




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE 						={
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					"mask2former"
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["swin"]
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					{"hidden_size": "hidden_dim"}


				def __init__(							self:   Tuple		,					_lowerCamelCase:   Optional[Dict] = None		,					_lowerCamelCase:   int = 2_56		,					_lowerCamelCase:   int = 2_56		,					_lowerCamelCase:   int = 2_56		,					_lowerCamelCase:   int = 10_24		,					_lowerCamelCase:   str = "relu"		,					_lowerCamelCase:   int = 6		,					_lowerCamelCase:   int = 10		,					_lowerCamelCase:   int = 8		,					_lowerCamelCase:   float = 0.0		,					_lowerCamelCase:   int = 20_48		,					_lowerCamelCase:   bool = False		,					_lowerCamelCase:   bool = False		,					_lowerCamelCase:   int = 4		,					_lowerCamelCase:   int = 2_55		,					_lowerCamelCase:   int = 1_00		,					_lowerCamelCase:   float = 0.1		,					_lowerCamelCase:   float = 2.0		,					_lowerCamelCase:   float = 5.0		,					_lowerCamelCase:   float = 5.0		,					_lowerCamelCase:   int = 1_25_44		,					_lowerCamelCase:   float = 3.0		,					_lowerCamelCase:   float = 0.75		,					_lowerCamelCase:   float = 0.02		,					_lowerCamelCase:   float = 1.0		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   List[int] = [4, 8, 16, 32]		,					_lowerCamelCase:   bool = None		,					**_lowerCamelCase:   Union[str, Any]		,					):
											if backbone_config is None:
																		logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''		)
																		SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING['''swin'''](
																		    image_size=2_24		,					in_channels=3		,					patch_size=4		,					embed_dim=96		,					depths=[2, 2, 18, 2]		,					num_heads=[3, 6, 12, 24]		,					window_size=7		,					drop_path_rate=0.3		,					use_absolute_embeddings=_lowerCamelCase		,					out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,					)

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    backbone_config.pop('''model_type'''		)
																		SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING[backbone_model_type]
																		SCREAMING_SNAKE_CASE_   	=    config_class.from_dict(_lowerCamelCase		)

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
																		logger.warning_once(
																		    f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
																		    f"Supported model types: {','.join(self.backbones_supported		)}"		)

											SCREAMING_SNAKE_CASE_   	=    backbone_config
											SCREAMING_SNAKE_CASE_   	=    feature_size
											SCREAMING_SNAKE_CASE_   	=    mask_feature_size
											SCREAMING_SNAKE_CASE_   	=    hidden_dim
											SCREAMING_SNAKE_CASE_   	=    encoder_feedforward_dim
											SCREAMING_SNAKE_CASE_   	=    activation_function
											SCREAMING_SNAKE_CASE_   	=    encoder_layers
											SCREAMING_SNAKE_CASE_   	=    decoder_layers
											SCREAMING_SNAKE_CASE_   	=    num_attention_heads
											SCREAMING_SNAKE_CASE_   	=    dropout
											SCREAMING_SNAKE_CASE_   	=    dim_feedforward
											SCREAMING_SNAKE_CASE_   	=    pre_norm
											SCREAMING_SNAKE_CASE_   	=    enforce_input_projection
											SCREAMING_SNAKE_CASE_   	=    common_stride
											SCREAMING_SNAKE_CASE_   	=    ignore_value
											SCREAMING_SNAKE_CASE_   	=    num_queries
											SCREAMING_SNAKE_CASE_   	=    no_object_weight
											SCREAMING_SNAKE_CASE_   	=    class_weight
											SCREAMING_SNAKE_CASE_   	=    mask_weight
											SCREAMING_SNAKE_CASE_   	=    dice_weight
											SCREAMING_SNAKE_CASE_   	=    train_num_points
											SCREAMING_SNAKE_CASE_   	=    oversample_ratio
											SCREAMING_SNAKE_CASE_   	=    importance_sample_ratio
											SCREAMING_SNAKE_CASE_   	=    init_std
											SCREAMING_SNAKE_CASE_   	=    init_xavier_std
											SCREAMING_SNAKE_CASE_   	=    use_auxiliary_loss
											SCREAMING_SNAKE_CASE_   	=    feature_strides
											SCREAMING_SNAKE_CASE_   	=    output_auxiliary_logits
											SCREAMING_SNAKE_CASE_   	=    decoder_layers

											super().__init__(**_lowerCamelCase		)


				@classmethod
				def     _A							(							cls:   Dict		,					_lowerCamelCase:   PretrainedConfig		,					**_lowerCamelCase:   Tuple		):
											return cls(
											    backbone_config=_lowerCamelCase		,					**_lowerCamelCase		,					)





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(self.__dict__		)
											SCREAMING_SNAKE_CASE_   	=    self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE_   	=    self.__class__.model_type
											return output








import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor






class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Union[str, Any]=7		,					_lowerCamelCase:   int=3		,					_lowerCamelCase:   Optional[int]=18		,					_lowerCamelCase:   Optional[Any]=30		,					_lowerCamelCase:   Any=4_00		,					_lowerCamelCase:   List[str]=True		,					_lowerCamelCase:   str=None		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Union[str, Any]=False		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   List[str]=[0.5, 0.5, 0.5]		,					_lowerCamelCase:   Dict=[0.5, 0.5, 0.5]		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    min_resolution
											SCREAMING_SNAKE_CASE_   	=    max_resolution
											SCREAMING_SNAKE_CASE_   	=    do_resize
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else {'''height''': 18, '''width''': 20}
											SCREAMING_SNAKE_CASE_   	=    do_thumbnail
											SCREAMING_SNAKE_CASE_   	=    do_align_axis
											SCREAMING_SNAKE_CASE_   	=    do_pad
											SCREAMING_SNAKE_CASE_   	=    do_normalize
											SCREAMING_SNAKE_CASE_   	=    image_mean
											SCREAMING_SNAKE_CASE_   	=    image_std





				def     _A							(							self:   str		):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					DonutImageProcessor if is_vision_available() else None


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    DonutImageProcessingTester(self		)


				@property
				def     _A							(							self:   List[str]		):
											return self.image_processor_tester.prepare_image_processor_dict()


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_resize'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''size'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_thumbnail'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_align_long_axis'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_pad'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''do_normalize'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_mean'''		)		)
											self.assertTrue(hasattr(_lowerCamelCase		,					'''image_std'''		)		)


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size		,					{'''height''': 18, '''width''': 20}		)

											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		)
											self.assertEqual(image_processor.size		,					{'''height''': 42, '''width''': 42}		)

											# Previous config had dimensions in (width, height) order
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class.from_dict(self.image_processor_dict		,					size=(42, 84)		)
											self.assertEqual(image_processor.size		,					{'''height''': 84, '''width''': 42}		)


				def     _A							(							self:   List[Any]		):
											pass


				@is_flaky()
				def     _A							(							self:   int		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											SCREAMING_SNAKE_CASE_   	=    prepare_image_inputs(self.image_processor_tester		,					equal_resolution=_lowerCamelCase		)
											for image in image_inputs:
																		self.assertIsInstance(_lowerCamelCase		,					Image.Image		)

											# Test not batched input
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)


				@is_flaky()
				def     _A							(							self:   Optional[Any]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)





				@is_flaky()
				def     _A							(							self:   List[str]		):
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_   	=    self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )		,					)


