


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
lowerCAmelCase			=				{
    '/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	):


		"""simple docstring"""




		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


lowerCAmelCase			=				{
    '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	):


		"""simple docstring"""




		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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE="./"		,							SCREAMING_SNAKE_CASE=8	):


		"""simple docstring"""




		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__":
			lowerCAmelCase			=				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')
			lowerCAmelCase			=				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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float		)		->  float:
   if days_between_payments <= 0:
      raise ValueError("""days_between_payments must be > 0"""		)
   if daily_interest_rate < 0:
      raise ValueError("""daily_interest_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)
   return principal * daily_interest_rate * days_between_payments





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_compounding_periods <= 0:
      raise ValueError("""number_of_compounding_periods must be > 0"""		)
   if nominal_annual_interest_rate_percentage < 0:
      raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return principal * (
       (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
       - 1
   )





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_years <= 0:
      raise ValueError("""number_of_years must be > 0"""		)
   if nominal_annual_percentage_rate < 0:
      raise ValueError("""nominal_annual_percentage_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return compound_interest(
       __lowerCAmelCase      ,    nominal_annual_percentage_rate / 3_65      ,    number_of_years * 3_65		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

