





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 					lowercase__    (					lowercase_			)					->		Dict:




      """simple docstring"""

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

            _UpperCamelCase      :	Optional[int]						=       r"(encoder|decoder)\/"

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

                  elif groups[0] == "decoder":
                        _UpperCamelCase      :	List[Any]						=       re.sub(r"/mlp/"		,r"/2/mlp/"		,A_			)
                        _UpperCamelCase      :	Tuple						=       re.sub(r"/pre_mlp_layer_norm/"		,r"/2/layer_norm/"		,A_			)

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

            print(F'''{key} -> {new_key}'''			)
            _UpperCamelCase      :	Union[str, Any]						=       s_dict.pop(A_			)

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

                  s_dict.pop(A_			)

      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 					lowercase__    (					lowercase_		,lowercase_			)					->		Optional[int]:




      """simple docstring"""

      import regex as re

      with open(A_		,"r"			) as f:
            _UpperCamelCase      :	List[str]						=       f.read()

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

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

      _UpperCamelCase      :	List[str]						=       num_experts
      _UpperCamelCase      :	Tuple						=       SwitchTransformersConfig(**A_			)
      return config




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=None		,lowercase_="./"		,lowercase_=8			)					->		str:




      """simple docstring"""

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

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

      _UpperCamelCase      :	int						=       SwitchTransformersForConditionalGeneration(A_			)

      _UpperCamelCase      :	Optional[Any]						=       flax_params["target"]
      _UpperCamelCase      :	Optional[Any]						=       flatten_dict(A_		,sep="/"			)
      _UpperCamelCase      :	Union[str, Any]						=       rename_keys(A_			)
      _UpperCamelCase      :	Dict						=       unflatten_dict(A_		,sep="/"			)

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

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


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"""


def 					lowercase__    (					lowercase_		,lowercase_			)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       len(lowercase_			)
      print("The following activities are selected:"			)

      # The first activity is always selected
      _UpperCamelCase      :	List[Any]						=       0
      print(lowercase_		,end=","			)

      # Consider rest of the activities
      for j in range(lowercase_			):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase_		,end=","			)
                  _UpperCamelCase      :	Optional[Any]						=       j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase__				  =   [1, 3, 0, 5, 8, 5]
  lowerCamelCase__				  =   [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)
