





from __future__ import annotations

import numpy as np


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     tuple[np.ndarray, np.ndarray]:
  lowerCamelCase   : Optional[Any]								=np.shape(SCREAMING_SNAKE_CASE_		)
  if rows != columns:
    lowerCamelCase   : Union[str, Any]								=(
        '''\'table\' has to be of square shaped array but got a '''
        F"{rows}x{columns} array:\n{table}"
    )
    raise ValueError(SCREAMING_SNAKE_CASE_		)

  lowerCamelCase   : Dict								=np.zeros((rows, columns)		)
  lowerCamelCase   : Any								=np.zeros((rows, columns)		)
  for i in range(SCREAMING_SNAKE_CASE_		):
    for j in range(SCREAMING_SNAKE_CASE_		):
      lowerCamelCase   : Optional[int]								=sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_		)		)
      if upper[j][j] == 0:
        raise ArithmeticError('''No LU decomposition exists'''		)
      lowerCamelCase   : Optional[Any]								=(table[i][j] - total) / upper[j][j]
    lowerCamelCase   : str								=1
    for j in range(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
      lowerCamelCase   : Any								=sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_		)		)
      lowerCamelCase   : Union[str, Any]								=table[i][j] - total
  return lower, upper


if __name__ == "__main__":
    import doctest

    doctest.testmod()








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
snake_case_							=			{
    '''/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 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		lowerCamelCase   : List[Any]								=list(s_dict.keys()		)
		for key in keys:
				lowerCamelCase   : Dict								=R'''.*/layers_(\d+)'''
				lowerCamelCase   : Optional[int]								=key
				if re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[Any]								=re.sub(R'''layers_(\d+)'''							,       R'''block/\1/layer'''							,       SCREAMING_SNAKE_CASE_		)

				lowerCamelCase   : Dict								=R'''(encoder|decoder)\/'''

				if re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Dict								=re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		).groups()
						if groups[0] == "encoder":
								lowerCamelCase   : Dict								=re.sub(R'''/mlp/'''							,       R'''/1/mlp/'''							,       SCREAMING_SNAKE_CASE_		)
								lowerCamelCase   : Optional[int]								=re.sub(R'''/pre_mlp_layer_norm/'''							,       R'''/1/layer_norm/'''							,       SCREAMING_SNAKE_CASE_		)

						elif groups[0] == "decoder":
								lowerCamelCase   : List[str]								=re.sub(R'''/mlp/'''							,       R'''/2/mlp/'''							,       SCREAMING_SNAKE_CASE_		)
								lowerCamelCase   : str								=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:
								lowerCamelCase   : Dict								=new_key.replace(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

				print(F"{key} -> {new_key}"		)
				lowerCamelCase   : Optional[Any]								=s_dict.pop(SCREAMING_SNAKE_CASE_		)

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

						s_dict.pop(SCREAMING_SNAKE_CASE_		)

		return s_dict


snake_case_							=			{
    '''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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(SCREAMING_SNAKE_CASE_							,       '''r'''		) as f:
				lowerCamelCase   : str								=f.read()

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

		lowerCamelCase   : int								=re.findall(R'''(.*activations) = \(\'(.*)\',\)'''							,       SCREAMING_SNAKE_CASE_		)[0]
		lowerCamelCase   : Any								=str(activation[1]		)

		lowerCamelCase   : Tuple								=num_experts
		lowerCamelCase   : Any								=SwitchTransformersConfig(**SCREAMING_SNAKE_CASE_		)
		return config


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_="./"							,       SCREAMING_SNAKE_CASE_=8		)   ->     Dict:
		# Initialise PyTorch model

		print(F"Loading flax weights from : {flax_checkpoint_path}"		)
		lowerCamelCase   : List[Any]								=checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_		)

		if gin_file is not None:
				lowerCamelCase   : Dict								=convert_gin_to_config(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		else:
				lowerCamelCase   : Optional[int]								=SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Any								=SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : int								=flax_params['''target''']
		lowerCamelCase   : Optional[int]								=flatten_dict(SCREAMING_SNAKE_CASE_							,       sep='''/'''		)
		lowerCamelCase   : str								=rename_keys(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[str]								=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__":
				snake_case_							=			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''')
				snake_case_							=			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,
				)

