _lowercase    :   Optional[Any]      =     do_resize
      _lowercase    :   Dict      =     size
      _lowercase    :   Any      =     do_normalize
      _lowercase    :   Tuple      =     image_mean
      _lowercase    :   Union[str, Any]      =     image_std
      _lowercase    :   Tuple      =     do_center_crop
      _lowercase    :   str      =     crop_size






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							TvltImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     TvltImageProcessorTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_center_crop"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Any      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL videos
      _lowercase    :   Any      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       Image.Image						)

      # Test not batched input
      _lowercase    :   Any      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   Union[str, Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       numpify=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       np.ndarray						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   List[Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   Optional[int]      =     prepare_video_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      for video in video_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       _UpperCamelCase						)
         self.assertIsInstance(video[0]    ,       torch.Tensor						)

      # Test not batched input
      _lowercase    :   int      =     image_processor(video_inputs[0]    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   List[Any]      =     image_processor(_UpperCamelCase    ,       return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_videos.shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,       )






