

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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
