



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 	__a					(   lowerCAmelCase_       : List[str]    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """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 	__a					(   lowerCAmelCase_       : int    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Union[str, Any]=None    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						for old_key in state_dict.keys():
												UpperCAmelCase_=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								UpperCAmelCase_=       key.replace("""moe_layer.experts.0"""      ,F"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								UpperCAmelCase_=       key.replace("""moe_layer.experts."""      ,"""ffn.experts.expert_"""    )
												if "gate" in key:
																		UpperCAmelCase_=       key.replace(""".moe_layer.gate.wg"""      ,""".ffn.router.classifier"""    )
												if "fc2" and "experts" not in key:
																		UpperCAmelCase_=       key.replace(""".fc2."""      ,""".ffn.fc2."""    )
												if "fc1" and "experts" not in key:
																		UpperCAmelCase_=       key.replace(""".fc1."""      ,""".ffn.fc1."""    )
												if ".encoder_attn." in key:
																		UpperCAmelCase_=       key.replace(""".encoder_attn."""      ,""".cross_attention."""    )
												if "encoder_attn_layer_norm" in key:
																		UpperCAmelCase_=       key.replace("""encoder_attn_layer_norm"""      ,"""cross_attention_layer_norm"""    )
												if "final_layer_norm" in key:
																		UpperCAmelCase_=       key.replace("""final_layer_norm"""      ,"""ff_layer_norm"""    )
												UpperCAmelCase_=       state_dict[old_key]
						return new_dict



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str = WEIGHTS_NAME    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						os.makedirs(lowerCAmelCase_      ,exist_ok=lowerCAmelCase_    )

						for expert in range(lowerCAmelCase_    ):
												UpperCAmelCase_=       switch_checkpoint_path + F"""-rank-{expert}.pt"""
												if os.path.isfile(lowerCAmelCase_    ):
																		UpperCAmelCase_=       torch.load(lowerCAmelCase_    )["""model"""]
																		remove_ignore_keys_(lowerCAmelCase_    )
																		UpperCAmelCase_=       rename_fairseq_keys(lowerCAmelCase_      ,lowerCAmelCase_    )
																		UpperCAmelCase_=       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
						UpperCAmelCase_=       os.path.join(lowerCAmelCase_      ,weights_name.replace(""".bin"""      ,F"""-{len(lowerCAmelCase_    )+1:05d}-of-???.bin"""    )    )
						UpperCAmelCase_=       torch.load(switch_checkpoint_path + """-shared.pt"""    )["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						UpperCAmelCase_=       rename_fairseq_keys(lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       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:
												UpperCAmelCase_=       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
						UpperCAmelCase_=       {}
						for idx, shard in enumerate(lowerCAmelCase_    ):
												UpperCAmelCase_=       weights_name.replace(""".bin"""      ,F"""-{idx+1:05d}-of-{len(lowerCAmelCase_    ):05d}.bin"""    )
												UpperCAmelCase_=       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:
																		UpperCAmelCase_=       shard_file

    # Add the metadata
						UpperCAmelCase_=       {"""total_size""": total_size}
						UpperCAmelCase_=       {"""metadata""": metadata, """weight_map""": weight_map}

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

						return metadata, index


if __name__ == "__main__":
	__A       =						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.''',
	)
	__A       =						parser.parse_args()
	__A   ,      __A       =						shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	__A       =						NllbMoeConfig.from_pretrained(
	    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	__A       =						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print('''Done''')
	model.save_pretrained(args.pytorch_dump_folder_path)









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




