
"""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  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  [
						    """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  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase   ,     __lowerCAmelCase     =  emb.weight.shape
						__lowerCAmelCase     =  nn.Linear(_lowerCAmelCase ,    _lowerCAmelCase ,    bias=_lowerCAmelCase						)
						__lowerCAmelCase     =  emb.weight.data
						return lin_layer

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = WEIGHTS_NAME						):
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  0
						os.makedirs(_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)

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

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

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

						return metadata, index


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

			SCREAMING_SNAKE_CASE_					     =	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)
			SCREAMING_SNAKE_CASE_					     =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('''Done''')
			model.save_pretrained(args.pytorch_dump_folder_path)


"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)

						set_param(
						    torch_layer.self_attention.query_key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  np.asarray(weights[3]						)

						set_param(
						    torch_layer.self_attention.query ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  weights[0][1]
						if len(_lowerCAmelCase						) < 4:
												set_layer_weights_in_torch_lsh(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)
						else:
												set_layer_weights_in_torch_local(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)

						# intermediate weighs
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][1]						)
						set_param(
						    torch_block.feed_forward.dense.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate out
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][1]						)
						set_param(
						    torch_block.feed_forward.output.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  np.asarray(weights[3][emb_idx][0]						)
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
						torch.save(model.state_dict() ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
