
"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

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

							return orig_key

def lowercase_  (   _snake_case   ,_snake_case						):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       str           =   orig_state_dict.pop(_snake_case						)

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

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   orig_state_dict["""cls.predictions.decoder.bias"""]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.arange(_snake_case						).expand((1, -1)						) + 2

							return orig_state_dict

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.load(_snake_case   ,map_location="""cpu"""						)["""model_state_dict"""]
							SCREAMING_SNAKE_CASE__						:       Dict           =   YosoConfig.from_json_file(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   YosoForMaskedLM(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       str           =   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__      :    List[Any]    							= parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''



from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Dict							     =			['image_processor']
    lowerCamelCase  :					str							     =			'SamImageProcessor'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         super().__init__(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.image_processor
         __lowerCamelCase   :	str								=		-10
         __lowerCamelCase   :	List[str]								=		self.image_processor.size['longest_edge']







    def __call__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     BatchEncoding:
         __lowerCamelCase   :	Union[str, Any]								=		self.image_processor(
             SCREAMING_SNAKE_CASE_			,     return_tensors=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

         # pop arguments that are not used in the foward but used nevertheless
         __lowerCamelCase   :	List[Any]								=		encoding_image_processor['original_sizes']

         if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):  # Checks if Torch or TF tensor
              __lowerCamelCase   :	Dict								=		original_sizes.numpy()

         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	str								=		self._check_and_preprocess_points(
             input_points=SCREAMING_SNAKE_CASE_			,     input_labels=SCREAMING_SNAKE_CASE_			,     input_boxes=SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	Tuple								=		self._normalize_and_convert(
             SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     input_points=SCREAMING_SNAKE_CASE_			,     input_labels=SCREAMING_SNAKE_CASE_			,     input_boxes=SCREAMING_SNAKE_CASE_			,     return_tensors=SCREAMING_SNAKE_CASE_			,     )

         return encoding_image_processor







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_="pt"			,     )				->     Optional[int]:
         if input_points is not None:
              if len(SCREAMING_SNAKE_CASE_	) != len(SCREAMING_SNAKE_CASE_	):
                   __lowerCamelCase   :	Union[str, Any]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     original_sizes[0]	) for point in input_points
                   ]
              else:
                   __lowerCamelCase   :	List[Any]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                       for point, original_size in zip(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                   ]
              # check that all arrays have the same shape
              if not all(point.shape == input_points[0].shape for point in input_points	):
                   if input_labels is not None:
                        __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self._pad_points_and_labels(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              __lowerCamelCase   :	Optional[int]								=		np.array(SCREAMING_SNAKE_CASE_	)

         if input_labels is not None:
              __lowerCamelCase   :	Optional[int]								=		np.array(SCREAMING_SNAKE_CASE_	)

         if input_boxes is not None:
              if len(SCREAMING_SNAKE_CASE_	) != len(SCREAMING_SNAKE_CASE_	):
                   __lowerCamelCase   :	Union[str, Any]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     original_sizes[0]			,     is_bounding_box=SCREAMING_SNAKE_CASE_	)
                       for box in input_boxes
                   ]
              else:
                   __lowerCamelCase   :	Optional[int]								=		[
                       self._normalize_coordinates(self.target_size			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     is_bounding_box=SCREAMING_SNAKE_CASE_	)
                       for box, original_size in zip(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                   ]
              __lowerCamelCase   :	Optional[int]								=		np.array(SCREAMING_SNAKE_CASE_	)

         if input_boxes is not None:
              if return_tensors == "pt":
                   __lowerCamelCase   :	Any								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # boxes batch size of 1 by default
                   __lowerCamelCase   :	Union[str, Any]								=		input_boxes.unsqueeze(1	) if len(input_boxes.shape	) != 3 else input_boxes
              elif return_tensors == "tf":
                   __lowerCamelCase   :	List[str]								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # boxes batch size of 1 by default
                   __lowerCamelCase   :	str								=		tf.expand_dims(SCREAMING_SNAKE_CASE_			,     1	) if len(input_boxes.shape	) != 3 else input_boxes
              encoding_image_processor.update({'input_boxes': input_boxes}	)
         if input_points is not None:
              if return_tensors == "pt":
                   __lowerCamelCase   :	int								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		input_points.unsqueeze(1	) if len(input_points.shape	) != 4 else input_points
              elif return_tensors == "tf":
                   __lowerCamelCase   :	Dict								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Tuple								=		tf.expand_dims(SCREAMING_SNAKE_CASE_			,     1	) if len(input_points.shape	) != 4 else input_points
              encoding_image_processor.update({'input_points': input_points}	)
         if input_labels is not None:
              if return_tensors == "pt":
                   __lowerCamelCase   :	List[Any]								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		input_labels.unsqueeze(1	) if len(input_labels.shape	) != 3 else input_labels
              elif return_tensors == "tf":
                   __lowerCamelCase   :	str								=		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_	)
                   # point batch size of 1 by default
                   __lowerCamelCase   :	Dict								=		tf.expand_dims(SCREAMING_SNAKE_CASE_			,     1	) if len(input_labels.shape	) != 3 else input_labels
              encoding_image_processor.update({'input_labels': input_labels}	)

         return encoding_image_processor







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Tuple:
         __lowerCamelCase   :	List[str]								=		max([point.shape[0] for point in input_points]	)
         __lowerCamelCase   :	Union[str, Any]								=		[]
         for i, point in enumerate(SCREAMING_SNAKE_CASE_	):
              if point.shape[0] != expected_nb_points:
                   __lowerCamelCase   :	Optional[int]								=		np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)	) + self.point_pad_value]			,     axis=0	)
                   __lowerCamelCase   :	List[Any]								=		np.append(input_labels[i]			,     [self.point_pad_value]	)
              processed_input_points.append(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		processed_input_points
         return input_points, input_labels







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=False	)				->     np.ndarray:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		original_size
         __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE_			,     longest_edge=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		deepcopy(SCREAMING_SNAKE_CASE_	).astype(SCREAMING_SNAKE_CASE_	)

         if is_bounding_box:
              __lowerCamelCase   :	Optional[int]								=		coords.reshape(-1			,     2			,     2	)

         __lowerCamelCase   :	List[Any]								=		coords[..., 0] * (new_w / old_w)
         __lowerCamelCase   :	Dict								=		coords[..., 1] * (new_h / old_h)

         if is_bounding_box:
              __lowerCamelCase   :	Tuple								=		coords.reshape(-1			,     4	)

         return coords







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     )				->     Optional[Any]:
         if input_points is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):  # Checks for TF or Torch tensor
                   __lowerCamelCase   :	List[str]								=		input_points.numpy().tolist()

              if not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) or not isinstance(input_points[0]			,     SCREAMING_SNAKE_CASE_	):
                   raise ValueError('Input points must be a list of list of floating points.'	)
              __lowerCamelCase   :	str								=		[np.array(SCREAMING_SNAKE_CASE_	) for input_point in input_points]
         else:
              __lowerCamelCase   :	Optional[Any]								=		None

         if input_labels is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):
                   __lowerCamelCase   :	Union[str, Any]								=		input_labels.numpy().tolist()

              if not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) or not isinstance(input_labels[0]			,     SCREAMING_SNAKE_CASE_	):
                   raise ValueError('Input labels must be a list of list integers.'	)
              __lowerCamelCase   :	Any								=		[np.array(SCREAMING_SNAKE_CASE_	) for label in input_labels]
         else:
              __lowerCamelCase   :	Dict								=		None

         if input_boxes is not None:
              if hasattr(SCREAMING_SNAKE_CASE_			,     'numpy'	):
                   __lowerCamelCase   :	int								=		input_boxes.numpy().tolist()

              if (
                  not isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                  or not isinstance(input_boxes[0]			,     SCREAMING_SNAKE_CASE_	)
                  or not isinstance(input_boxes[0][0]			,     SCREAMING_SNAKE_CASE_	)
              ):
                   raise ValueError('Input boxes must be a list of list of list of floating points.'	)
              __lowerCamelCase   :	List[Any]								=		[np.array(SCREAMING_SNAKE_CASE_	).astype(np.floataa	) for box in input_boxes]
         else:
              __lowerCamelCase   :	Tuple								=		None

         return input_points, input_labels, input_boxes







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.image_processor.model_input_names
         return list(dict.fromkeys(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)






