
'''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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViltImageProcessor







class 		snake_case_       (						unittest.TestCase						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase=7			,   UpperCamelCase=3			,   UpperCamelCase=18			,   UpperCamelCase=30			,   UpperCamelCase=4_00			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=2			,   UpperCamelCase=True			,   UpperCamelCase=[0.5, 0.5, 0.5]			,   UpperCamelCase=[0.5, 0.5, 0.5]			,   ):
          lowerCamelCase__				   =      size if size is not None else {"shortest_edge": 30}
          lowerCamelCase__				   =      parent
          lowerCamelCase__				   =      batch_size
          lowerCamelCase__				   =      num_channels
          lowerCamelCase__				   =      image_size
          lowerCamelCase__				   =      min_resolution
          lowerCamelCase__				   =      max_resolution
          lowerCamelCase__				   =      do_resize
          lowerCamelCase__				   =      size
          lowerCamelCase__				   =      size_divisor
          lowerCamelCase__				   =      do_normalize
          lowerCamelCase__				   =      image_mean
          lowerCamelCase__				   =      image_std







   def     __UpperCAmelCase      ( self):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase=False):
          if not batched:
                 lowerCamelCase__				   =      self.size["shortest_edge"]
                 lowerCamelCase__				   =      image_inputs[0]
                 if isinstance(_A			,   Image.Image):
                        lowerCamelCase__				   =      image.size
                 else:
                        lowerCamelCase__				   =      image.shape[1], image.shape[2]
                 lowerCamelCase__				   =      size / min(_A			,   _A)
                 if h < w:
                        lowerCamelCase__				   =      size, scale * w
                 else:
                        lowerCamelCase__				   =      scale * h, size

                 lowerCamelCase__				   =      int((13_33 / 8_00) * size)
                 if max(_A			,   _A) > max_size:
                        lowerCamelCase__				   =      max_size / max(_A			,   _A)
                        lowerCamelCase__				   =      newh * scale
                        lowerCamelCase__				   =      neww * scale

                 lowerCamelCase__				   =      int(newh + 0.5), int(neww + 0.5)
                 lowerCamelCase__				   =      (
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

          else:
                 lowerCamelCase__				   =      []
                 for image in image_inputs:
                        lowerCamelCase__				   =      self.get_expected_values([image])
                        expected_values.append((expected_height, expected_width))
                 lowerCamelCase__				   =      max(_A			,   key=lambda UpperCamelCase: item[0])[0]
                 lowerCamelCase__				   =      max(_A			,   key=lambda UpperCamelCase: item[1])[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 		snake_case_       (						A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Dict								=ViltImageProcessor if is_vision_available() else None







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ViltImageProcessingTester(self)







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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_A			,   "image_mean"))
          self.assertTrue(hasattr(_A			,   "image_std"))
          self.assertTrue(hasattr(_A			,   "do_normalize"))
          self.assertTrue(hasattr(_A			,   "do_resize"))
          self.assertTrue(hasattr(_A			,   "size"))
          self.assertTrue(hasattr(_A			,   "size_divisor"))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size			,   {"shortest_edge": 30})

          lowerCamelCase__				   =      self.image_processing_class.from_dict(self.image_processor_dict			,   size=42)
          self.assertEqual(image_processor.size			,   {"shortest_edge": 42})







   def     __UpperCAmelCase      ( self):
          pass







   def     __UpperCAmelCase      ( 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=_A)
          for image in image_inputs:
                 self.assertIsInstance(_A			,   Image.Image)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A)
          self.assertEqual(
              encoded_images.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(_A			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A			,   batched=_A)
          self.assertEqual(
              encoded_images.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )







   def     __UpperCAmelCase      ( 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=_A			,   numpify=_A)
          for image in image_inputs:
                 self.assertIsInstance(_A			,   np.ndarray)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A)
          self.assertEqual(
              encoded_images.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(_A			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A			,   batched=_A)
          self.assertEqual(
              encoded_images.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )







   def     __UpperCAmelCase      ( 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=_A			,   torchify=_A)
          for image in image_inputs:
                 self.assertIsInstance(_A			,   torch.Tensor)

          # Test not batched input
          lowerCamelCase__				   =      image_processing(image_inputs[0]			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A)
          self.assertEqual(
              encoded_images.shape			,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,   )

          # Test batched
          lowerCamelCase__				   =      image_processing(_A			,   return_tensors="pt").pixel_values

          lowerCamelCase__				   =      self.image_processor_tester.get_expected_values(_A			,   batched=_A)
          self.assertEqual(
              encoded_images.shape			,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			,   )






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)