


'''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      snake_case    (    lowerCAmelCase__			):





  """simple docstring"""




  _lowerCamelCase		    =  ["""image_processor"""]
  _lowerCamelCase		    =  """SamImageProcessor"""






  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__(__a     )
   lowerCamelCase_      =  self.image_processor
   lowerCamelCase_      =  -10
   lowerCamelCase_      =  self.image_processor.size["longest_edge"]






  def __call__(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.image_processor(
       __a			,  return_tensors=__a			,  **__a			,  )

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

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

   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  self._check_and_preprocess_points(
       input_points=__a			,  input_labels=__a			,  input_boxes=__a			,  )

   lowerCamelCase_      =  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			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase="pt"			,  ):







   """simple docstring"""
   if input_points is not None:
    if len(__a     ) != len(__a     ):
     lowerCamelCase_      =  [
         self._normalize_coordinates(self.target_size			,  __a			,  original_sizes[0]     ) for point in input_points
     ]
    else:
     lowerCamelCase_      =  [
         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:
      lowerCamelCase_ ,lowerCamelCase_      =  self._pad_points_and_labels(__a			,  __a     )

    lowerCamelCase_      =  np.array(__a     )

   if input_labels is not None:
    lowerCamelCase_      =  np.array(__a     )

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

   if input_boxes is not None:
    if return_tensors == "pt":
     lowerCamelCase_      =  torch.from_numpy(__a     )
     # boxes batch size of 1 by default
     lowerCamelCase_      =  input_boxes.unsqueeze(1     ) if len(input_boxes.shape     ) != 3 else input_boxes
    elif return_tensors == "tf":
     lowerCamelCase_      =  tf.convert_to_tensor(__a     )
     # boxes batch size of 1 by default
     lowerCamelCase_      =  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":
     lowerCamelCase_      =  torch.from_numpy(__a     )
     # point batch size of 1 by default
     lowerCamelCase_      =  input_points.unsqueeze(1     ) if len(input_points.shape     ) != 4 else input_points
    elif return_tensors == "tf":
     lowerCamelCase_      =  tf.convert_to_tensor(__a     )
     # point batch size of 1 by default
     lowerCamelCase_      =  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":
     lowerCamelCase_      =  torch.from_numpy(__a     )
     # point batch size of 1 by default
     lowerCamelCase_      =  input_labels.unsqueeze(1     ) if len(input_labels.shape     ) != 3 else input_labels
    elif return_tensors == "tf":
     lowerCamelCase_      =  tf.convert_to_tensor(__a     )
     # point batch size of 1 by default
     lowerCamelCase_      =  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			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  max([point.shape[0] for point in input_points]     )
   lowerCamelCase_      =  []
   for i, point in enumerate(__a     ):
    if point.shape[0] != expected_nb_points:
     lowerCamelCase_      =  np.concatenate(
         [point, np.zeros((expected_nb_points - point.shape[0], 2)     ) + self.point_pad_value]			,  axis=0     )
     lowerCamelCase_      =  np.append(input_labels[i]			,  [self.point_pad_value]     )
    processed_input_points.append(__a     )
   lowerCamelCase_      =  processed_input_points
   return input_points, input_labels






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  original_size
   lowerCamelCase_ ,lowerCamelCase_      =  self.image_processor._get_preprocess_shape(__a			,  longest_edge=__a     )
   lowerCamelCase_      =  deepcopy(__a     ).astype(__a     )

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

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

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

   return coords






  def   snake_case		(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  ):







   """simple docstring"""
   if input_points is not None:
    if hasattr(__a			,  "numpy"     ):  # Checks for TF or Torch tensor
     lowerCamelCase_      =  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."     )
    lowerCamelCase_      =  [np.array(__a     ) for input_point in input_points]
   else:
    lowerCamelCase_      =  None

   if input_labels is not None:
    if hasattr(__a			,  "numpy"     ):
     lowerCamelCase_      =  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."     )
    lowerCamelCase_      =  [np.array(__a     ) for label in input_labels]
   else:
    lowerCamelCase_      =  None

   if input_boxes is not None:
    if hasattr(__a			,  "numpy"     ):
     lowerCamelCase_      =  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."     )
    lowerCamelCase_      =  [np.array(__a     ).astype(np.floataa     ) for box in input_boxes]
   else:
    lowerCamelCase_      =  None

   return input_points, input_labels, input_boxes






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.image_processor.model_input_names
   return list(dict.fromkeys(__a     )     )







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.image_processor.post_process_masks(*__a			,  **__a     )







"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




