import copy
from typing import Dict, Optional

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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



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


	def __init__(   self       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 255       ,  lowerCAmelCase_ = 150       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 2.0       ,  lowerCAmelCase_ = 5.0       ,  lowerCAmelCase_ = 5.0       ,  lowerCAmelCase_ = 0.5       ,  lowerCAmelCase_ = 0.0_7       ,  lowerCAmelCase_ = 12544       ,  lowerCAmelCase_ = 3.0       ,  lowerCAmelCase_ = 0.7_5       ,  lowerCAmelCase_ = 0.0_2       ,  lowerCAmelCase_ = 1.0       ,  lowerCAmelCase_ = 1e-05       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = [4, 8, 16, 32]       ,  lowerCAmelCase_ = 77       ,  lowerCAmelCase_ = 256       ,  lowerCAmelCase_ = 77       ,  lowerCAmelCase_ = 6       ,  lowerCAmelCase_ = 49408       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 16       ,  lowerCAmelCase_ = 256       ,  lowerCAmelCase_ = 256       ,  lowerCAmelCase_ = 256       ,  lowerCAmelCase_ = 1024       ,  lowerCAmelCase_ = "GN"       ,  lowerCAmelCase_ = 6       ,  lowerCAmelCase_ = 10       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 8       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 2048       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 4       ,  **lowerCAmelCase_       ,  ):
		if backbone_config is None:
			logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."       )
			_lowercase   =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_       ):
			_lowercase   =backbone_config.get("model_type"       )
			_lowercase   =CONFIG_MAPPING[backbone_model_type]
			_lowercase   =config_class.from_dict(lowerCAmelCase_       )

		_lowercase   =backbone_config

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

		super().__init__(**lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =copy.deepcopy(self.__dict__       )
		_lowercase   =self.backbone_config.to_dict()
		_lowercase   =self.__class__.model_type
		return output








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

          _a            =		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
     def 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

