






import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase  							=					{
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}





def 						__UpperCAmelCase			(     a_):
							# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
							# the original model
							snake_case_				=							list(s_dict.keys())
							for key in keys:
														snake_case_				=							R""".*/layers_(\d+)"""
														snake_case_				=							key
														if re.match(_lowercase							,     _lowercase):
																					snake_case_				=							re.sub(R'layers_(\d+)'							,     R'block/\1/layer'							,     _lowercase)

														snake_case_				=							R"""(encoder|decoder)\/"""

														if re.match(_lowercase							,     _lowercase):
																					snake_case_				=							re.match(_lowercase							,     _lowercase).groups()
																					if groups[0] == "encoder":
																												snake_case_				=							re.sub(R'/mlp/'							,     R'/1/mlp/'							,     _lowercase)
																												snake_case_				=							re.sub(R'/pre_mlp_layer_norm/'							,     R'/1/layer_norm/'							,     _lowercase)

																					elif groups[0] == "decoder":
																												snake_case_				=							re.sub(R'/mlp/'							,     R'/2/mlp/'							,     _lowercase)
																												snake_case_				=							re.sub(R'/pre_mlp_layer_norm/'							,     R'/2/layer_norm/'							,     _lowercase)

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												snake_case_				=							new_key.replace(_lowercase							,     _lowercase)

														print(f'''{key} -> {new_key}''')
														snake_case_				=							s_dict.pop(_lowercase)

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														snake_case_				=							s_dict[
														    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														snake_case_				=							s_dict[
														    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()):
														if "expert" in key:
																					snake_case_				=							s_dict[key].shape[0]
																					snake_case_				=							s_dict[key]
																					for idx in range(_lowercase):
																												snake_case_				=							expert_weihts[idx]
																												print(f'''{key} -> {key.replace("expert/"							,     "nested fstring")}''')

																					s_dict.pop(_lowercase)

							return s_dict


lowercase  							=					{
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}





def 						__UpperCAmelCase			(     a_							,     a_):
							# Convert a google style config to the hugging face fromat
							import regex as re

							with open(_lowercase							,     'r') as f:
														snake_case_				=							f.read()

							snake_case_				=							re.findall(R'(.*) = ([0-9.]*)'							,     _lowercase)
							snake_case_				=							{}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					snake_case_				=							float(_lowercase) if """.""" in value else int(_lowercase)

							snake_case_				=							re.findall(R'(.*activations) = \(\'(.*)\',\)'							,     _lowercase)[0]
							snake_case_				=							str(activation[1])

							snake_case_				=							num_experts
							snake_case_				=							SwitchTransformersConfig(**_lowercase)
							return config





def 						__UpperCAmelCase			(     a_							,     a_							,     a_=None							,     a_="./"							,     a_=8):
							# Initialise PyTorch model

							print(f'''Loading flax weights from : {flax_checkpoint_path}''')
							snake_case_				=							checkpoints.load_tax_checkpoint(_lowercase)

							if gin_file is not None:
														snake_case_				=							convert_gin_to_config(_lowercase							,     _lowercase)
							else:
														snake_case_				=							SwitchTransformersConfig.from_pretrained(_lowercase)

							snake_case_				=							SwitchTransformersForConditionalGeneration(_lowercase)

							snake_case_				=							flax_params["""target"""]
							snake_case_				=							flatten_dict(_lowercase							,     sep='/')
							snake_case_				=							rename_keys(_lowercase)
							snake_case_				=							unflatten_dict(_lowercase							,     sep='/')

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(_lowercase							,     _lowercase)

							print(f'''Save PyTorch model to {pytorch_dump_path}''')
							pt_model.save_pretrained(_lowercase)


if __name__ == "__main__":
	lowercase  							=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--switch_t5x_checkpoint_path",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
	        " model architecture. If not provided, a `gin_file` has to be provided."
	    ),
	)
	parser.add_argument(
	    "--gin_file",
	    default=None,
	    type=str,
	    required=False,
	    help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
	)
	parser.add_argument(
	    "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
	)
	parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
	lowercase  							=					parser.parse_args()
	convert_flax_checkpoint_to_pytorch(
	    args.switch_tax_checkpoint_path,
	    args.config_name,
	    args.gin_file,
	    args.pytorch_dump_folder_path,
	    args.num_experts,
	)










'''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       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		[
							    """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       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		emb.weight.shape
							UpperCAmelCase     :		Dict							     =		nn.Linear(_lowercase	,				_lowercase	,				bias=_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		emb.weight.data
							return lin_layer



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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = WEIGHTS_NAME		)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Dict							     =		0
							os.makedirs(_lowercase	,				exist_ok=_lowercase		)

							for expert in range(_lowercase		):
														UpperCAmelCase     :		str							     =		switch_checkpoint_path + F'''-rank-{expert}.pt'''
														if os.path.isfile(_lowercase		):
																					UpperCAmelCase     :		Optional[Any]							     =		torch.load(_lowercase		)["""model"""]
																					remove_ignore_keys_(_lowercase		)
																					UpperCAmelCase     :		List[str]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
																					UpperCAmelCase     :		Optional[Any]							     =		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
							UpperCAmelCase     :		Optional[Any]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
							UpperCAmelCase     :		Optional[int]							     =		torch.load(switch_checkpoint_path + """-shared.pt"""		)["""model"""]
							remove_ignore_keys_(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
							UpperCAmelCase     :		int							     =		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:
														UpperCAmelCase     :		Dict							     =		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
							UpperCAmelCase     :		Optional[int]							     =		{}
							for idx, shard in enumerate(_lowercase		):
														UpperCAmelCase     :		List[str]							     =		weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-{len(_lowercase		):05d}.bin'''		)
														UpperCAmelCase     :		List[str]							     =		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:
																					UpperCAmelCase     :		Tuple							     =		shard_file

    # Add the metadata
							UpperCAmelCase     :		Any							     =		{"""total_size""": total_size}
							UpperCAmelCase     :		Optional[Any]							     =		{"""metadata""": metadata, """weight_map""": weight_map}

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

							return metadata, index


if __name__ == "__main__":
							a   :			List[Any]					  =    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   :			int					  =    parser.parse_args()
							a  ,      a   :			Any					  =    shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							a   :			str					  =    NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							a   :			Optional[int]					  =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

