
'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor




class     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any] = False)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     1_0

								if only_cross_attention:
														UpperCamelCase_     :	Union[str, Any]					     =     1_2
								else:
														# when only cross attention is not set, the cross attention dim must be the same as the query dim
														UpperCamelCase_     :	Union[str, Any]					     =     query_dim

								return {
								    "query_dim": query_dim,
								    "cross_attention_dim": cross_attention_dim,
								    "heads": 2,
								    "dim_head": 4,
								    "added_kv_proj_dim": 6,
								    "norm_num_groups": 1,
								    "only_cross_attention": only_cross_attention,
								    "processor": AttnAddedKVProcessor(),
								}




		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any])					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     2

								UpperCamelCase_     :	Tuple					     =     torch.rand(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    3			,    2)
								UpperCamelCase_     :	List[Any]					     =     torch.rand(_SCREAMING_SNAKE_CASE			,    4			,    _SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Tuple					     =     None

								return {
								    "hidden_states": hidden_states,
								    "encoder_hidden_states": encoder_hidden_states,
								    "attention_mask": attention_mask,
								}





		def 	_snake_case	(  self					:Union[str, Any])					->			str:






								'''simple docstring'''






								torch.manual_seed(0)

								UpperCamelCase_     :	Any					     =     self.get_constructor_arguments(only_cross_attention=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Union[str, Any]					     =     Attention(**_SCREAMING_SNAKE_CASE)

								self.assertTrue(attn.to_k is not None)
								self.assertTrue(attn.to_v is not None)

								UpperCamelCase_     :	Any					     =     self.get_forward_arguments(
								    query_dim=constructor_args["""query_dim"""]			,    added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""])

								UpperCamelCase_     :	List[Any]					     =     attn(**_SCREAMING_SNAKE_CASE)

								# only self attention

								torch.manual_seed(0)

								UpperCamelCase_     :	Optional[Any]					     =     self.get_constructor_arguments(only_cross_attention=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     Attention(**_SCREAMING_SNAKE_CASE)

								self.assertTrue(attn.to_k is None)
								self.assertTrue(attn.to_v is None)

								UpperCamelCase_     :	str					     =     self.get_forward_arguments(
								    query_dim=constructor_args["""query_dim"""]			,    added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""])

								UpperCamelCase_     :	Dict					     =     attn(**_SCREAMING_SNAKE_CASE)

								self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all())







class     __snake_case			(    unittest.TestCase	):




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






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""			,    safety_checker=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	int					     =     pipe(
								    """foo"""			,    num_inference_steps=2			,    generator=torch.Generator("""cpu""").manual_seed(0)			,    output_type="""np"""			,    ).images

								# the initial conversion succeeds
								UpperCamelCase_     :	List[str]					     =     DiffusionPipeline.from_pretrained(
								    """hf-internal-testing/tiny-stable-diffusion-pipe"""			,    device_map="""sequential"""			,    safety_checker=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Any					     =     pipe(
								    """foo"""			,    num_inference_steps=2			,    generator=torch.Generator("""cpu""").manual_seed(0)			,    output_type="""np"""			,    ).images

								with tempfile.TemporaryDirectory() as tmpdir:
														# save the converted model
														pipe.save_pretrained(_SCREAMING_SNAKE_CASE)

														# can also load the converted weights
														UpperCamelCase_     :	str					     =     DiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE			,    device_map="""sequential"""			,    safety_checker=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[str]					     =     pipe(
								    """foo"""			,    num_inference_steps=2			,    generator=torch.Generator("""cpu""").manual_seed(0)			,    output_type="""np"""			,    ).images

								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    atol=1E-5))
								self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    atol=1E-5))








'''simple docstring'''






import copy
from typing import Dict, Optional

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


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     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 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				CONFIG_MAPPING['swin'](
               image_size=2_24   ,					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=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

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

      super().__init__(**__magic_name__    )




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

