



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 MobileViTImageProcessor





class 		UpperCAmelCase						(						unittest.TestCase				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=18       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     ):
         _lowerCAmelCase 			=  size if size is not None else {'''shortest_edge''': 20}
         _lowerCAmelCase 			=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  image_size
         _lowerCAmelCase 			=  min_resolution
         _lowerCAmelCase 			=  max_resolution
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_center_crop
         _lowerCAmelCase 			=  crop_size
         _lowerCAmelCase 			=  do_flip_channel_order





   def 	__lowerCAmelCase							(	self			):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }






@require_torch
@require_vision
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     MobileViTImageProcessor if is_vision_available() else None





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MobileViTImageProcessingTester(self			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_resize'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''size'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_center_crop'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''center_crop'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_flip_channel_order'''			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 20}			)
         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       ,     {'''shortest_edge''': 42}			)
         self.assertEqual(image_processor.crop_size       ,     {'''height''': 84, '''width''': 84}			)





   def 	__lowerCAmelCase							(	self			):
         pass





   def 	__lowerCAmelCase							(	self			):
         # 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=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     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(_lowerCAmelCase       ,     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 	__lowerCAmelCase							(	self			):
         # 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=_lowerCAmelCase       ,     numpify=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     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(_lowerCAmelCase       ,     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 	__lowerCAmelCase							(	self			):
         # 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=_lowerCAmelCase       ,     torchify=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     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(_lowerCAmelCase       ,     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 ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

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


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