
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
UpperCAmelCase	      = {
    '''/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_		(							__SCREAMING_SNAKE_CASE							):
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	lowercase       =    list(s_dict.keys()							)
	for key in keys:
		lowercase       =    r'.*/layers_(\d+)'
		lowercase       =    key
		if re.match(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    re.sub(r'layers_(\d+)' ,							r'block/\1/layer' ,							__SCREAMING_SNAKE_CASE							)

		lowercase       =    r'(encoder|decoder)\/'

		if re.match(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    re.match(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							).groups()
			if groups[0] == "encoder":
				lowercase       =    re.sub(r'/mlp/' ,							r'/1/mlp/' ,							__SCREAMING_SNAKE_CASE							)
				lowercase       =    re.sub(r'/pre_mlp_layer_norm/' ,							r'/1/layer_norm/' ,							__SCREAMING_SNAKE_CASE							)

			elif groups[0] == "decoder":
				lowercase       =    re.sub(r'/mlp/' ,							r'/2/mlp/' ,							__SCREAMING_SNAKE_CASE							)
				lowercase       =    re.sub(r'/pre_mlp_layer_norm/' ,							r'/2/layer_norm/' ,							__SCREAMING_SNAKE_CASE							)

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

		print(F'''{key} -> {new_key}'''							)
		lowercase       =    s_dict.pop(__SCREAMING_SNAKE_CASE							)

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

			s_dict.pop(__SCREAMING_SNAKE_CASE							)

	return s_dict


UpperCAmelCase	      = {
    '''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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# Convert a google style config to the hugging face fromat
	import regex as re

	with open(__SCREAMING_SNAKE_CASE ,							'r'							) as f:
		lowercase       =    f.read()

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

	lowercase       =    re.findall(r'(.*activations) = \(\'(.*)\',\)' ,							__SCREAMING_SNAKE_CASE							)[0]
	lowercase       =    str(activation[1]							)

	lowercase       =    num_experts
	lowercase       =    SwitchTransformersConfig(**__SCREAMING_SNAKE_CASE							)
	return config






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE="./" ,							__SCREAMING_SNAKE_CASE=8							):
	# Initialise PyTorch model

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

	if gin_file is not None:
		lowercase       =    convert_gin_to_config(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	else:
		lowercase       =    SwitchTransformersConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)

	lowercase       =    SwitchTransformersForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

	lowercase       =    flax_params['target']
	lowercase       =    flatten_dict(__SCREAMING_SNAKE_CASE ,							sep='/'							)
	lowercase       =    rename_keys(__SCREAMING_SNAKE_CASE							)
	lowercase       =    unflatten_dict(__SCREAMING_SNAKE_CASE ,							sep='/'							)

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

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


if __name__ == "__main__":
	UpperCAmelCase	      = 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''')
	UpperCAmelCase	      = 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,
	)



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

