



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


a_   :Optional[int]        =      8
def        lowercase_							(A : Optional[int]		,					A : Optional[int]=BITS      ):
       snake_case__							:	Optional[Any]   	=	x.device

       snake_case__							:	List[Any]   	=	(x * 2_5_5).int().clamp(0		,					2_5_5      )

       snake_case__							:	Any   	=	2 ** torch.arange(bits - 1		,					-1		,					-1		,					device=A      )
       snake_case__							:	Tuple   	=	rearrange(A		,					'd -> d 1 1'      )
       snake_case__							:	Tuple   	=	rearrange(A		,					'b c h w -> b c 1 h w'      )

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

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

       snake_case__							:	Union[str, Any]   	=	rearrange(A		,					'd -> d 1 1'      )
       snake_case__							:	int   	=	rearrange(A		,					'b (c d) h w -> b c d h w'		,					d=8      )
       snake_case__							:	List[Any]   	=	reduce(x * mask		,					'b c d h w -> b c h w'		,					'sum'      )
       return (dec / 2_5_5).clamp(0.0		,					1.0      )
def        lowercase_							(self : List[str]		,					A : torch.FloatTensor		,					A : int		,					A : torch.FloatTensor		,					A : float = 0.0		,					A : bool = True		,					A : Optional[Any]=None		,					A : bool = True		,					):
       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)
       snake_case__							:	List[Any]   	=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

       snake_case__							:	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
       snake_case__							:	Dict   	=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

       # 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)
       snake_case__							:	int   	=	self._get_variance(A		,					A      )
       snake_case__							:	int   	=	eta * variance ** 0.5

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

              snake_case__							:	int   	=	prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=A		,					pred_original_sample=A      )
def        lowercase_							(self : List[str]		,					A : torch.FloatTensor		,					A : int		,					A : torch.FloatTensor		,					A : Tuple="epsilon"		,					A : Union[str, Any]=None		,					A : bool = True		,					):
       snake_case__							:	List[Any]   	=	timestep

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

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

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

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

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

       snake_case__							:	Any   	=	pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=A		,					pred_original_sample=A      )





class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


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

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






     @torch.no_grad()
     def __call__(       self  :  List[str], _snake_case  :  Optional[int] = 2_5_6, _snake_case  :  Optional[int] = 2_5_6, _snake_case  :  Optional[int] = 5_0, _snake_case  :  Optional[torch.Generator] = None, _snake_case  :  Optional[int] = 1, _snake_case  :  Optional[str] = "pil", _snake_case  :  bool = True, **_snake_case  :  Tuple, )   ->Union[Tuple, ImagePipelineOutput]:
            snake_case__							:	int   	=	torch.randn(
                (batch_size, self.unet.config.in_channels, height, width), generator=_snake_case, )
            snake_case__							:	Tuple   	=	decimal_to_bits(_snake_case					) * self.bit_scale
            snake_case__							:	Optional[Any]   	=	latents.to(self.device					)

            self.scheduler.set_timesteps(_snake_case					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                   # predict the noise residual
                   snake_case__							:	Union[str, Any]   	=	self.unet(_snake_case, _snake_case					).sample

                   # compute the previous noisy sample x_t -> x_t-1
                   snake_case__							:	int   	=	self.scheduler.step(_snake_case, _snake_case, _snake_case					).prev_sample

            snake_case__							:	Optional[int]   	=	bits_to_decimal(_snake_case					)

            if output_type == "pil":
                   snake_case__							:	Any   	=	self.numpy_to_pil(_snake_case					)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_snake_case					)











import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
       torch.save(model.state_dict()		,					A      )


if __name__ == "__main__":
 a_   :Union[str, Any]        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






