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 		_lowercase						(					_UpperCAmelCase       ):
 lowercase				     =   ['image_processor']
 lowercase				     =   'SamImageProcessor'

 def __init__( self	:							Union[str, Any]   ,      snake_case	:							Optional[int]			)   ->						List[str]:

  """simple docstring"""





  super().__init__(lowercase_			)
  UpperCamelCase_					:						str =					self.image_processor
  UpperCamelCase_					:						List[str] =					-1_0
  UpperCamelCase_					:						Optional[Any] =					self.image_processor.size['longest_edge']

 def __call__( self	:							Tuple   ,      snake_case	:							List[str]=None   ,      snake_case	:							Union[str, Any]=None   ,      snake_case	:							List[str]=None   ,      snake_case	:							str=None   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      **snake_case	:							str   ,      )   ->						BatchEncoding:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					self.image_processor(
      lowercase_   ,      return_tensors=lowercase_   ,      **lowercase_   ,      )

  # pop arguments that are not used in the foward but used nevertheless
  UpperCamelCase_					:						Union[str, Any] =					encoding_image_processor['original_sizes']

  if hasattr(lowercase_   ,      'numpy'			):  # Checks if Torch or TF tensor
   UpperCamelCase_					:						List[Any] =					original_sizes.numpy()

  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Dict =					self._check_and_preprocess_points(
      input_points=lowercase_   ,      input_labels=lowercase_   ,      input_boxes=lowercase_   ,      )

  UpperCamelCase_					:						List[str] =					self._normalize_and_convert(
      lowercase_   ,      lowercase_   ,      input_points=lowercase_   ,      input_labels=lowercase_   ,      input_boxes=lowercase_   ,      return_tensors=lowercase_   ,      )

  return encoding_image_processor

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      snake_case	:							List[str]=None   ,      snake_case	:							Dict=None   ,      snake_case	:							Optional[int]=None   ,      snake_case	:							Any="pt"   ,      )   ->						List[str]:

  """simple docstring"""





  if input_points is not None:
   if len(lowercase_			) != len(lowercase_			):
    UpperCamelCase_					:						Union[str, Any] =					[
        self._normalize_coordinates(self.target_size   ,      lowercase_   ,      original_sizes[0]			) for point in input_points
    ]
   else:
    UpperCamelCase_					:						Dict =					[
        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:
     UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					self._pad_points_and_labels(lowercase_   ,      lowercase_			)

   UpperCamelCase_					:						Optional[Any] =					np.array(lowercase_			)

  if input_labels is not None:
   UpperCamelCase_					:						Tuple =					np.array(lowercase_			)

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

  if input_boxes is not None:
   if return_tensors == "pt":
    UpperCamelCase_					:						str =					torch.from_numpy(lowercase_			)
    # boxes batch size of 1 by default
    UpperCamelCase_					:						int =					input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
   elif return_tensors == "tf":
    UpperCamelCase_					:						Union[str, Any] =					tf.convert_to_tensor(lowercase_			)
    # boxes batch size of 1 by default
    UpperCamelCase_					:						Dict =					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":
    UpperCamelCase_					:						Union[str, Any] =					torch.from_numpy(lowercase_			)
    # point batch size of 1 by default
    UpperCamelCase_					:						List[str] =					input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
   elif return_tensors == "tf":
    UpperCamelCase_					:						int =					tf.convert_to_tensor(lowercase_			)
    # point batch size of 1 by default
    UpperCamelCase_					:						int =					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":
    UpperCamelCase_					:						int =					torch.from_numpy(lowercase_			)
    # point batch size of 1 by default
    UpperCamelCase_					:						Any =					input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
   elif return_tensors == "tf":
    UpperCamelCase_					:						str =					tf.convert_to_tensor(lowercase_			)
    # point batch size of 1 by default
    UpperCamelCase_					:						Dict =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							Any   ,      snake_case	:							Tuple			)   ->						int:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							int   ,      snake_case	:							np.ndarray   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]=False			)   ->						np.ndarray:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					original_size
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					self.image_processor._get_preprocess_shape(lowercase_   ,      longest_edge=lowercase_			)
  UpperCamelCase_					:						List[Any] =					deepcopy(lowercase_			).astype(lowercase_			)

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

  UpperCamelCase_					:						Any =					coords[..., 0] * (new_w / old_w)
  UpperCamelCase_					:						Optional[int] =					coords[..., 1] * (new_h / old_h)

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

  return coords

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[Any]=None   ,      snake_case	:							str=None   ,      snake_case	:							Dict=None   ,      )   ->						Union[str, Any]:

  """simple docstring"""





  if input_points is not None:
   if hasattr(lowercase_   ,      'numpy'			):  # Checks for TF or Torch tensor
    UpperCamelCase_					:						Optional[int] =					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.'			)
   UpperCamelCase_					:						Optional[Any] =					[np.array(lowercase_			) for input_point in input_points]
  else:
   UpperCamelCase_					:						Any =					None

  if input_labels is not None:
   if hasattr(lowercase_   ,      'numpy'			):
    UpperCamelCase_					:						Dict =					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.'			)
   UpperCamelCase_					:						int =					[np.array(lowercase_			) for label in input_labels]
  else:
   UpperCamelCase_					:						List[str] =					None

  if input_boxes is not None:
   if hasattr(lowercase_   ,      'numpy'			):
    UpperCamelCase_					:						int =					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.'			)
   UpperCamelCase_					:						str =					[np.array(lowercase_			).astype(np.floataa			) for box in input_boxes]
  else:
   UpperCamelCase_					:						Any =					None

  return input_points, input_labels, input_boxes

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.image_processor.model_input_names
  return list(dict.fromkeys(lowercase_			)			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      *snake_case	:							Union[str, Any]   ,      **snake_case	:							Optional[int]			)   ->						Optional[int]:

  """simple docstring"""





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






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

