

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    :       Dict        =							{
    "/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__					(		UpperCAmelCase							:      Union[str, Any]      )  ->						Union[str, Any]:
  # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
  # the original model
  UpperCAmelCase	:							str						=			list(s_dict.keys()      )
  for key in keys:
    UpperCAmelCase	:							Optional[Any]						=			r'''.*/layers_(\d+)'''
    UpperCAmelCase	:							Any						=			key
    if re.match(UpperCAmelCase    ,			UpperCAmelCase      ):
      UpperCAmelCase	:							Optional[Any]						=			re.sub(r'''layers_(\d+)'''    ,			r'''block/\1/layer'''    ,			UpperCAmelCase      )

    UpperCAmelCase	:							List[Any]						=			r'''(encoder|decoder)\/'''

    if re.match(UpperCAmelCase    ,			UpperCAmelCase      ):
      UpperCAmelCase	:							int						=			re.match(UpperCAmelCase    ,			UpperCAmelCase      ).groups()
      if groups[0] == "encoder":
        UpperCAmelCase	:							Union[str, Any]						=			re.sub(r'''/mlp/'''    ,			r'''/1/mlp/'''    ,			UpperCAmelCase      )
        UpperCAmelCase	:							List[Any]						=			re.sub(r'''/pre_mlp_layer_norm/'''    ,			r'''/1/layer_norm/'''    ,			UpperCAmelCase      )

      elif groups[0] == "decoder":
        UpperCAmelCase	:							Tuple						=			re.sub(r'''/mlp/'''    ,			r'''/2/mlp/'''    ,			UpperCAmelCase      )
        UpperCAmelCase	:							Optional[Any]						=			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	:							Union[str, Any]						=			new_key.replace(UpperCAmelCase    ,			UpperCAmelCase      )

    print(f'''{key} -> {new_key}'''      )
    UpperCAmelCase	:							Tuple						=			s_dict.pop(UpperCAmelCase      )

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

      s_dict.pop(UpperCAmelCase      )

  return s_dict


_lowerCamelCase    :       List[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       a__					(		UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Union[str, Any]      )  ->						int:
  # Convert a google style config to the hugging face fromat
  import regex as re

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

  UpperCAmelCase	:							int						=			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	:							List[str]						=			re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    ,			UpperCAmelCase      )[0]
  UpperCAmelCase	:							List[str]						=			str(activation[1]      )

  UpperCAmelCase	:							Union[str, Any]						=			num_experts
  UpperCAmelCase	:							List[str]						=			SwitchTransformersConfig(**UpperCAmelCase      )
  return config

def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Union[str, Any]=None    ,			UpperCAmelCase							:      int="./"    ,			UpperCAmelCase							:      Optional[int]=8      )  ->						Optional[Any]:
  # Initialise PyTorch model

  print(f'''Loading flax weights from : {flax_checkpoint_path}'''      )
  UpperCAmelCase	:							Optional[int]						=			checkpoints.load_tax_checkpoint(UpperCAmelCase      )

  if gin_file is not None:
    UpperCAmelCase	:							Any						=			convert_gin_to_config(UpperCAmelCase    ,			UpperCAmelCase      )
  else:
    UpperCAmelCase	:							int						=			SwitchTransformersConfig.from_pretrained(UpperCAmelCase      )

  UpperCAmelCase	:							List[str]						=			SwitchTransformersForConditionalGeneration(UpperCAmelCase      )

  UpperCAmelCase	:							Optional[int]						=			flax_params['''target''']
  UpperCAmelCase	:							List[str]						=			flatten_dict(UpperCAmelCase    ,			sep='''/'''      )
  UpperCAmelCase	:							Optional[Any]						=			rename_keys(UpperCAmelCase      )
  UpperCAmelCase	:							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__":
     _lowerCamelCase    :       Dict        =							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    :       Optional[Any]        =							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,
     )








from math import log

from scipy.constants import Boltzmann, physical_constants

_lowerCamelCase    :       Tuple        =							3_0_0  # TEMPERATURE (unit = K)

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			)  ->						float:

  if donor_conc <= 0:
    raise ValueError('''Donor concentration should be positive'''      )
  elif acceptor_conc <= 0:
    raise ValueError('''Acceptor concentration should be positive'''      )
  elif intrinsic_conc <= 0:
    raise ValueError('''Intrinsic concentration should be positive'''      )
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        '''Donor concentration should be greater than intrinsic concentration'''      )
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        '''Acceptor concentration should be greater than intrinsic concentration'''      )
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2      )
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





