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   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						['''image_processor''']
  lowerCamelCase__     =						'''SamImageProcessor'''







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	List[str]:
        super().__init__(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.image_processor
        SCREAMING_SNAKE_CASE_          =  -10
        SCREAMING_SNAKE_CASE_          =  self.image_processor.size["longest_edge"]







  def __call__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Union[str, Any]=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Optional[Union[str, TensorType]] = None  ,     **__magic_name__ :       List[str]  ,     )  ->	BatchEncoding:
        SCREAMING_SNAKE_CASE_          =  self.image_processor(
            __magic_name__  ,     return_tensors=__magic_name__  ,     **__magic_name__  ,     )

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

        if hasattr(__magic_name__  ,     "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=__magic_name__  ,     input_labels=__magic_name__  ,     input_boxes=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  self._normalize_and_convert(
            __magic_name__  ,     __magic_name__  ,     input_points=__magic_name__  ,     input_labels=__magic_name__  ,     input_boxes=__magic_name__  ,     return_tensors=__magic_name__  ,     )

        return encoding_image_processor







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Optional[int]="pt"  ,     )  ->	int:
        if input_points is not None:
              if len(__magic_name__   ) != len(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     original_sizes[0]   ) for point in input_points
                    ]
              else:
                    SCREAMING_SNAKE_CASE_          =  [
                        self._normalize_coordinates(self.target_size  ,     __magic_name__  ,     __magic_name__   )
                        for point, original_size in zip(__magic_name__  ,     __magic_name__   )
                    ]
              # 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(__magic_name__  ,     __magic_name__   )

              SCREAMING_SNAKE_CASE_          =  np.array(__magic_name__   )

        if input_labels is not None:
              SCREAMING_SNAKE_CASE_          =  np.array(__magic_name__   )

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

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

        return encoding_image_processor







  def    __A     (							self :       str  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  max([point.shape[0] for point in input_points]   )
        SCREAMING_SNAKE_CASE_          =  []
        for i, point in enumerate(__magic_name__   ):
              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(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  processed_input_points
        return input_points, input_labels







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       np.ndarray  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       str=False   )  ->	np.ndarray:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  original_size
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor._get_preprocess_shape(__magic_name__  ,     longest_edge=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  deepcopy(__magic_name__   ).astype(__magic_name__   )

        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    __A     (							self :       str  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Tuple=None  ,     )  ->	List[Any]:
        if input_points is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):  # Checks for TF or Torch tensor
                    SCREAMING_SNAKE_CASE_          =  input_points.numpy().tolist()

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

        if input_labels is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):
                    SCREAMING_SNAKE_CASE_          =  input_labels.numpy().tolist()

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

        if input_boxes is not None:
              if hasattr(__magic_name__  ,     "numpy"   ):
                    SCREAMING_SNAKE_CASE_          =  input_boxes.numpy().tolist()

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

        return input_points, input_labels, input_boxes







  @property
  def    __A     (							self :       Optional[int]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.image_processor.model_input_names
        return list(dict.fromkeys(__magic_name__   )   )







  def    __A     (							self :       Optional[Any]  ,     *__magic_name__ :       Dict  ,     **__magic_name__ :       int   )  ->	int:
        return self.image_processor.post_process_masks(*__magic_name__  ,     **__magic_name__   )



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import jax
      import jaxlib

A     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
        super().__init__(features=__magic_name__   )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__magic_name__  ,     __magic_name__   ):
              raise ValueError(
                  F'''Expected {device} to be a `str` not {type(__magic_name__   )}, as `jaxlib.xla_extension.Device` '''
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."   )
        SCREAMING_SNAKE_CASE_          =  device if isinstance(__magic_name__  ,     __magic_name__   ) else str(jax.devices()[0]   )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()   ):
              logger.warning(
                  F'''Device with string identifier {self.device} not listed among the available '''
                  F'''devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '''
                  F'''device: {str(jax.devices()[0]   )}.'''   )
              SCREAMING_SNAKE_CASE_          =  str(jax.devices()[0]   )
        SCREAMING_SNAKE_CASE_          =  jnp_array_kwargs







  @staticmethod
  def    __A     (							)  ->	Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(__magic_name__   ): device for device in jax.devices()}







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     __magic_name__   ) and column:
              if all(
                  isinstance(__magic_name__  ,     jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                    return jnp.stack(__magic_name__  ,     axis=0   )
        return column







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     (str, bytes, type(__magic_name__   ))   ):
              return value
        elif isinstance(__magic_name__  ,     (np.character, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.character   ):
              return value.tolist()

        SCREAMING_SNAKE_CASE_          =  {}

        if isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.integer   ):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
              else:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
        elif isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.floating   ):
              SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__magic_name__  ,     PIL.Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]   ):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__magic_name__  ,     torch.Tensor   ):
                    return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
        if hasattr(__magic_name__  ,     "__array__"   ) and not isinstance(__magic_name__  ,     jax.Array   ):
              SCREAMING_SNAKE_CASE_          =  data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__magic_name__  ,     np.ndarray   ):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        elif isinstance(__magic_name__  ,     (list, tuple)   ):
              return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        return self._tensorize(__magic_name__   )







  def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
        return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
        return self.recursive_tensorize(__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
        return column







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        for column_name in batch:
              SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
        return batch


