





'''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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = ['''image_processor''']
    UpperCamelCase     = '''SamImageProcessor'''


    def __init__(		self ,		A       )		->   Any:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			self.image_processor
        _SCREAMING_SNAKE_CASE									=			-10
        _SCREAMING_SNAKE_CASE									=			self.image_processor.size["""longest_edge"""]


    def __call__(		self ,		A=None ,		A=None ,		A=None ,		A=None ,		A = None ,		**A ,		)		->   BatchEncoding:
        _SCREAMING_SNAKE_CASE									=			self.image_processor(
            A ,		return_tensors=A ,		**A ,		)

        # pop arguments that are not used in the foward but used nevertheless
        _SCREAMING_SNAKE_CASE									=			encoding_image_processor["""original_sizes"""]

        if hasattr(A ,		"""numpy"""       ):  # Checks if Torch or TF tensor
            _SCREAMING_SNAKE_CASE									=			original_sizes.numpy()

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._check_and_preprocess_points(
            input_points=A ,		input_labels=A ,		input_boxes=A ,		)

        _SCREAMING_SNAKE_CASE									=			self._normalize_and_convert(
            A ,		A ,		input_points=A ,		input_labels=A ,		input_boxes=A ,		return_tensors=A ,		)

        return encoding_image_processor


    def 			snake_case_(		self ,		A ,		A ,		A=None ,		A=None ,		A=None ,		A="pt" ,		)		->   int:
        if input_points is not None:
            if len(A       ) != len(A       ):
                _SCREAMING_SNAKE_CASE									=			[
                    self._normalize_coordinates(self.target_size ,		A ,		original_sizes[0]       ) for point in input_points
                ]
            else:
                _SCREAMING_SNAKE_CASE									=			[
                    self._normalize_coordinates(self.target_size ,		A ,		A       )
                    for point, original_size in zip(A ,		A       )
                ]
            # 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:
                    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._pad_points_and_labels(A ,		A       )

            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if input_labels is not None:
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if input_boxes is not None:
            if len(A       ) != len(A       ):
                _SCREAMING_SNAKE_CASE									=			[
                    self._normalize_coordinates(self.target_size ,		A ,		original_sizes[0] ,		is_bounding_box=A       )
                    for box in input_boxes
                ]
            else:
                _SCREAMING_SNAKE_CASE									=			[
                    self._normalize_coordinates(self.target_size ,		A ,		A ,		is_bounding_box=A       )
                    for box, original_size in zip(A ,		A       )
                ]
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if input_boxes is not None:
            if return_tensors == "pt":
                _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       )
                # boxes batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			input_boxes.unsqueeze(1       ) if len(input_boxes.shape       ) != 3 else input_boxes
            elif return_tensors == "tf":
                _SCREAMING_SNAKE_CASE									=			tf.convert_to_tensor(A       )
                # boxes batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			tf.expand_dims(A ,		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":
                _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       )
                # point batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			input_points.unsqueeze(1       ) if len(input_points.shape       ) != 4 else input_points
            elif return_tensors == "tf":
                _SCREAMING_SNAKE_CASE									=			tf.convert_to_tensor(A       )
                # point batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			tf.expand_dims(A ,		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":
                _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       )
                # point batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			input_labels.unsqueeze(1       ) if len(input_labels.shape       ) != 3 else input_labels
            elif return_tensors == "tf":
                _SCREAMING_SNAKE_CASE									=			tf.convert_to_tensor(A       )
                # point batch size of 1 by default
                _SCREAMING_SNAKE_CASE									=			tf.expand_dims(A ,		1       ) if len(input_labels.shape       ) != 3 else input_labels
            encoding_image_processor.update({"""input_labels""": input_labels}       )

        return encoding_image_processor


    def 			snake_case_(		self ,		A ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			max([point.shape[0] for point in input_points]       )
        _SCREAMING_SNAKE_CASE									=			[]
        for i, point in enumerate(A       ):
            if point.shape[0] != expected_nb_points:
                _SCREAMING_SNAKE_CASE									=			np.concatenate(
                    [point, np.zeros((expected_nb_points - point.shape[0], 2)       ) + self.point_pad_value] ,		axis=0       )
                _SCREAMING_SNAKE_CASE									=			np.append(input_labels[i] ,		[self.point_pad_value]       )
            processed_input_points.append(A       )
        _SCREAMING_SNAKE_CASE									=			processed_input_points
        return input_points, input_labels


    def 			snake_case_(		self ,		A ,		A ,		A ,		A=False       )		->   np.ndarray:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			original_size
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.image_processor._get_preprocess_shape(A ,		longest_edge=A       )
        _SCREAMING_SNAKE_CASE									=			deepcopy(A       ).astype(A       )

        if is_bounding_box:
            _SCREAMING_SNAKE_CASE									=			coords.reshape(-1 ,		2 ,		2       )

        _SCREAMING_SNAKE_CASE									=			coords[..., 0] * (new_w / old_w)
        _SCREAMING_SNAKE_CASE									=			coords[..., 1] * (new_h / old_h)

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

        return coords


    def 			snake_case_(		self ,		A=None ,		A=None ,		A=None ,		)		->   List[str]:
        if input_points is not None:
            if hasattr(A ,		"""numpy"""       ):  # Checks for TF or Torch tensor
                _SCREAMING_SNAKE_CASE									=			input_points.numpy().tolist()

            if not isinstance(A ,		A       ) or not isinstance(input_points[0] ,		A       ):
                raise ValueError("""Input points must be a list of list of floating points."""       )
            _SCREAMING_SNAKE_CASE									=			[np.array(A       ) for input_point in input_points]
        else:
            _SCREAMING_SNAKE_CASE									=			None

        if input_labels is not None:
            if hasattr(A ,		"""numpy"""       ):
                _SCREAMING_SNAKE_CASE									=			input_labels.numpy().tolist()

            if not isinstance(A ,		A       ) or not isinstance(input_labels[0] ,		A       ):
                raise ValueError("""Input labels must be a list of list integers."""       )
            _SCREAMING_SNAKE_CASE									=			[np.array(A       ) for label in input_labels]
        else:
            _SCREAMING_SNAKE_CASE									=			None

        if input_boxes is not None:
            if hasattr(A ,		"""numpy"""       ):
                _SCREAMING_SNAKE_CASE									=			input_boxes.numpy().tolist()

            if (
                not isinstance(A ,		A       )
                or not isinstance(input_boxes[0] ,		A       )
                or not isinstance(input_boxes[0][0] ,		A       )
            ):
                raise ValueError("""Input boxes must be a list of list of list of floating points."""       )
            _SCREAMING_SNAKE_CASE									=			[np.array(A       ).astype(np.floataa       ) for box in input_boxes]
        else:
            _SCREAMING_SNAKE_CASE									=			None

        return input_points, input_labels, input_boxes


    @property
    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.image_processor.model_input_names
        return list(dict.fromkeys(A       )       )





    def 			snake_case_(		self ,		*A ,		**A       )		->   str:
        return self.image_processor.post_process_masks(*A ,		**A       )










'''simple docstring'''

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
      # setup instructions, if using on-demand hardware

      # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
      # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
      # Throw an error if user passes both BYO and on-demand cluster args
      # Otherwise, use default values
      lowercase_					= argparse.ArgumentParser()
      parser.add_argument("""--user""", type=str, default="""ubuntu""")
      parser.add_argument("""--host""", type=str, default="""localhost""")
      parser.add_argument("""--key_path""", type=str, default=None)
      parser.add_argument("""--instance""", type=str, default="""V100:1""")
      parser.add_argument("""--provider""", type=str, default="""cheapest""")
      parser.add_argument("""--use_spot""", type=bool, default=False)
      parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
      lowercase_				,			lowercase_					= parser.parse_known_args()
      if args.host != "localhost":
            if args.instance != "V100:1" or args.provider != "cheapest":
                  raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
            lowercase_					= rh.cluster(
                name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
            )
      else:
            lowercase_					= rh.cluster(
                name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      lowercase_					= args.example.rsplit("""/""", 1)[0]

      # Set up remote environment
      cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
      # Note transformers is copied into the home directory on the remote machine, so we can install from there
      cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
      cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

      # Run example. You can bypass the CLI wrapper and paste your own code here.
      cluster.run([f"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

      # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
      # from my_script... import train
      # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
      # launch_train_gpu = rh.function(fn=train,
      #                                system=gpu,
      #                                reqs=reqs,
      #                                name='train_bert_glue')
      #
      # We can pass in arguments just like we would to a function:
      # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
      #                  stream_logs=True)



