



"""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 LevitImageProcessor

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def __init__(	self							:  Tuple,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Tuple=7,  lowerCamelCase							:  List[str]=3,  lowerCamelCase							:  Optional[int]=18,  lowerCamelCase							:  Any=30,  lowerCamelCase							:  int=400,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  str=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  int=[0.5, 0.5, 0.5],  lowerCamelCase							:  str=[0.5, 0.5, 0.5],  )-> Optional[int]:
								lowerCamelCase__		:		str		     =size if size is not None else {'''shortest_edge''': 18}
								lowerCamelCase__		:		List[Any]		     =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__		:		List[Any]		     =parent
								lowerCamelCase__		:		List[str]		     =batch_size
								lowerCamelCase__		:		Optional[Any]		     =num_channels
								lowerCamelCase__		:		Dict		     =image_size
								lowerCamelCase__		:		Tuple		     =min_resolution
								lowerCamelCase__		:		List[str]		     =max_resolution
								lowerCamelCase__		:		Dict		     =do_resize
								lowerCamelCase__		:		Optional[Any]		     =size
								lowerCamelCase__		:		Optional[int]		     =do_center_crop
								lowerCamelCase__		:		Optional[int]		     =crop_size
								lowerCamelCase__		:		Dict		     =do_normalize
								lowerCamelCase__		:		List[str]		     =image_mean
								lowerCamelCase__		:		str		     =image_std




					def 		snake_case	(	self							:  Optional[Any]					)-> Optional[int]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}





@require_torch
@require_vision
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							LevitImageProcessor if is_vision_available() else None



					def 		snake_case	(	self							:  List[str]					)-> Optional[Any]:
								lowerCamelCase__		:		List[str]		     =LevitImageProcessingTester(self					)



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



					def 		snake_case	(	self							:  List[Any]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[int]		     =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,  '''do_center_crop'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''size'''					)					)



					def 		snake_case	(	self							:  Optional[int]					)-> Any:
								lowerCamelCase__		:		str		     =self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size,  {'''shortest_edge''': 18}					)
								self.assertEqual(image_processor.crop_size,  {'''height''': 18, '''width''': 18}					)

								lowerCamelCase__		:		List[str]		     =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 		snake_case	(	self							:  Optional[int]					)-> List[str]:
								pass



					def 		snake_case	(	self							:  List[str]					)-> Any:
								# Initialize image_processing
								lowerCamelCase__		:		Union[str, Any]		     =self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCamelCase__		:		Dict		     =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__		:		Optional[Any]		     =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__		:		List[str]		     =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 		snake_case	(	self							:  Union[str, Any]					)-> str:
								# Initialize image_processing
								lowerCamelCase__		:		Optional[int]		     =self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCamelCase__		:		List[Any]		     =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__		:		Any		     =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__		:		int		     =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 		snake_case	(	self							:  int					)-> str:
								# Initialize image_processing
								lowerCamelCase__		:		Optional[int]		     =self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCamelCase__		:		List[str]		     =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__		:		Tuple		     =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__		:		List[Any]		     =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'''],
								    ),  )









"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if "model" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''model.'''							,		''''''     )
			if "norm1" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''norm1'''							,		'''attention.output.LayerNorm'''     )
			if "norm2" in orig_key:
						lowerCamelCase__		:		List[Any]		     =orig_key.replace('''norm2'''							,		'''output.LayerNorm'''     )
			if "norm" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''norm'''							,		'''LayerNorm'''     )
			if "transformer" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.split('''.'''     )[0].split('''_'''     )[-1]
						lowerCamelCase__		:		Dict		     =orig_key.replace(f'''transformer_{layer_num}'''							,		f'''encoder.layer.{layer_num}'''     )
			if "mha.attn" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''mha.attn'''							,		'''attention.self'''     )
			if "mha" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''mha'''							,		'''attention'''     )
			if "W_q" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''W_q'''							,		'''self.query'''     )
			if "W_k" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''W_k'''							,		'''self.key'''     )
			if "W_v" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''W_v'''							,		'''self.value'''     )
			if "ff1" in orig_key:
						lowerCamelCase__		:		Dict		     =orig_key.replace('''ff1'''							,		'''intermediate.dense'''     )
			if "ff2" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''ff2'''							,		'''output.dense'''     )
			if "ff" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''ff'''							,		'''output.dense'''     )
			if "mlm_class" in orig_key:
						lowerCamelCase__		:		Tuple		     =orig_key.replace('''mlm.mlm_class'''							,		'''cls.predictions.decoder'''     )
			if "mlm" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''mlm'''							,		'''cls.predictions.transform'''     )
			if "cls" not in orig_key:
						lowerCamelCase__		:		Optional[int]		     ='''yoso.''' + orig_key

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =convert_checkpoint_helper(config.max_position_embeddings							,		__lowerCamelCase     )

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

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


if __name__ == "__main__":
		_lowercase			:			int										=						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."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




