
"""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__				=					logging.get_logger(__name__)

lowerCAmelCase__				=					{
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''blip_2_vision_model'''

							def __init__(self							,							SCREAMING_SNAKE_CASE_=14_08							,							SCREAMING_SNAKE_CASE_=61_44							,							SCREAMING_SNAKE_CASE_=39							,							SCREAMING_SNAKE_CASE_=16							,							SCREAMING_SNAKE_CASE_=2_24							,							SCREAMING_SNAKE_CASE_=14							,							SCREAMING_SNAKE_CASE_="gelu"							,							SCREAMING_SNAKE_CASE_=0.0_00_01							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=1e-1_0							,							SCREAMING_SNAKE_CASE_=True							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )

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







							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			cls.get_config_dict(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									# get the vision config dict if we are loading from Blip2Config
									if config_dict.get('''model_type'''     ) == "blip-2":
											SCREAMING_SNAKE_CASE_    			=			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(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''blip_2_qformer'''

							def __init__(self							,							SCREAMING_SNAKE_CASE_=3_05_22							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=12							,							SCREAMING_SNAKE_CASE_=12							,							SCREAMING_SNAKE_CASE_=30_72							,							SCREAMING_SNAKE_CASE_="gelu"							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=5_12							,							SCREAMING_SNAKE_CASE_=0.02							,							SCREAMING_SNAKE_CASE_=1e-1_2							,							SCREAMING_SNAKE_CASE_=0							,							SCREAMING_SNAKE_CASE_="absolute"							,							SCREAMING_SNAKE_CASE_=2							,							SCREAMING_SNAKE_CASE_=14_08							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

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







							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			cls.get_config_dict(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									# get the qformer config dict if we are loading from Blip2Config
									if config_dict.get('''model_type'''     ) == "blip-2":
											SCREAMING_SNAKE_CASE_    			=			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(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''blip-2'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=32							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )

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

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

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

									SCREAMING_SNAKE_CASE_    			=			BlipaVisionConfig(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			BlipaQFormerConfig(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
									SCREAMING_SNAKE_CASE_    			=			CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			self.text_config.tie_word_embeddings
									SCREAMING_SNAKE_CASE_    			=			self.text_config.is_encoder_decoder

									SCREAMING_SNAKE_CASE_    			=			num_query_tokens
									SCREAMING_SNAKE_CASE_    			=			self.vision_config.hidden_size
									SCREAMING_SNAKE_CASE_    			=			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									SCREAMING_SNAKE_CASE_    			=			1.0
									SCREAMING_SNAKE_CASE_    			=			0.02

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""simple docstring"""







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

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""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=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







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

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass