



"""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


snake_case   					=				8




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				List[str]:
  _snake_case   	=      x.device

  _snake_case   	=      (x * 255).int().clamp(0  ,						255	)

  _snake_case   	=      2 ** torch.arange(bits - 1  ,						-1  ,						-1  ,						device=lowerCAmelCase_	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''d -> d 1 1'''	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b c h w -> b c 1 h w'''	)

  _snake_case   	=      ((x & mask) != 0).float()
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b c d h w -> b (c d) h w'''	)
  _snake_case   	=      bits * 2 - 1
  return bits




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=BITS	)						->				Union[str, Any]:
  _snake_case   	=      x.device

  _snake_case   	=      (x > 0).int()
  _snake_case   	=      2 ** torch.arange(bits - 1  ,						-1  ,						-1  ,						device=lowerCAmelCase_  ,						dtype=torch.intaa	)

  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''d -> d 1 1'''	)
  _snake_case   	=      rearrange(lowerCAmelCase_  ,						'''b (c d) h w -> b c d h w'''  ,						d=8	)
  _snake_case   	=      reduce(x * mask  ,						'''b c d h w -> b c h w'''  ,						'''sum'''	)
  return (dec / 255).clamp(0.0  ,						1.0	)




def 			snake_case		(		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)
  _snake_case   	=      timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  _snake_case   	=      self.alphas_cumprod[timestep]
  _snake_case   	=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  _snake_case   	=      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   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  _snake_case   	=      self.bit_scale
  if self.config.clip_sample:
    _snake_case   	=      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)
  _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _snake_case   	=      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   	=      (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   	=      (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   	=      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   	=      model_output.device if torch.is_tensor(lowerCAmelCase_	) else '''cpu'''
    _snake_case   	=      torch.randn(model_output.shape  ,						dtype=model_output.dtype  ,						generator=lowerCAmelCase_	).to(lowerCAmelCase_	)
    _snake_case   	=      self._get_variance(lowerCAmelCase_  ,						lowerCAmelCase_	) ** 0.5 * eta * noise

    _snake_case   	=      prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)




def 			snake_case		(		self  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_="epsilon"  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_ = True  ,						)						->				Union[DDPMSchedulerOutput, Tuple]:
  _snake_case   	=      timestep

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

  # 1. compute alphas, betas
  _snake_case   	=      self.alphas_cumprod[t]
  _snake_case   	=      self.alphas_cumprod[t - 1] if t > 0 else self.one
  _snake_case   	=      1 - alpha_prod_t
  _snake_case   	=      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   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    _snake_case   	=      model_output
  else:
    raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

  # 3. Clip "predicted x_0"
  _snake_case   	=      self.bit_scale
  if self.config.clip_sample:
    _snake_case   	=      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
  _snake_case   	=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  _snake_case   	=      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   	=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  _snake_case   	=      pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_  ,						pred_original_sample=lowerCAmelCase_	)







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	UNetaDConditionModel							,       __lowerCamelCase    :	Union[DDIMScheduler, DDPMScheduler]							,       __lowerCamelCase    :	Optional[float] = 1.0							,       ):



     """simple docstring"""
     super().__init__()
     _snake_case   	=      bit_scale
     _snake_case   	=      (
         ddim_bit_scheduler_step if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else ddpm_bit_scheduler_step
     )

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





   @torch.no_grad()
   def __call__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 2_5_6							,       __lowerCamelCase    :	Optional[int] = 5_0							,       __lowerCamelCase    :	Optional[torch.Generator] = None							,       __lowerCamelCase    :	Optional[int] = 1							,       __lowerCamelCase    :	Optional[str] = "pil"							,       __lowerCamelCase    :	bool = True							,       **__lowerCamelCase    :	List[str]							,       ):



     """simple docstring"""
     _snake_case   	=      torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)							,       generator=__lowerCamelCase							,       )
     _snake_case   	=      decimal_to_bits(__lowerCamelCase				) * self.bit_scale
     _snake_case   	=      latents.to(self.device				)

     self.scheduler.set_timesteps(__lowerCamelCase				)

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

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

     _snake_case   	=      bits_to_decimal(__lowerCamelCase				)

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

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase				)










import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							Any      						= [
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "decoder.output_projection.weight",
				    "_float_tensor",
				    "encoder.embed_positions._float_tensor",
				    "decoder.embed_positions._float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__						,   a__	:							Optional[int]      						= emb.weight.shape
				a__	:							Union[str, Any]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							str      						= emb.weight.data
				return lin_layer






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=None )	->						Optional[int]:
				a__	:							Union[str, Any]      						= {}
				for old_key in state_dict.keys():
								a__	:							List[Any]      						= old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																a__	:							List[str]      						= key.replace("moe_layer.experts.0"	,				F'ffn.experts.expert_{expert_idx}' )
												else:
																a__	:							Optional[Any]      						= key.replace("moe_layer.experts."	,				"ffn.experts.expert_" )
								if "gate" in key:
												a__	:							Optional[Any]      						= key.replace(".moe_layer.gate.wg"	,				".ffn.router.classifier" )
								if "fc2" and "experts" not in key:
												a__	:							int      						= key.replace(".fc2."	,				".ffn.fc2." )
								if "fc1" and "experts" not in key:
												a__	:							Any      						= key.replace(".fc1."	,				".ffn.fc1." )
								if ".encoder_attn." in key:
												a__	:							str      						= key.replace(".encoder_attn."	,				".cross_attention." )
								if "encoder_attn_layer_norm" in key:
												a__	:							Optional[int]      						= key.replace("encoder_attn_layer_norm"	,				"cross_attention_layer_norm" )
								if "final_layer_norm" in key:
												a__	:							int      						= key.replace("final_layer_norm"	,				"ff_layer_norm" )
								a__	:							Optional[int]      						= state_dict[old_key]
				return new_dict






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Dict:
				a__	:							Any      						= []
				a__	:							List[str]      						= 0
				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )

				for expert in range(__UpperCamelCase ):
								a__	:							Union[str, Any]      						= switch_checkpoint_path + F'-rank-{expert}.pt'
								if os.path.isfile(__UpperCamelCase ):
												a__	:							str      						= torch.load(__UpperCamelCase )["model"]
												remove_ignore_keys_(__UpperCamelCase )
												a__	:							Tuple      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
												a__	:							str      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												torch.save(__UpperCamelCase	,				__UpperCamelCase )
												sharded_state_dicts.append(expert_state.keys() )
												total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
												    expert_state[list(__UpperCamelCase )[0]].dtype )

    # Add the last block
				a__	:							int      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				a__	:							Any      						= torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
				remove_ignore_keys_(__UpperCamelCase )
				a__	:							Optional[Any]      						= rename_fairseq_keys(__UpperCamelCase	,				__UpperCamelCase )
				a__	:							List[Any]      						= shared_weights["decoder.embed_tokens.weight"]
				sharded_state_dicts.append(shared_weights.keys() )

				# If we only have the shared weights (dummy model/experts saved on the same file)
				if len(__UpperCamelCase ) == 1:
								a__	:							Union[str, Any]      						= os.path.join(__UpperCamelCase	,				__UpperCamelCase )
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(__UpperCamelCase	,				__UpperCamelCase )
				# Otherwise, let's build the index
				a__	:							Any      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Any      						= weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )
								a__	:							Optional[Any]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								for key in shard:
												a__	:							Any      						= shard_file

    # Add the metadata
				a__	:							Optional[int]      						= {"total_size": total_size}
				a__	:							Optional[Any]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							Any      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--nllb_moe_checkpoint_path""",
						    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
						    type=str,
						    required=False,
						    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
						)
						parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
						    type=str,
						    required=False,
						    help="""Path to the output pytorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						lowerCamelCase       ,   lowerCamelCase		       =      shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_28,
						    args.dtype,
						)

						lowerCamelCase		       =      NllbMoeConfig.from_pretrained(
						    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						lowerCamelCase		       =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)


