






"""simple docstring"""



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
A__	:    List[str]			 = {
    '/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 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     Tuple:
							# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
							# the original model
							lowerCamelCase_	:							str									=list(s_dict.keys()  )
							for key in keys:
														lowerCamelCase_	:							int									=r".*/layers_(\d+)"
														lowerCamelCase_	:							Optional[int]									=key
														if re.match(lowerCamelCase__    ,						lowerCamelCase__  ):
																					lowerCamelCase_	:							str									=re.sub(r"layers_(\d+)"    ,						r"block/\1/layer"    ,						lowerCamelCase__  )

														lowerCamelCase_	:							Tuple									=r"(encoder|decoder)\/"

														if re.match(lowerCamelCase__    ,						lowerCamelCase__  ):
																					lowerCamelCase_	:							List[str]									=re.match(lowerCamelCase__    ,						lowerCamelCase__  ).groups()
																					if groups[0] == "encoder":
																												lowerCamelCase_	:							str									=re.sub(r"/mlp/"    ,						r"/1/mlp/"    ,						lowerCamelCase__  )
																												lowerCamelCase_	:							Tuple									=re.sub(r"/pre_mlp_layer_norm/"    ,						r"/1/layer_norm/"    ,						lowerCamelCase__  )

																					elif groups[0] == "decoder":
																												lowerCamelCase_	:							str									=re.sub(r"/mlp/"    ,						r"/2/mlp/"    ,						lowerCamelCase__  )
																												lowerCamelCase_	:							int									=re.sub(r"/pre_mlp_layer_norm/"    ,						r"/2/layer_norm/"    ,						lowerCamelCase__  )

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												lowerCamelCase_	:							List[str]									=new_key.replace(lowerCamelCase__    ,						lowerCamelCase__  )

														print(F"""{key} -> {new_key}"""  )
														lowerCamelCase_	:							Optional[int]									=s_dict.pop(lowerCamelCase__  )

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														lowerCamelCase_	:							Any									=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:
														lowerCamelCase_	:							List[str]									=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:
																					lowerCamelCase_	:							List[str]									=s_dict[key].shape[0]
																					lowerCamelCase_	:							Union[str, Any]									=s_dict[key]
																					for idx in range(lowerCamelCase__  ):
																												lowerCamelCase_	:							List[Any]									=expert_weihts[idx]
																												print(F"""{key} -> {key.replace('expert/'    ,						'nested fstring'  )}"""  )

																					s_dict.pop(lowerCamelCase__  )

							return s_dict


A__	:    Optional[Any]			 = {
    '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 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   int  )    ->     Any:
							# Convert a google style config to the hugging face fromat
							import regex as re

							with open(lowerCamelCase__    ,						"r"  ) as f:
														lowerCamelCase_	:							Optional[Any]									=f.read()

							lowerCamelCase_	:							List[Any]									=re.findall(r"(.*) = ([0-9.]*)"    ,						lowerCamelCase__  )
							lowerCamelCase_	:							Optional[Any]									={}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					lowerCamelCase_	:							List[str]									=float(lowerCamelCase__  ) if "." in value else int(lowerCamelCase__  )

							lowerCamelCase_	:							List[str]									=re.findall(r"(.*activations) = \(\'(.*)\',\)"    ,						lowerCamelCase__  )[0]
							lowerCamelCase_	:							Optional[Any]									=str(activation[1]  )

							lowerCamelCase_	:							Tuple									=num_experts
							lowerCamelCase_	:							List[Any]									=SwitchTransformersConfig(**lowerCamelCase__  )
							return config

def 			_snake_case    (					lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   str=None    ,						lowerCamelCase__     :   int="./"    ,						lowerCamelCase__     :   int=8  )    ->     List[str]:
							# Initialise PyTorch model

							print(F"""Loading flax weights from : {flax_checkpoint_path}"""  )
							lowerCamelCase_	:							Any									=checkpoints.load_tax_checkpoint(lowerCamelCase__  )

							if gin_file is not None:
														lowerCamelCase_	:							str									=convert_gin_to_config(lowerCamelCase__    ,						lowerCamelCase__  )
							else:
														lowerCamelCase_	:							str									=SwitchTransformersConfig.from_pretrained(lowerCamelCase__  )

							lowerCamelCase_	:							List[str]									=SwitchTransformersForConditionalGeneration(lowerCamelCase__  )

							lowerCamelCase_	:							Union[str, Any]									=flax_params["target"]
							lowerCamelCase_	:							Dict									=flatten_dict(lowerCamelCase__    ,						sep="/"  )
							lowerCamelCase_	:							int									=rename_keys(lowerCamelCase__  )
							lowerCamelCase_	:							Dict									=unflatten_dict(lowerCamelCase__    ,						sep="/"  )

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

							print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
							pt_model.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
						A__	:    Dict			 = 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')
						A__	:    str			 = 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"""



from __future__ import annotations

from collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



