
'''simple docstring'''




from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase			:				int    		=			8







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=BITS						):




    '''simple docstring'''



    A       :   Optional[Any]					=						x.device

    A       :   Optional[Any]					=						(x * 255).int().clamp(0		,				255						)

    A       :   Union[str, Any]					=						2 ** torch.arange(bits - 1		,				-1		,				-1		,				device=snake_case__						)
    A       :   int					=						rearrange(snake_case__		,				'''d -> d 1 1'''						)
    A       :   Optional[Any]					=						rearrange(snake_case__		,				'''b c h w -> b c 1 h w'''						)

    A       :   Union[str, Any]					=						((x & mask) != 0).float()
    A       :   int					=						rearrange(snake_case__		,				'''b c d h w -> b (c d) h w'''						)
    A       :   Any					=						bits * 2 - 1
    return bits







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=BITS						):




    '''simple docstring'''



    A       :   Any					=						x.device

    A       :   List[str]					=						(x > 0).int()
    A       :   str					=						2 ** torch.arange(bits - 1		,				-1		,				-1		,				device=snake_case__		,				dtype=torch.intaa						)

    A       :   Union[str, Any]					=						rearrange(snake_case__		,				'''d -> d 1 1'''						)
    A       :   Tuple					=						rearrange(snake_case__		,				'''b (c d) h w -> b c d h w'''		,				d=8						)
    A       :   int					=						reduce(x * mask		,				'''b c d h w -> b c h w'''		,				'''sum'''						)
    return (dec / 255).clamp(0.0		,				1.0						)







def 		lowerCAmelCase_				(   self		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = 0.0		,				snake_case__ = True		,				snake_case__=None		,				snake_case__ = True		,				):




    '''simple docstring'''



    if self.num_inference_steps is None:
        raise ValueError(
            '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    A       :   Union[str, Any]					=						timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    A       :   str					=						self.alphas_cumprod[timestep]
    A       :   Any					=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    A       :   int					=						1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A       :   Union[str, Any]					=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    A       :   Optional[Any]					=						self.bit_scale
    if self.config.clip_sample:
        A       :   Union[str, Any]					=						torch.clamp(snake_case__		,				-scale		,				snake_case__						)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    A       :   Optional[Any]					=						self._get_variance(snake_case__		,				snake_case__						)
    A       :   Optional[int]					=						eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        A       :   Union[str, Any]					=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A       :   List[Any]					=						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    A       :   Optional[Any]					=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        A       :   Dict					=						model_output.device if torch.is_tensor(snake_case__						) else '''cpu'''
        A       :   Optional[Any]					=						torch.randn(model_output.shape		,				dtype=model_output.dtype		,				generator=snake_case__						).to(snake_case__						)
        A       :   int					=						self._get_variance(snake_case__		,				snake_case__						) ** 0.5 * eta * noise

        A       :   List[str]					=						prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=snake_case__		,				pred_original_sample=snake_case__						)







def 		lowerCAmelCase_				(   self		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__="epsilon"		,				snake_case__=None		,				snake_case__ = True		,				):




    '''simple docstring'''



    A       :   Dict					=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        A,  A       :   int					=						torch.split(snake_case__		,				sample.shape[1]		,				dim=1						)
    else:
        A       :   Any					=						None

    # 1. compute alphas, betas
    A       :   int					=						self.alphas_cumprod[t]
    A       :   Any					=						self.alphas_cumprod[t - 1] if t > 0 else self.one
    A       :   Union[str, Any]					=						1 - alpha_prod_t
    A       :   int					=						1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        A       :   Union[str, Any]					=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        A       :   str					=						model_output
    else:
        raise ValueError(F'Unsupported prediction_type {prediction_type}.'						)

    # 3. Clip "predicted x_0"
    A       :   Optional[int]					=						self.bit_scale
    if self.config.clip_sample:
        A       :   Any					=						torch.clamp(snake_case__		,				-scale		,				snake_case__						)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A       :   Optional[Any]					=						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    A       :   List[str]					=						self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    A       :   Union[str, Any]					=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    A       :   int					=						0
    if t > 0:
        A       :   Tuple					=						torch.randn(
            model_output.size()		,				dtype=model_output.dtype		,				layout=model_output.layout		,				generator=snake_case__						).to(model_output.device						)
        A       :   str					=						(self._get_variance(snake_case__		,				predicted_variance=snake_case__						) ** 0.5) * noise

    A       :   Dict					=						pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=snake_case__		,				pred_original_sample=snake_case__						)







class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 1.0						,			) ->				Dict:






         """simple docstring"""






         super().__init__()
         A       :   Optional[int]					=						bit_scale
         A       :   str					=						(
             ddim_bit_scheduler_step if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else ddpm_bit_scheduler_step
         )

         self.register_modules(unet=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE	)





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE = 256						,			SCREAMING_SNAKE_CASE = 256						,			SCREAMING_SNAKE_CASE = 50						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = "pil"						,			SCREAMING_SNAKE_CASE = True						,			**SCREAMING_SNAKE_CASE						,			) ->				Union[Tuple, ImagePipelineOutput]:






         """simple docstring"""






         A       :   List[Any]					=						torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)						,			generator=SCREAMING_SNAKE_CASE						,			)
         A       :   Tuple					=						decimal_to_bits(SCREAMING_SNAKE_CASE	) * self.bit_scale
         A       :   str					=						latents.to(self.device	)

         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE	)

         for t in self.progress_bar(self.scheduler.timesteps	):
             # predict the noise residual
             A       :   Any					=						self.unet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	).sample

             # compute the previous noisy sample x_t -> x_t-1
             A       :   Union[str, Any]					=						self.scheduler.step(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	).prev_sample

         A       :   List[Any]					=						bits_to_decimal(SCREAMING_SNAKE_CASE	)

         if output_type == "pil":
             A       :   Any					=						self.numpy_to_pil(SCREAMING_SNAKE_CASE	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE	)







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					:			List[str]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_vision_model'





			def __init__(		self					,  __snake_case=1408					,  __snake_case=6144					,  __snake_case=39					,  __snake_case=16					,  __snake_case=224					,  __snake_case=14					,  __snake_case="gelu"					,  __snake_case=1e-6					,  __snake_case=0.0					,  __snake_case=1e-10					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__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 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_qformer'





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=2					,  __snake_case=1408					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  **__snake_case					)

						__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 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=32					,  **__snake_case					)   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

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

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

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

						__a        =InstructBlipVisionConfig(**__snake_case					)
						__a        =InstructBlipQFormerConfig(**__snake_case					)
						__a        =text_config['model_type'] if 'model_type' in text_config else 'opt'
						__a        =CONFIG_MAPPING[text_model_type](**__snake_case					)

						__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        =0.02





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **__snake_case					,  )





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__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






