


"""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
UpperCAmelCase    :     str   						=		{
    '/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       (     _UpperCamelCase		:      List[str]							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[str]      							=					list(s_dict.keys()							)
							for key in keys:
														__UpperCAmelCase   :						List[Any]      							=					R""".*/layers_(\d+)"""
														__UpperCAmelCase   :						int      							=					key
														if re.match(_UpperCamelCase       ,			_UpperCamelCase							):
																					__UpperCAmelCase   :						Tuple      							=					re.sub(R"""layers_(\d+)"""       ,			R"""block/\1/layer"""       ,			_UpperCamelCase							)

														__UpperCAmelCase   :						Union[str, Any]      							=					R"""(encoder|decoder)\/"""

														if re.match(_UpperCamelCase       ,			_UpperCamelCase							):
																					__UpperCAmelCase   :						Any      							=					re.match(_UpperCamelCase       ,			_UpperCamelCase							).groups()
																					if groups[0] == "encoder":
																												__UpperCAmelCase   :						int      							=					re.sub(R"""/mlp/"""       ,			R"""/1/mlp/"""       ,			_UpperCamelCase							)
																												__UpperCAmelCase   :						int      							=					re.sub(R"""/pre_mlp_layer_norm/"""       ,			R"""/1/layer_norm/"""       ,			_UpperCamelCase							)

																					elif groups[0] == "decoder":
																												__UpperCAmelCase   :						Any      							=					re.sub(R"""/mlp/"""       ,			R"""/2/mlp/"""       ,			_UpperCamelCase							)
																												__UpperCAmelCase   :						Dict      							=					re.sub(R"""/pre_mlp_layer_norm/"""       ,			R"""/2/layer_norm/"""       ,			_UpperCamelCase							)

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												__UpperCAmelCase   :						Optional[Any]      							=					new_key.replace(_UpperCamelCase       ,			_UpperCamelCase							)

														print(f'''{key} -> {new_key}'''							)
														__UpperCAmelCase   :						List[str]      							=					s_dict.pop(_UpperCamelCase							)

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

																					s_dict.pop(_UpperCamelCase							)

							return s_dict


UpperCAmelCase    :     Union[str, Any]   						=		{
    '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       (     _UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Dict							)		->       List[str]:

							'''simple docstring'''



							import regex as re

							with open(_UpperCamelCase       ,			"""r"""							) as f:
														__UpperCAmelCase   :						Tuple      							=					f.read()

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

							__UpperCAmelCase   :						Tuple      							=					re.findall(R"""(.*activations) = \(\'(.*)\',\)"""       ,			_UpperCamelCase							)[0]
							__UpperCAmelCase   :						List[str]      							=					str(activation[1]							)

							__UpperCAmelCase   :						Any      							=					num_experts
							__UpperCAmelCase   :						Union[str, Any]      							=					SwitchTransformersConfig(**_UpperCamelCase							)
							return config
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      List[Any]=None       ,			_UpperCamelCase		:      Any="./"       ,			_UpperCamelCase		:      Tuple=8							)		->       Optional[Any]:

							'''simple docstring'''



							print(f'''Loading flax weights from : {flax_checkpoint_path}'''							)
							__UpperCAmelCase   :						Any      							=					checkpoints.load_tax_checkpoint(_UpperCamelCase							)

							if gin_file is not None:
														__UpperCAmelCase   :						List[Any]      							=					convert_gin_to_config(_UpperCamelCase       ,			_UpperCamelCase							)
							else:
														__UpperCAmelCase   :						Optional[int]      							=					SwitchTransformersConfig.from_pretrained(_UpperCamelCase							)

							__UpperCAmelCase   :						List[Any]      							=					SwitchTransformersForConditionalGeneration(_UpperCamelCase							)

							__UpperCAmelCase   :						Optional[int]      							=					flax_params["""target"""]
							__UpperCAmelCase   :						List[str]      							=					flatten_dict(_UpperCamelCase       ,			sep="""/"""							)
							__UpperCAmelCase   :						int      							=					rename_keys(_UpperCamelCase							)
							__UpperCAmelCase   :						Optional[Any]      							=					unflatten_dict(_UpperCamelCase       ,			sep="""/"""							)

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

							print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
							pt_model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Tuple   						=		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    :     List[str]   						=		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"""






def      lowerCamelCase       (     _UpperCamelCase		:      float       ,			_UpperCamelCase		:      float       ,			_UpperCamelCase		:      int							)		->       float:

							'''simple docstring'''



							if principal <= 0:
														raise Exception("""Principal borrowed must be > 0"""							)
							if rate_per_annum < 0:
														raise Exception("""Rate of interest must be >= 0"""							)
							if years_to_repay <= 0 or not isinstance(_UpperCamelCase       ,			_UpperCamelCase							):
														raise Exception("""Years to repay must be an integer > 0"""							)

							# Yearly rate is divided by 12 to get monthly rate
							__UpperCAmelCase   :						List[Any]      							=					rate_per_annum / 1_2

							# Years to repay is multiplied by 12 to get number of payments as payment is monthly
							__UpperCAmelCase   :						Optional[int]      							=					years_to_repay * 1_2

							return (
							    principal
							    * rate_per_month
							    * (1 + rate_per_month) ** number_of_payments
							    / ((1 + rate_per_month) ** number_of_payments - 1)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
