
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 CLIPImageProcessor





class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			int=7  ,      _lowerCAmelCase					:			List[str]=3  ,      _lowerCAmelCase					:			Tuple=18  ,      _lowerCAmelCase					:			str=30  ,      _lowerCAmelCase					:			int=400  ,      _lowerCAmelCase					:			int=True  ,      _lowerCAmelCase					:			Dict=None  ,      _lowerCAmelCase					:			Optional[int]=True  ,      _lowerCAmelCase					:			List[str]=None  ,      _lowerCAmelCase					:			Optional[int]=True  ,      _lowerCAmelCase					:			Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]  ,      _lowerCAmelCase					:			Any=[0.26862954, 0.26130258, 0.27577711]  ,      _lowerCAmelCase					:			Any=True  ,      )					->   Optional[int]:
         _lowerCAmelCase						=		size if size is not None else {'shortest_edge': 20}
         _lowerCAmelCase						=		crop_size if crop_size is not None else {'height': 18, 'width': 18}
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		num_channels
         _lowerCAmelCase						=		image_size
         _lowerCAmelCase						=		min_resolution
         _lowerCAmelCase						=		max_resolution
         _lowerCAmelCase						=		do_resize
         _lowerCAmelCase						=		size
         _lowerCAmelCase						=		do_center_crop
         _lowerCAmelCase						=		crop_size
         _lowerCAmelCase						=		do_normalize
         _lowerCAmelCase						=		image_mean
         _lowerCAmelCase						=		image_std
         _lowerCAmelCase						=		do_convert_rgb



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Any:
         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_convert_rgb": self.do_convert_rgb,
         }






       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			Union[str, Any]=False  ,      _lowerCAmelCase					:			Tuple=False  ,      _lowerCAmelCase					:			Dict=False       )					->   Union[str, Any]:
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
           _lowerCAmelCase						=		[]
           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:
           _lowerCAmelCase						=		[]
           for i in range(self.batch_size       ):
             _lowerCAmelCase	,       _lowerCAmelCase						=		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
           _lowerCAmelCase						=		[Image.fromarray(np.moveaxis(_lowerCAmelCase  ,      0  ,      -1       )       ) for x in image_inputs]

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

         return image_inputs





@require_torch
@require_vision
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							CLIPImageProcessor if is_vision_available() else None



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   str:
         _lowerCAmelCase						=		CLIPImageProcessingTester(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         return self.image_processor_tester.prepare_image_processor_dict()



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   int:
         _lowerCAmelCase						=		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'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_convert_rgb'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Optional[int]:
         _lowerCAmelCase						=		self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size  ,      {'shortest_edge': 20}       )
         self.assertEqual(image_processor.crop_size  ,      {'height': 18, 'width': 18}       )

         _lowerCAmelCase						=		self.image_processing_class.from_dict(self.image_processor_dict  ,      size=42  ,      crop_size=84       )
         self.assertEqual(image_processor.size  ,      {'shortest_edge': 42}       )
         self.assertEqual(image_processor.crop_size  ,      {'height': 84, 'width': 84}       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         pass



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Optional[int]:
         # Initialize image_processing
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         _lowerCAmelCase						=		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase  ,      Image.Image       )

         # Test not batched input
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Optional[Any]:
         # Initialize image_processing
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   List[Any]:
         # Initialize image_processing
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		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'],
             )  ,      )





@require_torch
@require_vision
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							CLIPImageProcessor if is_vision_available() else None



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   Union[str, Any]:
         _lowerCAmelCase						=		CLIPImageProcessingTester(self  ,      num_channels=4       )
         _lowerCAmelCase						=		3



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[str]:
         _lowerCAmelCase						=		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'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_convert_rgb'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         pass






       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Tuple:
         # Initialize image_processing
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         _lowerCAmelCase						=		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase  ,      Image.Image       )

         # Test not batched input
         _lowerCAmelCase						=		image_processing(image_inputs[0]  ,      return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,      (
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )  ,      )

         # Test batched
         _lowerCAmelCase						=		image_processing(_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.crop_size['height'],
                 self.image_processor_tester.crop_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
