



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


A_		:  str										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''vision-text-dual-encoder'''
   _UpperCAmelCase  						=      True

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							Any=512				, __lowerCAmelCase      :							Any=2.6_5_9_2				, **__lowerCAmelCase      :							str				)							->					Optional[int]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				)

    if "vision_config" not in kwargs:
     raise ValueError("`vision_config` can not be `None`."				)

    if "text_config" not in kwargs:
     raise ValueError("`text_config` can not be `None`."				)

    a							=		kwargs.pop("vision_config"				)
    a							=		kwargs.pop("text_config"				)

    a							=		vision_config.pop("model_type"				)
    a							=		text_config.pop("model_type"				)

    if vision_model_type == "clip":
     a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				).vision_config
    elif vision_model_type == "clip_vision_model":
     a							=		CLIPVisionConfig(**__lowerCAmelCase				)
    else:
     a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				)

    a							=		AutoConfig.for_model(__lowerCAmelCase				, **__lowerCAmelCase				)

    a							=		projection_dim
    a							=		logit_scale_init_value

   @classmethod
   def 		A				(   cls      :							str				, __lowerCAmelCase      :							PretrainedConfig				, __lowerCAmelCase      :							PretrainedConfig				, **__lowerCAmelCase      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    return cls(vision_config=vision_config.to_dict()				, text_config=text_config.to_dict()				, **__lowerCAmelCase				)




   def 		A				(   self      :							str				)							->					int:


    """simple docstring"""






    a							=		copy.deepcopy(self.__dict__				)
    a							=		self.vision_config.to_dict()
    a							=		self.text_config.to_dict()
    a							=		self.__class__.model_type
    return output








'''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       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

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




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

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

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

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

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

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







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

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

											# the initial conversion succeeds
											_snake_case: Dict	       =		DiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: 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(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)
											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)





