




"""simple docstring"""

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


if TYPE_CHECKING:
						pass

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


__lowerCamelCase										=    logging.get_logger(__name__)

__lowerCamelCase										=    {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}



class 			_snake_case					(				_SCREAMING_SNAKE_CASE							):

				'''simple docstring'''




				UpperCamelCase__		  ='''align_text_model'''





				def __init__(				self   :	List[Any]       ,				snake_case   :	Optional[Any]=30_522       ,				snake_case   :	Any=768       ,				snake_case   :	Optional[int]=12       ,				snake_case   :	Dict=12       ,				snake_case   :	int=3_072       ,				snake_case   :	List[str]="gelu"       ,				snake_case   :	Optional[Any]=0.1       ,				snake_case   :	Any=0.1       ,				snake_case   :	Optional[Any]=512       ,				snake_case   :	Tuple=2       ,				snake_case   :	List[str]=0.02       ,				snake_case   :	List[str]=1e-12       ,				snake_case   :	Any=0       ,				snake_case   :	List[Any]="absolute"       ,				snake_case   :	Union[str, Any]=True       ,				**snake_case   :	Optional[Any]       ,				):
								super().__init__(**_lowercase  )

								UpperCAmelCase_       :str         =   vocab_size
								UpperCAmelCase_       :Tuple         =   hidden_size
								UpperCAmelCase_       :Optional[Any]         =   num_hidden_layers
								UpperCAmelCase_       :str         =   num_attention_heads
								UpperCAmelCase_       :Dict         =   hidden_act
								UpperCAmelCase_       :List[str]         =   intermediate_size
								UpperCAmelCase_       :str         =   hidden_dropout_prob
								UpperCAmelCase_       :Any         =   attention_probs_dropout_prob
								UpperCAmelCase_       :Union[str, Any]         =   max_position_embeddings
								UpperCAmelCase_       :List[Any]         =   type_vocab_size
								UpperCAmelCase_       :List[str]         =   initializer_range
								UpperCAmelCase_       :Optional[int]         =   layer_norm_eps
								UpperCAmelCase_       :Dict         =   position_embedding_type
								UpperCAmelCase_       :Dict         =   use_cache
								UpperCAmelCase_       :Dict         =   pad_token_id





				@classmethod
				def 					snake_case_			(				cls   :	str       ,				snake_case   :	List[str]       ,				**snake_case   :	Any  ):
								cls._set_token_in_kwargs(_lowercase  )

								UpperCAmelCase_      ,UpperCAmelCase_       :str         =   cls.get_config_dict(_lowercase       ,				**_lowercase  )

								# get the text config dict if we are loading from AlignConfig
								if config_dict.get('''model_type'''  ) == "align":
												UpperCAmelCase_       :Dict         =   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(_lowercase       ,				**_lowercase  )



class 			_snake_case					(				_SCREAMING_SNAKE_CASE							):

				'''simple docstring'''




				UpperCamelCase__		  ='''align_vision_model'''





				def __init__(				self   :	Any       ,				snake_case   :	Optional[int] = 3       ,				snake_case   :	Dict = 600       ,				snake_case   :	List[str] = 2.0       ,				snake_case   :	Dict = 3.1       ,				snake_case   :	Dict = 8       ,				snake_case   :	Dict = [3, 3, 5, 3, 5, 5, 3]       ,				snake_case   :	Optional[Any] = [32, 16, 24, 40, 80, 112, 192]       ,				snake_case   :	Tuple = [16, 24, 40, 80, 112, 192, 320]       ,				snake_case   :	Union[str, Any] = []       ,				snake_case   :	Any = [1, 2, 2, 2, 1, 2, 1]       ,				snake_case   :	Optional[int] = [1, 2, 2, 3, 3, 4, 1]       ,				snake_case   :	Optional[int] = [1, 6, 6, 6, 6, 6, 6]       ,				snake_case   :	Union[str, Any] = 0.25       ,				snake_case   :	str = "swish"       ,				snake_case   :	List[Any] = 2_560       ,				snake_case   :	Union[str, Any] = "mean"       ,				snake_case   :	Optional[int] = 0.02       ,				snake_case   :	str = 0.001       ,				snake_case   :	Optional[int] = 0.99       ,				snake_case   :	Optional[Any] = 0.2       ,				**snake_case   :	Dict       ,				):
								super().__init__(**_lowercase  )

								UpperCAmelCase_       :Optional[int]         =   num_channels
								UpperCAmelCase_       :Tuple         =   image_size
								UpperCAmelCase_       :List[Any]         =   width_coefficient
								UpperCAmelCase_       :int         =   depth_coefficient
								UpperCAmelCase_       :Optional[int]         =   depth_divisor
								UpperCAmelCase_       :Optional[Any]         =   kernel_sizes
								UpperCAmelCase_       :Optional[int]         =   in_channels
								UpperCAmelCase_       :Dict         =   out_channels
								UpperCAmelCase_       :Optional[Any]         =   depthwise_padding
								UpperCAmelCase_       :Optional[int]         =   strides
								UpperCAmelCase_       :Optional[Any]         =   num_block_repeats
								UpperCAmelCase_       :List[str]         =   expand_ratios
								UpperCAmelCase_       :List[str]         =   squeeze_expansion_ratio
								UpperCAmelCase_       :Optional[Any]         =   hidden_act
								UpperCAmelCase_       :str         =   hidden_dim
								UpperCAmelCase_       :Union[str, Any]         =   pooling_type
								UpperCAmelCase_       :List[Any]         =   initializer_range
								UpperCAmelCase_       :str         =   batch_norm_eps
								UpperCAmelCase_       :str         =   batch_norm_momentum
								UpperCAmelCase_       :Any         =   drop_connect_rate
								UpperCAmelCase_       :List[str]         =   sum(_lowercase  ) * 4





				@classmethod
				def 					snake_case_			(				cls   :	Optional[Any]       ,				snake_case   :	str       ,				**snake_case   :	Optional[int]  ):
								cls._set_token_in_kwargs(_lowercase  )

								UpperCAmelCase_      ,UpperCAmelCase_       :Optional[Any]         =   cls.get_config_dict(_lowercase       ,				**_lowercase  )

								# get the vision config dict if we are loading from AlignConfig
								if config_dict.get('''model_type'''  ) == "align":
												UpperCAmelCase_       :Tuple         =   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 			_snake_case					(				_SCREAMING_SNAKE_CASE							):

				'''simple docstring'''




				UpperCamelCase__		  ='''align'''
				UpperCamelCase__		  =True





				def __init__(				self   :	List[Any]       ,				snake_case   :	str=None       ,				snake_case   :	Any=None       ,				snake_case   :	Union[str, Any]=640       ,				snake_case   :	Any=1.0       ,				snake_case   :	List[str]=0.02       ,				**snake_case   :	Any       ,				):
								super().__init__(**_lowercase  )

								if text_config is None:
												UpperCAmelCase_       :List[str]         =   {}
												logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''  )

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

								UpperCAmelCase_       :Optional[int]         =   AlignTextConfig(**_lowercase  )
								UpperCAmelCase_       :Optional[Any]         =   AlignVisionConfig(**_lowercase  )

								UpperCAmelCase_       :Tuple         =   projection_dim
								UpperCAmelCase_       :str         =   temperature_init_value
								UpperCAmelCase_       :List[str]         =   initializer_range





				@classmethod
				def 					snake_case_			(				cls   :	Dict       ,				snake_case   :	Optional[Any]       ,				snake_case   :	Dict       ,				**snake_case   :	Union[str, Any]  ):
								return cls(text_config=text_config.to_dict()       ,				vision_config=vision_config.to_dict()       ,				**_lowercase  )





				def 					snake_case_			(				self   :	List[Any]  ):
								UpperCAmelCase_       :Union[str, Any]         =   copy.deepcopy(self.__dict__  )
								UpperCAmelCase_       :Optional[int]         =   self.text_config.to_dict()
								UpperCAmelCase_       :List[str]         =   self.vision_config.to_dict()
								UpperCAmelCase_       :Optional[int]         =   self.__class__.model_type
								return output




'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												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=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(prediction_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 258.9606 ) < 1e-2
												assert abs(result_mean.item() - 0.3372 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 202.0296 ) < 1e-2
												assert abs(result_mean.item() - 0.2631 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

												for i, timestep in enumerate(_lowercase ):
																		if i == len(_lowercase ) - 1:
																								_lowerCAmelCase    		=					-1
																		else:
																								_lowerCAmelCase    		=					timesteps[i + 1]

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 51, 0]

												with self.assertRaises(_lowercase       ,  msg="""`custom_timesteps` must be in descending order.""" ):
																		scheduler.set_timesteps(timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

												with self.assertRaises(_lowercase       ,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase       ,  timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config()
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _lowercase       ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,  ):
																		scheduler.set_timesteps(timesteps=_lowercase )
