


import unittest

import numpy as np

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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import BlipImageProcessor







class     __lowerCamelCase     (  unittest.TestCase     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase=7						,       UpperCAmelCase=3						,       UpperCAmelCase=18						,       UpperCAmelCase=30						,       UpperCAmelCase=400						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=False						,       UpperCAmelCase=[0.48145466, 0.4578275, 0.40821073]						,       UpperCAmelCase=[0.26862954, 0.26130258, 0.27577711]						,       UpperCAmelCase=True						,       )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = size if size is not None else {"height": 20, "width": 20}
								lowercase_			 = parent
								lowercase_			 = batch_size
								lowercase_			 = num_channels
								lowercase_			 = image_size
								lowercase_			 = min_resolution
								lowercase_			 = max_resolution
								lowercase_			 = do_resize
								lowercase_			 = size
								lowercase_			 = do_normalize
								lowercase_			 = image_mean
								lowercase_			 = image_std
								lowercase_			 = do_pad
								lowercase_			 = do_convert_rgb




							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								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_convert_rgb": self.do_convert_rgb,
								    "do_pad": self.do_pad,
								}




							def 				A__      (  self						,       UpperCAmelCase=False						,       UpperCAmelCase=False						,       UpperCAmelCase=False  )     ->       Union[str, Any]:






								'''simple docstring'''






								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
									lowercase_			 = []
									for i in range(self.batch_size  ):
										image_inputs.append(
										    np.random.randint(
										        255						,       size=(self.num_channels, self.max_resolution, self.max_resolution)						,       dtype=np.uinta  )  )
								else:
									lowercase_			 = []
									for i in range(self.batch_size  ):
										lowercase_ ,	lowercase_			 = np.random.choice(np.arange(self.min_resolution						,       self.max_resolution  )						,       2  )
										image_inputs.append(np.random.randint(255						,       size=(self.num_channels, width, height)						,       dtype=np.uinta  )  )

								if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									lowercase_			 = [Image.fromarray(np.moveaxis(_lowerCAmelCase						,       0						,       -1  )  ) for x in image_inputs]

								if torchify:
									lowercase_			 = [torch.from_numpy(_lowerCAmelCase  ) for x in image_inputs]

								return image_inputs







@require_torch
@require_vision
class     __lowerCamelCase     (  snake_case_							,   unittest.TestCase     ):


							"""simple docstring"""

							lowerCAmelCase__          =				BlipImageProcessor if is_vision_available() else None




							def 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = BlipImageProcessingTester(self  )




							@property
							def 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								return self.image_processor_tester.prepare_image_processor_dict()




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_resize"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "size"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_normalize"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "image_mean"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "image_std"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_convert_rgb"  )  )




							def 				A__      (  self  )     ->       List[Any]:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase_			 = self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,       Image.Image  )

								# Test not batched input
								lowercase_			 = image_processor(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
								lowercase_			 = image_processor(_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"],
								    )						,       )




							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								lowercase_			 = self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						,       numpify=_lowerCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,       np.ndarray  )

								# Test not batched input
								lowercase_			 = image_processor(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
								lowercase_			 = image_processor(_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"],
								    )						,       )




							def 				A__      (  self  )     ->       str:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								lowercase_			 = self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						,       torchify=_lowerCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,       torch.Tensor  )

								# Test not batched input
								lowercase_			 = image_processor(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
								lowercase_			 = image_processor(_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"],
								    )						,       )







@require_torch
@require_vision
class     __lowerCamelCase     (  snake_case_							,   unittest.TestCase     ):


							"""simple docstring"""

							lowerCAmelCase__          =				BlipImageProcessor if is_vision_available() else None




							def 				A__      (  self  )     ->       str:






								'''simple docstring'''






								lowercase_			 = BlipImageProcessingTester(self						,       num_channels=4  )
								lowercase_			 = 3




							@property
							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								return self.image_processor_tester.prepare_image_processor_dict()




							def 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_resize"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "size"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_normalize"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "image_mean"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "image_std"  )  )
								self.assertTrue(hasattr(_lowerCAmelCase						,       "do_convert_rgb"  )  )




							def 				A__      (  self  )     ->       List[Any]:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase_			 = self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,       Image.Image  )

								# Test not batched input
								lowercase_			 = image_processor(image_inputs[0]						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )						,       )

								# Test batched
								lowercase_			 = image_processor(_lowerCAmelCase						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )						,       )



import copy
from typing import Dict, Optional

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


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 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']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
