



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 BeitImageProcessor






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=7,		lowerCamelCase__=3,		lowerCamelCase__=18,		lowerCamelCase__=30,		lowerCamelCase__=400,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=False,		):
        A :       int          =   size if size is not None else {"""height""": 20, """width""": 20}
        A :       Dict          =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        A :       int          =   parent
        A :       Optional[int]          =   batch_size
        A :       Tuple          =   num_channels
        A :       Dict          =   image_size
        A :       Dict          =   min_resolution
        A :       int          =   max_resolution
        A :       Any          =   do_resize
        A :       List[str]          =   size
        A :       List[str]          =   do_center_crop
        A :       Dict          =   crop_size
        A :       int          =   do_normalize
        A :       Any          =   image_mean
        A :       Optional[int]          =   image_std
        A :       Dict          =   do_reduce_labels






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




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   load_dataset("""hf-internal-testing/fixtures_ade20k"""      ,     split="""test"""		)

    A :       Any          =   Image.open(dataset[0]["""file"""]		)
    A :       Dict          =   Image.open(dataset[1]["""file"""]		)

    return image, map




def 	__UpperCamelCase	(  )       ->  Optional[int]:



    """simple docstring"""






    A :       Tuple          =   load_dataset("""hf-internal-testing/fixtures_ade20k"""      ,     split="""test"""		)

    A :       Union[str, Any]          =   Image.open(ds[0]["""file"""]		)
    A :       Dict          =   Image.open(ds[1]["""file"""]		)
    A :       List[Any]          =   Image.open(ds[2]["""file"""]		)
    A :       int          =   Image.open(ds[3]["""file"""]		)

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






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: int					=      BeitImageProcessor if is_vision_available() else None






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   BeitImageProcessingTester(self		)






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.image_processor_tester.prepare_image_processor_dict()






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_resize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""size"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_center_crop"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""center_crop"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_normalize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_mean"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_std"""		)		)






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size,		{"""height""": 20, """width""": 20}		)
        self.assertEqual(image_processor.crop_size,		{"""height""": 18, """width""": 18}		)
        self.assertEqual(image_processor.do_reduce_labels,		lowerCamelCase__		)

        A :       int          =   self.image_processing_class.from_dict(
            self.image_processor_dict,		size=42,		crop_size=84,		reduce_labels=lowerCamelCase__		)
        self.assertEqual(image_processor.size,		{"""height""": 42, """width""": 42}		)
        self.assertEqual(image_processor.crop_size,		{"""height""": 84, """width""": 84}		)
        self.assertEqual(image_processor.do_reduce_labels,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       Optional[Any]          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A :       int          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		Image.Image		)

        # Test not batched input
        A :       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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)

        # Test batched
        A :       List[Any]          =   image_processing(lowerCamelCase__,		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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       Any          =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A :       int          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		numpify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		np.ndarray		)

        # Test not batched input
        A :       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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)

        # Test batched
        A :       Any          =   image_processing(lowerCamelCase__,		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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       Any          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A :       Any          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		torchify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		torch.Tensor		)

        # Test not batched input
        A :       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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)

        # Test batched
        A :       Union[str, Any]          =   image_processing(lowerCamelCase__,		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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       int          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A :       Tuple          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		torchify=lowerCamelCase__		)
        A :       Union[str, Any]          =   []
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		torch.Tensor		)
            maps.append(torch.zeros(image.shape[-2:]		).long()		)

        # Test not batched input
        A :       int          =   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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)
        self.assertEqual(
            encoding["""labels"""].shape,		(
                1,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_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
        A :       List[Any]          =   image_processing(lowerCamelCase__,		lowerCamelCase__,		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.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)
        self.assertEqual(
            encoding["""labels"""].shape,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_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)
        A     ,							A :       Any          =   prepare_semantic_single_inputs()

        A :       Any          =   image_processing(lowerCamelCase__,		lowerCamelCase__,		return_tensors="""pt"""		)
        self.assertEqual(
            encoding["""pixel_values"""].shape,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)
        self.assertEqual(
            encoding["""labels"""].shape,		(
                1,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_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)
        A     ,							A :       List[str]          =   prepare_semantic_batch_inputs()

        A :       Any          =   image_processing(lowerCamelCase__,		lowerCamelCase__,		return_tensors="""pt"""		)
        self.assertEqual(
            encoding["""pixel_values"""].shape,		(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            ),		)
        self.assertEqual(
            encoding["""labels"""].shape,		(
                2,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_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		):
        # Initialize image_processing
        A :       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
        A     ,							A :       List[Any]          =   prepare_semantic_single_inputs()
        A :       Union[str, Any]          =   image_processing(lowerCamelCase__,		lowerCamelCase__,		return_tensors="""pt"""		)
        self.assertTrue(encoding["""labels"""].min().item() >= 0		)
        self.assertTrue(encoding["""labels"""].max().item() <= 150		)

        A :       int          =   True
        A :       Tuple          =   image_processing(lowerCamelCase__,		lowerCamelCase__,		return_tensors="""pt"""		)
        self.assertTrue(encoding["""labels"""].min().item() >= 0		)
        self.assertTrue(encoding["""labels"""].max().item() <= 255		)







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       Any          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       int          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


