





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__(__snake_case							)				->      List[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		list(s_dict.keys()							)
	for key in keys:
		lowerCamelCase__								=		R'''.*/layers_(\d+)'''
		lowerCamelCase__								=		key
		if re.match(__snake_case       ,__snake_case							):
			lowerCamelCase__								=		re.sub(R'''layers_(\d+)'''       ,R'''block/\1/layer'''       ,__snake_case							)

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

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

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

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

		print(F'{key} -> {new_key}'							)
		lowerCamelCase__								=		s_dict.pop(__snake_case							)

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

			s_dict.pop(__snake_case							)

	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__(__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	import regex as re

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

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

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

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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case=None       ,__snake_case="./"       ,__snake_case=8							)				->      Tuple:







	'''simple docstring'''







	print(F'Loading flax weights from : {flax_checkpoint_path}'							)
	lowerCamelCase__								=		checkpoints.load_tax_checkpoint(__snake_case							)

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

	lowerCamelCase__								=		SwitchTransformersForConditionalGeneration(__snake_case							)

	lowerCamelCase__								=		flax_params['''target''']
	lowerCamelCase__								=		flatten_dict(__snake_case       ,sep='''/'''							)
	lowerCamelCase__								=		rename_keys(__snake_case							)
	lowerCamelCase__								=		unflatten_dict(__snake_case       ,sep='''/'''							)

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

	print(F'Save PyTorch model to {pytorch_dump_path}'							)
	pt_model.save_pretrained(__snake_case							)


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,
			)










_a 						=      65_521





def lowerCAmelCase__(__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		1
	lowerCamelCase__								=		0
	for plain_chr in plain_text:
		lowerCamelCase__								=		(a + ord(__snake_case							)) % MOD_ADLER
		lowerCamelCase__								=		(b + a) % MOD_ADLER
	return (b << 16) | a



