
import copy
from typing import Dict, Optional

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


lowercase						:      Any							=							logging.get_logger(__name__)

lowercase						:      Union[str, 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 __snake_case	(   UpperCAmelCase_  ):
    _a	:		Any=       "oneformer"
    _a	:		Any=       {"hidden_size": "hidden_dim"}
    def __init__(						self	,snake_case = None	,snake_case = 255	,snake_case = 150	,snake_case = 0.1	,snake_case = 2.0	,snake_case = 5.0	,snake_case = 5.0	,snake_case = 0.5	,snake_case = 0.07	,snake_case = 12544	,snake_case = 3.0	,snake_case = 0.75	,snake_case = 0.02	,snake_case = 1.0	,snake_case = 1e-05	,snake_case = False	,snake_case = True	,snake_case = True	,snake_case = [4, 8, 16, 32]	,snake_case = 77	,snake_case = 256	,snake_case = 77	,snake_case = 6	,snake_case = 49408	,snake_case = 2	,snake_case = 16	,snake_case = 256	,snake_case = 256	,snake_case = 256	,snake_case = 1024	,snake_case = "GN"	,snake_case = 6	,snake_case = 10	,snake_case = True	,snake_case = 8	,snake_case = 0.1	,snake_case = 2048	,snake_case = False	,snake_case = False	,snake_case = 2	,snake_case = 4	,**snake_case	,):



           '''simple docstring'''







           if backbone_config is None:
                  logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""   )
                  lowercase							:  List[str] 			=   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=snake_case	,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	,)
           elif isinstance(snake_case	,snake_case   ):
                  lowercase							:  Optional[Any] 			=   backbone_config.get("""model_type"""   )
                  lowercase							:  Any 			=   CONFIG_MAPPING[backbone_model_type]
                  lowercase							:  List[Any] 			=   config_class.from_dict(snake_case   )

           lowercase							:  Union[str, Any] 			=   backbone_config

           lowercase							:  List[str] 			=   ignore_value
           lowercase							:  Optional[Any] 			=   num_queries
           lowercase							:  Dict 			=   no_object_weight
           lowercase							:  List[str] 			=   class_weight
           lowercase							:  str 			=   mask_weight
           lowercase							:  Optional[int] 			=   dice_weight
           lowercase							:  Dict 			=   contrastive_weight
           lowercase							:  Optional[int] 			=   contrastive_temperature
           lowercase							:  str 			=   train_num_points
           lowercase							:  List[Any] 			=   oversample_ratio
           lowercase							:  str 			=   importance_sample_ratio
           lowercase							:  Any 			=   init_std
           lowercase							:  Optional[int] 			=   init_xavier_std
           lowercase							:  str 			=   layer_norm_eps
           lowercase							:  List[str] 			=   is_training
           lowercase							:  int 			=   use_auxiliary_loss
           lowercase							:  str 			=   output_auxiliary_logits
           lowercase							:  int 			=   strides
           lowercase							:  Optional[Any] 			=   task_seq_len
           lowercase							:  Dict 			=   text_encoder_width
           lowercase							:  Tuple 			=   text_encoder_context_length
           lowercase							:  List[str] 			=   text_encoder_num_layers
           lowercase							:  Any 			=   text_encoder_vocab_size
           lowercase							:  List[Any] 			=   text_encoder_proj_layers
           lowercase							:  str 			=   text_encoder_n_ctx
           lowercase							:  List[str] 			=   conv_dim
           lowercase							:  str 			=   mask_dim
           lowercase							:  Tuple 			=   hidden_dim
           lowercase							:  str 			=   encoder_feedforward_dim
           lowercase							:  Dict 			=   norm
           lowercase							:  List[Any] 			=   encoder_layers
           lowercase							:  Tuple 			=   decoder_layers
           lowercase							:  Optional[int] 			=   use_task_norm
           lowercase							:  Any 			=   num_attention_heads
           lowercase							:  int 			=   dropout
           lowercase							:  Dict 			=   dim_feedforward
           lowercase							:  Optional[Any] 			=   pre_norm
           lowercase							:  str 			=   enforce_input_proj
           lowercase							:  str 			=   query_dec_layers
           lowercase							:  Any 			=   common_stride
           lowercase							:  Any 			=   decoder_layers

           super().__init__(**snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   copy.deepcopy(self.__dict__   )
           lowercase							:  List[str] 			=   self.backbone_config.to_dict()
           lowercase							:  List[str] 			=   self.__class__.model_type
           return output


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, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   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   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   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   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   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   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
