



"""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    =			{
    '/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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
							# the original model
							A         =					list(s_dict.keys()			)
							for key in keys:
														A         =					R'.*/layers_(\d+)'
														A         =					key
														if re.match(lowerCamelCase__				,      lowerCamelCase__			):
																					A         =					re.sub(R'layers_(\d+)'				,      R'block/\1/layer'				,      lowerCamelCase__			)

														A         =					R'(encoder|decoder)\/'

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

																					elif groups[0] == "decoder":
																												A         =					re.sub(R'/mlp/'				,      R'/2/mlp/'				,      lowerCamelCase__			)
																												A         =					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:
																												A         =					new_key.replace(lowerCamelCase__				,      lowerCamelCase__			)

														print(f"""{key} -> {new_key}"""			)
														A         =					s_dict.pop(lowerCamelCase__			)

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

																					s_dict.pop(lowerCamelCase__			)

							return s_dict


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

							with open(lowerCamelCase__				,      'r'			) as f:
														A         =					f.read()

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

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

							A         =					num_experts
							A         =					SwitchTransformersConfig(**lowerCamelCase__			)
							return config

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

							print(f"""Loading flax weights from : {flax_checkpoint_path}"""			)
							A         =					checkpoints.load_tax_checkpoint(lowerCamelCase__			)

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

							A         =					SwitchTransformersForConditionalGeneration(lowerCamelCase__			)

							A         =					flax_params['target']
							A         =					flatten_dict(lowerCamelCase__				,      sep='/'			)
							A         =					rename_keys(lowerCamelCase__			)
							A         =					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    =			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    =			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 re

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list:
							return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'				,      str_			)]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					split_input(str_			)
							return "".join(
							    [''.join([char.capitalize() for char in sub_str]			) for sub_str in string_split]			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							try:
														A         =					split_input(lowerCamelCase__			)
														if upper:
																					A         =					''.join(
																					    [
																					        separator.join([char.upper() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														else:
																					A         =					''.join(
																					    [
																					        separator.join([char.lower() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														return res_str
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							return to_simple_case(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							try:
														A         =					to_simple_case(lowerCamelCase__			)
														return res_str[0].lower() + res_str[1:]
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '_'			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '-'			)


if __name__ == "__main__":
							__import__('doctest').testmod()



