

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    A_				( unittest.TestCase  ):


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=7       , lowercase__=3       , lowercase__=30       , lowercase__=400       , lowercase__=True       , lowercase__=None       , lowercase__=True       , lowercase__=[0.5, 0.5, 0.5]       , lowercase__=[0.5, 0.5, 0.5]       , lowercase__=False       , )       -> List[Any]:
        __UpperCAmelCase			   =     size if size is not None else {'''height''': 30, '''width''': 30}
        __UpperCAmelCase			   =     parent
        __UpperCAmelCase			   =     batch_size
        __UpperCAmelCase			   =     num_channels
        __UpperCAmelCase			   =     min_resolution
        __UpperCAmelCase			   =     max_resolution
        __UpperCAmelCase			   =     do_resize
        __UpperCAmelCase			   =     size
        __UpperCAmelCase			   =     do_normalize
        __UpperCAmelCase			   =     image_mean
        __UpperCAmelCase			   =     image_std
        __UpperCAmelCase			   =     do_reduce_labels






    def        lowerCAmelCase_  (self					)       -> Any:
        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    (				) ->       Union[str, Any]:






    '''simple docstring'''
    __UpperCAmelCase			   =     load_dataset('''hf-internal-testing/fixtures_ade20k'''    ,				split='''test''' )

    __UpperCAmelCase			   =     Image.open(dataset[0]['''file'''] )
    __UpperCAmelCase			   =     Image.open(dataset[1]['''file'''] )

    return image, map


def  __a    (				) ->       str:






    '''simple docstring'''
    __UpperCAmelCase			   =     load_dataset('''hf-internal-testing/fixtures_ade20k'''    ,				split='''test''' )

    __UpperCAmelCase			   =     Image.open(dataset[0]['''file'''] )
    __UpperCAmelCase			   =     Image.open(dataset[1]['''file'''] )
    __UpperCAmelCase			   =     Image.open(dataset[2]['''file'''] )
    __UpperCAmelCase			   =     Image.open(dataset[3]['''file'''] )

    return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class    A_				( _a	,       unittest.TestCase  ):


    '''simple docstring'''






    a__       							=    SegformerImageProcessor if is_vision_available() else None


    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     SegformerImageProcessingTester(self					)


    @property
    def        lowerCAmelCase_  (self					)       -> int:
        return self.image_processor_tester.prepare_image_processor_dict()


    def        lowerCAmelCase_  (self					)       -> Dict:
        __UpperCAmelCase			   =     self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(lowercase__       , '''do_resize'''					)					)
        self.assertTrue(hasattr(lowercase__       , '''size'''					)					)
        self.assertTrue(hasattr(lowercase__       , '''do_normalize'''					)					)
        self.assertTrue(hasattr(lowercase__       , '''image_mean'''					)					)
        self.assertTrue(hasattr(lowercase__       , '''image_std'''					)					)
        self.assertTrue(hasattr(lowercase__       , '''do_reduce_labels'''					)					)


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     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       , lowercase__					)

        __UpperCAmelCase			   =     self.image_processing_class.from_dict(self.image_processor_dict       , size=42       , reduce_labels=lowercase__					)
        self.assertEqual(image_processor.size       , {'''height''': 42, '''width''': 42}					)
        self.assertEqual(image_processor.do_reduce_labels       , lowercase__					)


    def        lowerCAmelCase_  (self					)       -> List[str]:
        pass


    def        lowerCAmelCase_  (self					)       -> List[str]:
        # Initialize image_processing
        __UpperCAmelCase			   =     self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        __UpperCAmelCase			   =     prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowercase__					)
        for image in image_inputs:
            self.assertIsInstance(lowercase__       , Image.Image					)

        # Test not batched input
        __UpperCAmelCase			   =     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
        __UpperCAmelCase			   =     image_processing(lowercase__       , 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					)       -> Any:
        # Initialize image_processing
        __UpperCAmelCase			   =     self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        __UpperCAmelCase			   =     prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowercase__       , numpify=lowercase__					)
        for image in image_inputs:
            self.assertIsInstance(lowercase__       , np.ndarray					)

        # Test not batched input
        __UpperCAmelCase			   =     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
        __UpperCAmelCase			   =     image_processing(lowercase__       , 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					)       -> Dict:
        # Initialize image_processing
        __UpperCAmelCase			   =     self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        __UpperCAmelCase			   =     prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowercase__       , torchify=lowercase__					)
        for image in image_inputs:
            self.assertIsInstance(lowercase__       , torch.Tensor					)

        # Test not batched input
        __UpperCAmelCase			   =     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
        __UpperCAmelCase			   =     image_processing(lowercase__       , 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					)       -> Dict:
        # Initialize image_processing
        __UpperCAmelCase			   =     self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        __UpperCAmelCase			   =     prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowercase__       , torchify=lowercase__					)
        __UpperCAmelCase			   =     []
        for image in image_inputs:
            self.assertIsInstance(lowercase__       , torch.Tensor					)
            maps.append(torch.zeros(image.shape[-2:]					).long()					)

        # Test not batched input
        __UpperCAmelCase			   =     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() <= 255					)

        # Test batched
        __UpperCAmelCase			   =     image_processing(lowercase__       , lowercase__       , 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() <= 255					)

        # Test not batched input (PIL images)
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     prepare_semantic_single_inputs()

        __UpperCAmelCase			   =     image_processing(lowercase__       , lowercase__       , 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() <= 255					)

        # Test batched input (PIL images)
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     prepare_semantic_batch_inputs()

        __UpperCAmelCase			   =     image_processing(lowercase__       , lowercase__       , 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() <= 255					)






    def        lowerCAmelCase_  (self					)       -> str:
        # Initialize image_processing
        __UpperCAmelCase			   =     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
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     prepare_semantic_single_inputs()
        __UpperCAmelCase			   =     image_processing(lowercase__       , lowercase__       , return_tensors='''pt'''					)
        self.assertTrue(encoding['''labels'''].min().item() >= 0					)
        self.assertTrue(encoding['''labels'''].max().item() <= 150					)

        __UpperCAmelCase			   =     True
        __UpperCAmelCase			   =     image_processing(lowercase__       , lowercase__       , return_tensors='''pt'''					)
        self.assertTrue(encoding['''labels'''].min().item() >= 0					)
        self.assertTrue(encoding['''labels'''].max().item() <= 255					)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




