from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__													=			logging.get_logger(__name__)

lowerCAmelCase__													=			{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'decision_transformer'
 UpperCAmelCase_     			= ['past_key_values']
 UpperCAmelCase_     			= {
     'max_position_embeddings': 'n_positions',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }




 def __init__(      self :  Optional[Any]      ,   lowercase__ :  Optional[Any]=17      ,   lowercase__ :  Dict=4      ,   lowercase__ :  Optional[Any]=128      ,   lowercase__ :  List[Any]=4_096      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  List[Any]=1      ,   lowercase__ :  Dict=1_024      ,   lowercase__ :  str=3      ,   lowercase__ :  Dict=1      ,   lowercase__ :  Union[str, Any]=None      ,   lowercase__ :  Tuple="relu"      ,   lowercase__ :  Dict=0.1      ,   lowercase__ :  str=0.1      ,   lowercase__ :  Any=0.1      ,   lowercase__ :  Union[str, Any]=1e-5      ,   lowercase__ :  List[str]=0.02      ,   lowercase__ :  Optional[int]=True      ,   lowercase__ :  Optional[Any]=True      ,   lowercase__ :  List[str]=50_256      ,   lowercase__ :  List[str]=50_256      ,   lowercase__ :  Dict=False      ,   lowercase__ :  Optional[Any]=False      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = state_dim
        lowerCAmelCase__					     = act_dim
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = max_ep_len
        lowerCAmelCase__					     = action_tanh
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = n_positions
        lowerCAmelCase__					     = n_layer
        lowerCAmelCase__					     = n_head
        lowerCAmelCase__					     = n_inner
        lowerCAmelCase__					     = activation_function
        lowerCAmelCase__					     = resid_pdrop
        lowerCAmelCase__					     = embd_pdrop
        lowerCAmelCase__					     = attn_pdrop
        lowerCAmelCase__					     = layer_norm_epsilon
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = scale_attn_weights
        lowerCAmelCase__					     = use_cache
        lowerCAmelCase__					     = scale_attn_by_inverse_layer_idx
        lowerCAmelCase__					     = reorder_and_upcast_attn

        lowerCAmelCase__					     = bos_token_id
        lowerCAmelCase__					     = eos_token_id

        super().__init__(bos_token_id=_a      ,   eos_token_id=_a      ,   **_a)

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_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['image_processor']
 UpperCAmelCase_     			= 'SamImageProcessor'




 def __init__(      self :  Tuple      ,   lowercase__ :  Dict):







        '''simple docstring'''



        super().__init__(lowercase__)
        lowerCAmelCase__					     = self.image_processor
        lowerCAmelCase__					     = -10
        lowerCAmelCase__					     = self.image_processor.size['longest_edge']




 def __call__(      self :  List[Any]      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor(
            lowercase__      ,   return_tensors=lowercase__      ,   **lowercase__      ,   )

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

        if hasattr(lowercase__      ,   'numpy'):  # Checks if Torch or TF tensor
               lowerCAmelCase__					     = original_sizes.numpy()

        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self._check_and_preprocess_points(
            input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   )

        lowerCAmelCase__					     = self._normalize_and_convert(
            lowercase__      ,   lowercase__      ,   input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   return_tensors=lowercase__      ,   )

        return encoding_image_processor




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[str]      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[Any]="pt"      ,   ):







        '''simple docstring'''



        if input_points is not None:
               if len(lowercase__) != len(lowercase__):
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   original_sizes[0]) for point in input_points
                      ]
               else:
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   lowercase__)
                          for point, original_size in zip(lowercase__      ,   lowercase__)
                      ]
               # 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(lowercase__      ,   lowercase__)

               lowerCAmelCase__					     = np.array(lowercase__)

        if input_labels is not None:
               lowerCAmelCase__					     = np.array(lowercase__)

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

        if input_boxes is not None:
               if return_tensors == "pt":
                      lowerCAmelCase__					     = torch.from_numpy(lowercase__)
                      # 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(lowercase__)
                      # boxes batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   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(lowercase__)
                      # 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(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   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(lowercase__)
                      # 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(lowercase__)
                      # point batch size of 1 by default
                      lowerCAmelCase__					     = tf.expand_dims(lowercase__      ,   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 :  str      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = max([point.shape[0] for point in input_points])
        lowerCAmelCase__					     = []
        for i, point in enumerate(lowercase__):
               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(lowercase__)
        lowerCAmelCase__					     = processed_input_points
        return input_points, input_labels




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  int      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  int      ,   lowercase__ :  Optional[Any]=False):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = original_size
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processor._get_preprocess_shape(lowercase__      ,   longest_edge=lowercase__)
        lowerCAmelCase__					     = deepcopy(lowercase__).astype(lowercase__)

        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 :  Dict      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  int=None      ,   ):







        '''simple docstring'''



        if input_points is not None:
               if hasattr(lowercase__      ,   'numpy'):  # Checks for TF or Torch tensor
                      lowerCAmelCase__					     = input_points.numpy().tolist()

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

        if input_labels is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_labels.numpy().tolist()

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

        if input_boxes is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_boxes.numpy().tolist()

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

        return input_points, input_labels, input_boxes




 @property
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor.model_input_names
        return list(dict.fromkeys(lowercase__))




 def 		__snake_case						(      self :  int      ,   *lowercase__ :  int      ,   **lowercase__ :  int):







        '''simple docstring'''



        return self.image_processor.post_process_masks(*lowercase__      ,   **lowercase__)
