'''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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   ["image_processor"]
   __lowerCAmelCase         =   "SamImageProcessor"
   def __init__(      self      :			List[str], UpperCamelCase__      :			Tuple     )  ->			Tuple:
        super().__init__(UpperCamelCase__     )
        _A    		=  self.image_processor
        _A    		=  -10
        _A    		=  self.image_processor.size['longest_edge']
   def __call__(      self      :			Tuple, UpperCamelCase__      :			List[Any]=None, UpperCamelCase__      :			List[str]=None, UpperCamelCase__      :			Optional[int]=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			Optional[Union[str, TensorType]] = None, **UpperCamelCase__      :			Dict, )  ->			BatchEncoding:
        _A    		=  self.image_processor(
            UpperCamelCase__, return_tensors=UpperCamelCase__, **UpperCamelCase__, )

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

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

        _A		,       _A		,       _A    		=  self._check_and_preprocess_points(
            input_points=UpperCamelCase__, input_labels=UpperCamelCase__, input_boxes=UpperCamelCase__, )

        _A    		=  self._normalize_and_convert(
            UpperCamelCase__, UpperCamelCase__, input_points=UpperCamelCase__, input_labels=UpperCamelCase__, input_boxes=UpperCamelCase__, return_tensors=UpperCamelCase__, )

        return encoding_image_processor
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			Optional[int]=None, UpperCamelCase__      :			Any="pt", )  ->			str:
        if input_points is not None:
             if len(UpperCamelCase__     ) != len(UpperCamelCase__     ):
                  _A    		=  [
                      self._normalize_coordinates(self.target_size, UpperCamelCase__, original_sizes[0]     ) for point in input_points
                  ]
             else:
                  _A    		=  [
                      self._normalize_coordinates(self.target_size, UpperCamelCase__, UpperCamelCase__     )
                      for point, original_size in zip(UpperCamelCase__, UpperCamelCase__     )
                  ]
             # 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:
                       _A		,       _A    		=  self._pad_points_and_labels(UpperCamelCase__, UpperCamelCase__     )

             _A    		=  np.array(UpperCamelCase__     )

        if input_labels is not None:
             _A    		=  np.array(UpperCamelCase__     )

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

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

        return encoding_image_processor
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			List[str], UpperCamelCase__      :			int     )  ->			List[Any]:
        _A    		=  max([point.shape[0] for point in input_points]     )
        _A    		=  []
        for i, point in enumerate(UpperCamelCase__     ):
             if point.shape[0] != expected_nb_points:
                  _A    		=  np.concatenate(
                      [point, np.zeros((expected_nb_points - point.shape[0], 2)     ) + self.point_pad_value], axis=0     )
                  _A    		=  np.append(input_labels[i], [self.point_pad_value]     )
             processed_input_points.append(UpperCamelCase__     )
        _A    		=  processed_input_points
        return input_points, input_labels
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			int, UpperCamelCase__      :			np.ndarray, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Any=False     )  ->			np.ndarray:
        _A		,       _A    		=  original_size
        _A		,       _A    		=  self.image_processor._get_preprocess_shape(UpperCamelCase__, longest_edge=UpperCamelCase__     )
        _A    		=  deepcopy(UpperCamelCase__     ).astype(UpperCamelCase__     )

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

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

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

        return coords
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int]=None, UpperCamelCase__      :			Union[str, Any]=None, UpperCamelCase__      :			Optional[int]=None, )  ->			str:
        if input_points is not None:
             if hasattr(UpperCamelCase__, 'numpy'     ):  # Checks for TF or Torch tensor
                  _A    		=  input_points.numpy().tolist()

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

        if input_labels is not None:
             if hasattr(UpperCamelCase__, 'numpy'     ):
                  _A    		=  input_labels.numpy().tolist()

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

        if input_boxes is not None:
             if hasattr(UpperCamelCase__, 'numpy'     ):
                  _A    		=  input_boxes.numpy().tolist()

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

        return input_points, input_labels, input_boxes
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Optional[Any]:
        _A    		=  self.image_processor.model_input_names
        return list(dict.fromkeys(UpperCamelCase__     )     )

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




'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



