


'''simple docstring'''

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 lowerCamelCase      (      lowerCAmelCase    :     Any		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[
	    '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(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					emb.weight.shape
	__magic_name__	:   Any					     =					nn.Linear(lowerCAmelCase		,		lowerCAmelCase		,		bias=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					emb.weight.data
	return lin_layer





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Union[str, Any]=None		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     str = WEIGHTS_NAME		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					[]
	__magic_name__	:   Union[str, Any]					     =					0
	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)

	for expert in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					switch_checkpoint_path + f'-rank-{expert}.pt'
		if os.path.isfile(lowerCAmelCase		):
			__magic_name__	:   int					     =					torch.load(lowerCAmelCase		)['model']
			remove_ignore_keys_(lowerCAmelCase		)
			__magic_name__	:   Optional[Any]					     =					rename_fairseq_keys(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   Dict					     =					os.path.join(
			    lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{len(lowerCAmelCase		)+1:05d}-of-???.bin'		)		)
			torch.save(lowerCAmelCase		,		lowerCAmelCase		)
			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(lowerCAmelCase		)[0]].dtype		)

    # Add the last block
	__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{len(lowerCAmelCase		)+1:05d}-of-???.bin'		)		)
	__magic_name__	:   Optional[Any]					     =					torch.load(switch_checkpoint_path + '-shared.pt'		)['model']
	remove_ignore_keys_(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					rename_fairseq_keys(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					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(lowerCAmelCase		) == 1:
		__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
		torch.save(lowerCAmelCase		,		lowerCAmelCase		)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(lowerCAmelCase		,		lowerCAmelCase		)
	# Otherwise, let's build the index
	__magic_name__	:   List[str]					     =					{}
	for idx, shard in enumerate(lowerCAmelCase		):
		__magic_name__	:   Union[str, Any]					     =					weights_name.replace('.bin'		,		f'-{idx+1:05d}-of-{len(lowerCAmelCase		):05d}.bin'		)
		__magic_name__	:   List[str]					     =					os.path.join(lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{idx+1:05d}-of-???.bin'		)		)
		os.rename(lowerCAmelCase		,		os.path.join(lowerCAmelCase		,		lowerCAmelCase		)		)
		for key in shard:
			__magic_name__	:   int					     =					shard_file

    # Add the metadata
	__magic_name__	:   str					     =					{'total_size': total_size}
	__magic_name__	:   Union[str, Any]					     =					{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(lowerCAmelCase		,		lowerCAmelCase		)		,		'w'		,		encoding='utf-8'		) as f:
		__magic_name__	:   Union[str, Any]					     =					json.dumps(lowerCAmelCase		,		indent=2		,		sort_keys=lowerCAmelCase		) + '\n'
		f.write(lowerCAmelCase		)

	return metadata, index


if __name__ == "__main__":
							lowerCAmelCase				:Any        =      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				:List[str]        =      parser.parse_args()
							lowerCAmelCase				:str        =      shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							lowerCAmelCase				:Dict        =      NllbMoeConfig.from_pretrained(
							    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							lowerCAmelCase				:List[str]        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('''Done''')
							model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

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







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )