





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
       pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__:      Optional[int]									=		logging.get_logger(__name__)

a__:      Tuple									=		{
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''align_text_model'''



 def __init__(							self,__lowerCamelCase=3_0522,__lowerCamelCase=768,__lowerCamelCase=12,__lowerCamelCase=12,__lowerCamelCase=3072,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=512,__lowerCamelCase=2,__lowerCamelCase=0.02,__lowerCamelCase=1E-12,__lowerCamelCase=0,__lowerCamelCase="absolute",__lowerCamelCase=True,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )

        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__											=						type_vocab_size
        A__											=						initializer_range
        A__											=						layer_norm_eps
        A__											=						position_embedding_type
        A__											=						use_cache
        A__											=						pad_token_id




 @classmethod
 def 				UpperCamelCase			(							cls,__lowerCamelCase,**__lowerCamelCase ):
        cls._set_token_in_kwargs(__lowerCamelCase )

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

        # get the text config dict if we are loading from AlignConfig
        if config_dict.get('''model_type''' ) == "align":
               A__											=						config_dict['''text_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(__lowerCamelCase,**__lowerCamelCase )




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''align_vision_model'''



 def __init__(							self,__lowerCamelCase = 3,__lowerCamelCase = 600,__lowerCamelCase = 2.0,__lowerCamelCase = 3.1,__lowerCamelCase = 8,__lowerCamelCase = [3, 3, 5, 3, 5, 5, 3],__lowerCamelCase = [32, 16, 24, 40, 80, 112, 192],__lowerCamelCase = [16, 24, 40, 80, 112, 192, 320],__lowerCamelCase = [],__lowerCamelCase = [1, 2, 2, 2, 1, 2, 1],__lowerCamelCase = [1, 2, 2, 3, 3, 4, 1],__lowerCamelCase = [1, 6, 6, 6, 6, 6, 6],__lowerCamelCase = 0.25,__lowerCamelCase = "swish",__lowerCamelCase = 2560,__lowerCamelCase = "mean",__lowerCamelCase = 0.02,__lowerCamelCase = 0.001,__lowerCamelCase = 0.99,__lowerCamelCase = 0.2,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )

        A__											=						num_channels
        A__											=						image_size
        A__											=						width_coefficient
        A__											=						depth_coefficient
        A__											=						depth_divisor
        A__											=						kernel_sizes
        A__											=						in_channels
        A__											=						out_channels
        A__											=						depthwise_padding
        A__											=						strides
        A__											=						num_block_repeats
        A__											=						expand_ratios
        A__											=						squeeze_expansion_ratio
        A__											=						hidden_act
        A__											=						hidden_dim
        A__											=						pooling_type
        A__											=						initializer_range
        A__											=						batch_norm_eps
        A__											=						batch_norm_momentum
        A__											=						drop_connect_rate
        A__											=						sum(__lowerCamelCase ) * 4




 @classmethod
 def 				UpperCamelCase			(							cls,__lowerCamelCase,**__lowerCamelCase ):
        cls._set_token_in_kwargs(__lowerCamelCase )

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

        # get the vision config dict if we are loading from AlignConfig
        if config_dict.get('''model_type''' ) == "align":
               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(__lowerCamelCase,**__lowerCamelCase )




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''align'''
 __SCREAMING_SNAKE_CASE							       =	True



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=640,__lowerCamelCase=1.0,__lowerCamelCase=0.02,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )

        if text_config is None:
               A__											=						{}
               logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''' )

        if vision_config is None:
               A__											=						{}
               logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''' )

        A__											=						AlignTextConfig(**__lowerCamelCase )
        A__											=						AlignVisionConfig(**__lowerCamelCase )

        A__											=						projection_dim
        A__											=						temperature_init_value
        A__											=						initializer_range



 @classmethod
 def 				UpperCamelCase			(							cls,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ):
        return cls(text_config=text_config.to_dict(),vision_config=vision_config.to_dict(),**__lowerCamelCase )




 def 				UpperCamelCase			(							self ):
        A__											=						copy.deepcopy(self.__dict__ )
        A__											=						self.text_config.to_dict()
        A__											=						self.vision_config.to_dict()
        A__											=						self.__class__.model_type
        return output












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

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

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

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

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

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

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





