





'''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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''instructblip_vision_model'''


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			int=14_08      ,						lowerCamelCase_				:			int=61_44      ,						lowerCamelCase_				:			List[str]=39      ,						lowerCamelCase_				:			List[str]=16      ,						lowerCamelCase_				:			Optional[Any]=2_24      ,						lowerCamelCase_				:			List[str]=14      ,						lowerCamelCase_				:			List[str]="gelu"      ,						lowerCamelCase_				:			Optional[int]=1e-6      ,						lowerCamelCase_				:			List[Any]=0.0      ,						lowerCamelCase_				:			List[Any]=1e-10      ,						lowerCamelCase_				:			Union[str, Any]=True      ,						**lowerCamelCase_				:			int      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			hidden_size
							SCREAMING_SNAKE_CASE :				str		=			intermediate_size
							SCREAMING_SNAKE_CASE :				List[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Tuple		=			num_attention_heads
							SCREAMING_SNAKE_CASE :				Tuple		=			patch_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			image_size
							SCREAMING_SNAKE_CASE :				List[Any]		=			initializer_range
							SCREAMING_SNAKE_CASE :				int		=			attention_dropout
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			qkv_bias



					@classmethod
					def lowerCamelCase_			(						cls				:			Union[str, Any]      ,						lowerCamelCase_				:			Union[str, os.PathLike]      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							cls._set_token_in_kwargs(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			cls.get_config_dict(lowerCamelCase_      ,						**lowerCamelCase_						)

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get("""model_type"""						) == "instructblip":
									SCREAMING_SNAKE_CASE :				List[Any]		=			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      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''instructblip_qformer'''


					def __init__(						self				:			Tuple      ,						lowerCamelCase_				:			List[str]=3_05_22      ,						lowerCamelCase_				:			int=7_68      ,						lowerCamelCase_				:			int=12      ,						lowerCamelCase_				:			str=12      ,						lowerCamelCase_				:			Optional[int]=30_72      ,						lowerCamelCase_				:			int="gelu"      ,						lowerCamelCase_				:			str=0.1      ,						lowerCamelCase_				:			List[str]=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			List[Any]=0.02      ,						lowerCamelCase_				:			int=1e-12      ,						lowerCamelCase_				:			Tuple=0      ,						lowerCamelCase_				:			Any="absolute"      ,						lowerCamelCase_				:			Dict=2      ,						lowerCamelCase_				:			int=14_08      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Tuple		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_size
							SCREAMING_SNAKE_CASE :				List[str]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			num_attention_heads
							SCREAMING_SNAKE_CASE :				Tuple		=			hidden_act
							SCREAMING_SNAKE_CASE :				str		=			intermediate_size
							SCREAMING_SNAKE_CASE :				str		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				List[str]		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				List[str]		=			initializer_range
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				List[str]		=			position_embedding_type
							SCREAMING_SNAKE_CASE :				Tuple		=			cross_attention_frequency
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			encoder_hidden_size



					@classmethod
					def lowerCamelCase_			(						cls				:			Dict      ,						lowerCamelCase_				:			Union[str, os.PathLike]      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							cls._set_token_in_kwargs(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[int]		=			cls.get_config_dict(lowerCamelCase_      ,						**lowerCamelCase_						)

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get("""model_type"""						) == "instructblip":
									SCREAMING_SNAKE_CASE :				Dict		=			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(lowerCamelCase_      ,						**lowerCamelCase_						)







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''instructblip'''
					SCREAMING_SNAKE_CASE__				=	True


					def __init__(						self				:			Tuple      ,						lowerCamelCase_				:			List[str]=None      ,						lowerCamelCase_				:			Union[str, Any]=None      ,						lowerCamelCase_				:			List[str]=None      ,						lowerCamelCase_				:			int=32      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							if vision_config is None:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{}
									logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""						)

							if qformer_config is None:
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			{}
									logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""						)

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

							SCREAMING_SNAKE_CASE :				Dict		=			InstructBlipVisionConfig(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			InstructBlipQFormerConfig(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			text_config["""model_type"""] if """model_type""" in text_config else """opt"""
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			CONFIG_MAPPING[text_model_type](**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.text_config.tie_word_embeddings
							SCREAMING_SNAKE_CASE :				List[str]		=			self.text_config.is_encoder_decoder

							SCREAMING_SNAKE_CASE :				List[str]		=			num_query_tokens
							SCREAMING_SNAKE_CASE :				int		=			self.vision_config.hidden_size
							SCREAMING_SNAKE_CASE :				List[str]		=			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			1.0
							SCREAMING_SNAKE_CASE :				List[str]		=			0.02


					@classmethod
					def lowerCamelCase_			(						cls				:			Tuple      ,						lowerCamelCase_				:			InstructBlipVisionConfig      ,						lowerCamelCase_				:			InstructBlipQFormerConfig      ,						lowerCamelCase_				:			PretrainedConfig      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							return cls(
							    vision_config=vision_config.to_dict()      ,						qformer_config=qformer_config.to_dict()      ,						text_config=text_config.to_dict()      ,						**lowerCamelCase_      ,						)



					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			copy.deepcopy(self.__dict__						)
							SCREAMING_SNAKE_CASE :				Any		=			self.vision_config.to_dict()
							SCREAMING_SNAKE_CASE :				Dict		=			self.qformer_config.to_dict()
							SCREAMING_SNAKE_CASE :				List[str]		=			self.text_config.to_dict()
							SCREAMING_SNAKE_CASE :				str		=			self.__class__.model_type
							return output










'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



