






"""simple docstring"""



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple    ):





  '''simple docstring'''





  if "model" in orig_key:
    lowercase							    =       orig_key.replace('model.'		,					''    )
  if "norm1" in orig_key:
    lowercase							    =       orig_key.replace('norm1'		,					'attention.output.LayerNorm'    )
  if "norm2" in orig_key:
    lowercase							    =       orig_key.replace('norm2'		,					'output.LayerNorm'    )
  if "norm" in orig_key:
    lowercase							    =       orig_key.replace('norm'		,					'LayerNorm'    )
  if "transformer" in orig_key:
    lowercase							    =       orig_key.split('.'    )[0].split('_'    )[-1]
    lowercase							    =       orig_key.replace(f'transformer_{layer_num}'		,					f'encoder.layer.{layer_num}'    )
  if "mha.attn" in orig_key:
    lowercase							    =       orig_key.replace('mha.attn'		,					'attention.self'    )
  if "mha" in orig_key:
    lowercase							    =       orig_key.replace('mha'		,					'attention'    )
  if "W_q" in orig_key:
    lowercase							    =       orig_key.replace('W_q'		,					'self.query'    )
  if "W_k" in orig_key:
    lowercase							    =       orig_key.replace('W_k'		,					'self.key'    )
  if "W_v" in orig_key:
    lowercase							    =       orig_key.replace('W_v'		,					'self.value'    )
  if "ff1" in orig_key:
    lowercase							    =       orig_key.replace('ff1'		,					'intermediate.dense'    )
  if "ff2" in orig_key:
    lowercase							    =       orig_key.replace('ff2'		,					'output.dense'    )
  if "ff" in orig_key:
    lowercase							    =       orig_key.replace('ff'		,					'output.dense'    )
  if "mlm_class" in orig_key:
    lowercase							    =       orig_key.replace('mlm.mlm_class'		,					'cls.predictions.decoder'    )
  if "mlm" in orig_key:
    lowercase							    =       orig_key.replace('mlm'		,					'cls.predictions.transform'    )
  if "cls" not in orig_key:
    lowercase							    =       'yoso.' + orig_key

  return orig_key
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  str    ):





  '''simple docstring'''





  for key in orig_state_dict.copy().keys():
    lowercase							    =       orig_state_dict.pop(__snake_case    )

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowercase							    =       val

  lowercase							    =       orig_state_dict['cls.predictions.decoder.bias']
  lowercase							    =       torch.arange(__snake_case    ).expand((1, -1)    ) + 2

  return orig_state_dict
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Any		,					__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       torch.load(__snake_case		,					map_location='cpu'    )['model_state_dict']
  lowercase							    =       YosoConfig.from_json_file(__snake_case    )
  lowercase							    =       YosoForMaskedLM(__snake_case    )

  lowercase							    =       convert_checkpoint_helper(config.max_position_embeddings		,					__snake_case    )

  print(model.load_state_dict(__snake_case    )    )
  model.eval()
  model.save_pretrained(__snake_case    )

  print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'    )


if __name__ == "__main__":
    _UpperCamelCase		:  Union[str, Any]						=		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for YOSO model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    _UpperCamelCase		:  int						=		parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""



import unittest

import numpy as np

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

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 DPTImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							    =       size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       do_normalize
      lowercase							    =       image_mean
      lowercase							    =       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,
      }


@require_torch
@require_vision
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =DPTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       DPTImageProcessingTester(self       )







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}       )

      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2       )
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       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
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       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
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


