

'''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 a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= [
	    '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(lowercase__			, lowercase__	)





def a(lowercase__	):





	'''simple docstring'''
	snake_case_		,       snake_case_      		= emb.weight.shape
	snake_case_      		= nn.Linear(lowercase__			, lowercase__			, bias=lowercase__	)
	snake_case_      		= emb.weight.data
	return lin_layer





def a(lowercase__			, lowercase__=None	):





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





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__ = WEIGHTS_NAME	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= 0
	os.makedirs(lowercase__			, exist_ok=lowercase__	)

	for expert in range(lowercase__	):
		snake_case_      		= switch_checkpoint_path + f"""-rank-{expert}.pt"""
		if os.path.isfile(lowercase__	):
			snake_case_      		= torch.load(lowercase__	)['model']
			remove_ignore_keys_(lowercase__	)
			snake_case_      		= rename_fairseq_keys(lowercase__			, lowercase__	)
			snake_case_      		= os.path.join(
			    lowercase__			, weights_name.replace('.bin'			, f"""-{len(lowercase__	)+1:05d}-of-???.bin"""	)	)
			torch.save(lowercase__			, lowercase__	)
			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(lowercase__	)[0]].dtype	)

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

    # Add the metadata
	snake_case_      		= {'total_size': total_size}
	snake_case_      		= {'metadata': metadata, 'weight_map': weight_map}

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

	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)



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= VQModel(
				    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=3 ,				)
				return model


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= CLIPTextConfig(
				    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
				return CLIPTextModel(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= DDIMScheduler()
				snake_case_      		= self.dummy_vq_model

				snake_case_      		= LDMPipeline(unet=__UpperCamelCase ,				vqvae=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy'							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=2 ,				output_type='numpy' ,				return_dict=__UpperCamelCase							)[0]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				snake_case_      		= np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
				ldm.to(__UpperCamelCase							)
				ldm.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= ldm(generator=__UpperCamelCase ,				num_inference_steps=5 ,				output_type='numpy'							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)
				snake_case_      		= np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]							)
				snake_case_      		= 1E-2 if torch_device != 'mps' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
