





'''simple docstring'''

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							(DPMSolverSDEScheduler,)
 __lowercase  						=							10
 def 	UpperCAmelCase_				(							self :int		,			**lowercase_ :Union[str, Any]				)->				Dict:
     A__							=     {
         "num_train_timesteps": 11_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "noise_sampler_seed": 0,
     }

     config.update(**lowercase_				)
     return config
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				str:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,			[0.0_0_0_2, 0.0_0_2, 0.0_2]				):
         self.check_over_configs(beta_start=lowercase_		,			beta_end=lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Optional[int]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				List[str]:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[str]:
     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config()
     A__							=     scheduler_class(**lowercase_				)

     scheduler.set_timesteps(self.num_inference_steps				)

     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter * scheduler.init_noise_sigma
     A__							=     sample.to(lowercase_				)

     for i, t in enumerate(scheduler.timesteps				):
         A__							=     scheduler.scale_model_input(lowercase_		,			lowercase_				)

         A__							=     model(lowercase_		,			lowercase_				)

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_				)
         A__							=     output.prev_sample

     A__							=     torch.sum(torch.abs(lowercase_				)				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5				) < 1E-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7				) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6				) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2				) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6				) < 1E-3
 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config(prediction_type="v_prediction"				)
     A__							=     scheduler_class(**lowercase_				)

     scheduler.set_timesteps(self.num_inference_steps				)

     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter * scheduler.init_noise_sigma
     A__							=     sample.to(lowercase_				)

     for i, t in enumerate(scheduler.timesteps				):
         A__							=     scheduler.scale_model_input(lowercase_		,			lowercase_				)

         A__							=     model(lowercase_		,			lowercase_				)

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_				)
         A__							=     output.prev_sample

     A__							=     torch.sum(torch.abs(lowercase_				)				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3				) < 1E-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4				) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3				) < 1E-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7				) < 1E-3
     else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5				) < 1E-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1				) < 1E-3
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				List[Any]:
     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config()
     A__							=     scheduler_class(**lowercase_				)

     scheduler.set_timesteps(self.num_inference_steps		,			device=lowercase_				)

     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
         A__							=     scheduler.scale_model_input(lowercase_		,			lowercase_				)

         A__							=     model(lowercase_		,			lowercase_				)

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_				)
         A__							=     output.prev_sample

     A__							=     torch.sum(torch.abs(lowercase_				)				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8				) < 1E-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5				) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2				) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1				) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6				) < 1E-3



 def 	UpperCAmelCase_				(							self :Tuple				)->				Tuple:
     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config()
     A__							=     scheduler_class(**lowercase_		,			use_karras_sigmas=lowercase_				)

     scheduler.set_timesteps(self.num_inference_steps		,			device=lowercase_				)

     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma
     A__							=     sample.to(lowercase_				)

     for t in scheduler.timesteps:
         A__							=     scheduler.scale_model_input(lowercase_		,			lowercase_				)

         A__							=     model(lowercase_		,			lowercase_				)

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_				)
         A__							=     output.prev_sample

     A__							=     torch.sum(torch.abs(lowercase_				)				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8				) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5				) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2
     else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2				) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2










'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_vision_model"""
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=14_08		,			lowercase_ :int=61_44		,			lowercase_ :List[str]=39		,			lowercase_ :List[Any]=16		,			lowercase_ :Union[str, Any]=2_24		,			lowercase_ :List[str]=14		,			lowercase_ :Dict="gelu"		,			lowercase_ :Tuple=0.0_0_0_0_1		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=1E-10		,			lowercase_ :Optional[Any]=True		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(**lowercase_				)

     A__							=     hidden_size
     A__							=     intermediate_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     patch_size
     A__							=     image_size
     A__							=     initializer_range
     A__							=     attention_dropout
     A__							=     layer_norm_eps
     A__							=     hidden_act
     A__							=     qkv_bias



 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :Optional[Any]				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the vision config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["vision_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_qformer"""
 def __init__(							self :Dict		,			lowercase_ :int=3_05_22		,			lowercase_ :Union[str, Any]=7_68		,			lowercase_ :Tuple=12		,			lowercase_ :Union[str, Any]=12		,			lowercase_ :str=30_72		,			lowercase_ :Any="gelu"		,			lowercase_ :Optional[int]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Dict=5_12		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Dict=1E-12		,			lowercase_ :List[Any]=0		,			lowercase_ :str="absolute"		,			lowercase_ :List[str]=2		,			lowercase_ :Tuple=14_08		,			**lowercase_ :Tuple		,			)->				List[Any]:
     super().__init__(pad_token_id=lowercase_		,			**lowercase_				)

     A__							=     vocab_size
     A__							=     hidden_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     hidden_act
     A__							=     intermediate_size
     A__							=     hidden_dropout_prob
     A__							=     attention_probs_dropout_prob
     A__							=     max_position_embeddings
     A__							=     initializer_range
     A__							=     layer_norm_eps
     A__							=     position_embedding_type
     A__							=     cross_attention_frequency
     A__							=     encoder_hidden_size



 @classmethod
 def 	UpperCAmelCase_				(							cls :int		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :str				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the qformer config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["qformer_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip-2"""
 __lowercase  						=							True
 def __init__(							self :int		,			lowercase_ :List[Any]=None		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=32		,			**lowercase_ :Any				)->				Union[str, Any]:
     super().__init__(**lowercase_				)

     if vision_config is None:
         A__							=     {}
         logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."				)

     if qformer_config is None:
         A__							=     {}
         logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."				)

     if text_config is None:
         A__							=     {}
         logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."				)

     A__							=     BlipaVisionConfig(**lowercase_				)
     A__							=     BlipaQFormerConfig(**lowercase_				)
     A__							=     text_config["model_type"] if "model_type" in text_config else "opt"
     A__							=     CONFIG_MAPPING[text_model_type](**lowercase_				)

     A__							=     self.text_config.tie_word_embeddings
     A__							=     self.text_config.is_encoder_decoder

     A__							=     num_query_tokens
     A__							=     self.vision_config.hidden_size
     A__							=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     A__							=     1.0
     A__							=     0.0_2
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :BlipaVisionConfig		,			lowercase_ :BlipaQFormerConfig		,			lowercase_ :PretrainedConfig		,			**lowercase_ :Any		,			)->				Optional[int]:
     return cls(
         vision_config=vision_config.to_dict()		,			qformer_config=qformer_config.to_dict()		,			text_config=text_config.to_dict()		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.vision_config.to_dict()
     A__							=     self.qformer_config.to_dict()
     A__							=     self.text_config.to_dict()
     A__							=     self.__class__.model_type
     return output



