


'''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


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'instructblip_vision_model'




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

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)

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




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							cls._set_token_in_kwargs(UpperCamelCase_							)

							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= cls.get_config_dict(UpperCamelCase_    ,   **UpperCamelCase_							)

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



class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'instructblip_qformer'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=0    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=2    ,   UpperCamelCase_=1408    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


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

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




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							cls._set_token_in_kwargs(UpperCamelCase_							)

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= cls.get_config_dict(UpperCamelCase_    ,   **UpperCamelCase_							)

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



class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'instructblip'
		_a     =				True




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

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)

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

							if qformer_config is None:
												UpperCamelCase__   :Union[str, Any]												= {}
												logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''							)

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

							UpperCamelCase__   :Union[str, Any]												= InstructBlipVisionConfig(**UpperCamelCase_							)
							UpperCamelCase__   :str												= InstructBlipQFormerConfig(**UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
							UpperCamelCase__   :Union[str, Any]												= CONFIG_MAPPING[text_model_type](**UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= self.text_config.tie_word_embeddings
							UpperCamelCase__   :int												= self.text_config.is_encoder_decoder

							UpperCamelCase__   :Union[str, Any]												= num_query_tokens
							UpperCamelCase__   :Optional[Any]												= self.vision_config.hidden_size
							UpperCamelCase__   :Any												= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							UpperCamelCase__   :str												= 1.0
							UpperCamelCase__   :List[str]												= 0.02




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




'''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


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

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

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

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  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)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

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

  __lowerCamelCase       :       Union[str, Any]           = 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
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 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)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (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
  __lowerCamelCase       :       Union[str, 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
  __lowerCamelCase       :       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
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

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

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 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":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

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

  # 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
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = 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
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

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




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

    for t in self.progress_bar(self.scheduler.timesteps   ):
      # predict the noise residual
      __lowerCamelCase       :       int           = self.unet(_a			,	_a   ).sample

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

    if output_type == "pil":
      __lowerCamelCase       :       int           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






