


"""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 				_snake_case      (   __snake_case							:      Any   ):




   """simple docstring"""







   _lowerCamelCase	:					List[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(__snake_case						, __snake_case   )

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		emb.weight.shape
   _lowerCamelCase	:					Dict   =		nn.Linear(__snake_case						, __snake_case						, bias=__snake_case   )
   _lowerCamelCase	:					Dict   =		emb.weight.data
   return lin_layer

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]=None   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      Union[str, Any]						, __snake_case							:      str						, __snake_case							:      List[str]						, __snake_case							:      List[str]						, __snake_case							:      str = WEIGHTS_NAME   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		[]
   _lowerCamelCase	:					Dict   =		0
   os.makedirs(__snake_case						, exist_ok=__snake_case   )

   for expert in range(__snake_case   ):
      _lowerCamelCase	:					Tuple   =		switch_checkpoint_path + F'-rank-{expert}.pt'
      if os.path.isfile(__snake_case   ):
         _lowerCamelCase	:					int   =		torch.load(__snake_case   )["""model"""]
         remove_ignore_keys_(__snake_case   )
         _lowerCamelCase	:					int   =		rename_fairseq_keys(__snake_case						, __snake_case   )
         _lowerCamelCase	:					Union[str, Any]   =		os.path.join(
             __snake_case						, weights_name.replace(""".bin"""						, F'-{len(__snake_case   )+1:05d}-of-???.bin'   )   )
         torch.save(__snake_case						, __snake_case   )
         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(__snake_case   )[0]].dtype   )

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

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

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

   return metadata, index


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

 UpperCAmelCase				=						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)
 UpperCAmelCase				=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("""Done""")
 model.save_pretrained(args.pytorch_dump_folder_path)



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase			)







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				self.check_over_configs(thresholding=lowercase			)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowercase   ,     prediction_type=lowercase   ,     sample_max_value=lowercase   ,     )







	def  snake_case__     (     self      :Union[str, Any]			)		->      List[Any]:







				"""simple docstring"""






				for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase			)







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

				with self.assertRaises(lowercase   ,     msg='''`custom_timesteps` must be in descending order.'''			):
							scheduler.set_timesteps(timesteps=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				with self.assertRaises(lowercase   ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''			):
							scheduler.set_timesteps(num_inference_steps=lowercase   ,     timesteps=lowercase			)







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    lowercase   ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,     ):
							scheduler.set_timesteps(timesteps=lowercase			)