




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, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import OwlViTImageProcessor





class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    def __init__(       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     List[str]=7   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=3   ,	SCREAMING_SNAKE_CASE__     :     int=18   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=30   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=400   ,	SCREAMING_SNAKE_CASE__     :     Tuple=True   ,	SCREAMING_SNAKE_CASE__     :     Tuple=None   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=True   ,	SCREAMING_SNAKE_CASE__     :     str=None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=True   ,	SCREAMING_SNAKE_CASE__     :     Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,	SCREAMING_SNAKE_CASE__     :     List[str]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,	SCREAMING_SNAKE_CASE__     :     List[str]=True   ,	)      ->       List[Any]:
         lowerCAmelCase__		= parent
         lowerCAmelCase__		= batch_size
         lowerCAmelCase__		= num_channels
         lowerCAmelCase__		= image_size
         lowerCAmelCase__		= min_resolution
         lowerCAmelCase__		= max_resolution
         lowerCAmelCase__		= do_resize
         lowerCAmelCase__		= size if size is not None else {"height": 18, "width": 18}
         lowerCAmelCase__		= do_center_crop
         lowerCAmelCase__		= crop_size if crop_size is not None else {"height": 18, "width": 18}
         lowerCAmelCase__		= do_normalize
         lowerCAmelCase__		= image_mean
         lowerCAmelCase__		= image_std
         lowerCAmelCase__		= do_convert_rgb


    def    a      (       self     :     int   )      ->       int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }






@require_torch
@require_vision
class  __lowerCamelCase       (    UpperCamelCase__	,				unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      OwlViTImageProcessor if is_vision_available() else None


    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= OwlViTImageProcessingTester(self   )


    @property
    def    a      (       self     :     Optional[int]   )      ->       Union[str, Any]:
         return self.image_processor_tester.prepare_image_processor_dict()


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_resize"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"size"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_center_crop"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"center_crop"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_normalize"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"image_mean"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"image_std"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_convert_rgb"   )   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size   ,	{"height": 18, "width": 18}   )
         self.assertEqual(image_processor.crop_size   ,	{"height": 18, "width": 18}   )

         lowerCAmelCase__		= self.image_processing_class.from_dict(self.image_processor_dict   ,	size=42   ,	crop_size=84   )
         self.assertEqual(image_processor.size   ,	{"height": 42, "width": 42}   )
         self.assertEqual(image_processor.crop_size   ,	{"height": 84, "width": 84}   )


    def    a      (       self     :     int   )      ->       Tuple:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   )

         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	Image.Image   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)


    def    a      (       self     :     Any   )      ->       str:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   ,	numpify=SCREAMING_SNAKE_CASE__   )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	np.ndarray   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)


    def    a      (       self     :     Optional[Any]   )      ->       int:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   ,	torchify=SCREAMING_SNAKE_CASE__   )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,	)






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   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     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(lowerCAmelCase_			)

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


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)
