




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	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				["image_processor"]
    snake_case__ =				"SamImageProcessor"


    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	str:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.image_processor
         lowerCAmelCase__          =			-10
         lowerCAmelCase__          =			self.image_processor.size["longest_edge"]


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

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

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

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

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

         return encoding_image_processor


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

              lowerCAmelCase__          =			np.array(SCREAMING_SNAKE_CASE__       )

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

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

         if input_boxes is not None:
              if return_tensors == "pt":
                   lowerCAmelCase__          =			torch.from_numpy(SCREAMING_SNAKE_CASE__       )
                   # 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(SCREAMING_SNAKE_CASE__       )
                   # boxes batch size of 1 by default
                   lowerCAmelCase__          =			tf.expand_dims(SCREAMING_SNAKE_CASE__       ,     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(SCREAMING_SNAKE_CASE__       )
                   # 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(SCREAMING_SNAKE_CASE__       )
                   # point batch size of 1 by default
                   lowerCAmelCase__          =			tf.expand_dims(SCREAMING_SNAKE_CASE__       ,     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(SCREAMING_SNAKE_CASE__       )
                   # 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(SCREAMING_SNAKE_CASE__       )
                   # point batch size of 1 by default
                   lowerCAmelCase__          =			tf.expand_dims(SCREAMING_SNAKE_CASE__       ,     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					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			max([point.shape[0] for point in input_points]       )
         lowerCAmelCase__          =			[]
         for i, point in enumerate(SCREAMING_SNAKE_CASE__       ):
              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(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			processed_input_points
         return input_points, input_labels


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       )				->	np.ndarray:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			original_size
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE__       ,     longest_edge=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			deepcopy(SCREAMING_SNAKE_CASE__       ).astype(SCREAMING_SNAKE_CASE__       )

         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        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Dict=None       ,     )				->	Dict:
         if input_points is not None:
              if hasattr(SCREAMING_SNAKE_CASE__       ,     "numpy"       ):  # Checks for TF or Torch tensor
                   lowerCAmelCase__          =			input_points.numpy().tolist()

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

         if input_labels is not None:
              if hasattr(SCREAMING_SNAKE_CASE__       ,     "numpy"       ):
                   lowerCAmelCase__          =			input_labels.numpy().tolist()

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

         if input_boxes is not None:
              if hasattr(SCREAMING_SNAKE_CASE__       ,     "numpy"       ):
                   lowerCAmelCase__          =			input_boxes.numpy().tolist()

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

         return input_points, input_labels, input_boxes


    @property
    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			self.image_processor.model_input_names
         return list(dict.fromkeys(SCREAMING_SNAKE_CASE__       )       )


    def        a				(	self					:					Tuple       ,     *SCREAMING_SNAKE_CASE__					:					Dict       ,     **SCREAMING_SNAKE_CASE__					:					List[str]       )				->	int:
         return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

         lowerCAmelCase__          =			BlipImageProcessor()
         lowerCAmelCase__          =			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"       )

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta       )]

         lowerCAmelCase__          =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__       ,     0       ,     -1       )       ) for x in image_inputs]

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         lowerCAmelCase__          =			self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"       )
         lowerCAmelCase__          =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         lowerCAmelCase__          =			BlipaProcessor.from_pretrained(
             self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE__       )

         self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			tokenizer(SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


    def        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )

         # test if it raises when no input is passed
         with pytest.raises(SCREAMING_SNAKE_CASE__       ):
              processor()


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"
         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()       )       ,     ["pixel_values", "input_ids", "attention_mask"]       )
