
"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       ["""image_processor""", """tokenizer"""]
    __lowercase				:			Dict	=       """AutoImageProcessor"""
    __lowercase				:			int	=       """AutoTokenizer"""
    def __init__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):
      _snake_case  :    int            = None
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,     snake_case_    ,     )
        _snake_case  :    Union[str, Any]            = kwargs.pop("feature_extractor"  )

      _snake_case  :    List[str]            = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You have to specify an image_processor."  )
      if tokenizer is None:
        raise ValueError("You have to specify a tokenizer."  )

      super().__init__(snake_case_    ,     snake_case_  )
      _snake_case  :    List[str]            = self.image_processor
    def __call__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):

      if text is None and images is None:
        raise ValueError("You have to specify either text or images. Both cannot be none."  )

      if text is not None:
        _snake_case  :    List[str]            = self.tokenizer(snake_case_    ,     return_tensors=snake_case_    ,     **snake_case_  )

      if images is not None:
        _snake_case  :    Optional[int]            = self.image_processor(snake_case_    ,     return_tensors=snake_case_    ,     **snake_case_  )

      if text is not None and images is not None:
        _snake_case  :    Dict            = image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      else:
        return BatchEncoding(data=dict(**snake_case_  )    ,     tensor_type=snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.batch_decode(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.decode(*snake_case_    ,     **snake_case_  )
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.tokenizer.model_input_names
      _snake_case  :    int            = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,     snake_case_    ,     )
      return self.image_processor_class


    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,     snake_case_    ,     )
      return self.image_processor



"""simple docstring"""
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor






class   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=[0.5, 0.5, 0.5]    ,     snake_case_=False    ,     ):
      _snake_case  :    List[str]            = size if size is not None else {"height": 30, "width": 30}
      _snake_case  :    Optional[int]            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    Any            = num_channels
      _snake_case  :    Optional[Any]            = min_resolution
      _snake_case  :    int            = max_resolution
      _snake_case  :    int            = do_resize
      _snake_case  :    List[Any]            = size
      _snake_case  :    int            = do_normalize
      _snake_case  :    Any            = image_mean
      _snake_case  :    str            = image_std
      _snake_case  :    List[Any]            = do_reduce_labels


    def        lowerCamelCase__						(     self  ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }


def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    List[str]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Any            = Image.open(dataset[0]["file"]		)
  _snake_case  :    Optional[int]            = Image.open(dataset[1]["file"]		)

  return image, map
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Optional[int]            = load_dataset("hf-internal-testing/fixtures_ade20k"							,  split="test"		)

  _snake_case  :    Optional[int]            = Image.open(dataset[0]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[1]["file"]		)
  _snake_case  :    Dict            = Image.open(dataset[2]["file"]		)
  _snake_case  :    List[str]            = Image.open(dataset[3]["file"]		)

  return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Dict	=       SegformerImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = SegformerImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(snake_case_    ,     "do_resize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "size"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_reduce_labels"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size    ,     {"height": 30, "width": 30}  )
      self.assertEqual(image_processor.do_reduce_labels    ,     snake_case_  )

      _snake_case  :    int            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     reduce_labels=snake_case_  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.do_reduce_labels    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     Image.Image  )

      # Test not batched input
      _snake_case  :    Any            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Any            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     numpify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      # Test not batched input
      _snake_case  :    Optional[int]            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Optional[int]            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Optional[int]            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[Any]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )

      # Test not batched input
      _snake_case  :    Dict            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    int            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    List[str]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      _snake_case  :    Optional[int]            = []
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )
        maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _snake_case  :    Optional[Any]            = image_processing(image_inputs[0]    ,     maps[0]    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test batched
      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test not batched input (PIL images)
      _snake_case      ,       _snake_case  :    int            = prepare_semantic_single_inputs()

      _snake_case  :    List[str]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

      # Test batched input (PIL images)
      _snake_case      ,       _snake_case  :    str            = prepare_semantic_batch_inputs()

      _snake_case  :    List[Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertEqual(
          encoding["pixel_values"].shape    ,     (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(
          encoding["labels"].shape    ,     (
              2,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,     )
      self.assertEqual(encoding["labels"].dtype    ,     torch.long  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )


    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    List[Any]            = self.image_processing_class(**self.image_processor_dict  )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _snake_case      ,       _snake_case  :    List[Any]            = prepare_semantic_single_inputs()
      _snake_case  :    Dict            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 1_50  )

      _snake_case  :    Tuple            = True
      _snake_case  :    Union[str, Any]            = image_processing(snake_case_    ,     snake_case_    ,     return_tensors="pt"  )
      self.assertTrue(encoding["labels"].min().item() >= 0  )
      self.assertTrue(encoding["labels"].max().item() <= 2_55  )

