





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       =				{
    '''/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 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:List[str]       )     ->       Optional[Any]:
 # 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	:			Optional[Any]         =  r".*/layers_(\d+)"
  __lowerCAmelCase	:			List[Any]         =  key
  if re.match(_lowerCAmelCase       ,       _lowerCAmelCase       ):
   __lowerCAmelCase	:			Optional[Any]         =  re.sub(r"""layers_(\d+)"""       ,       r"""block/\1/layer"""       ,       _lowerCAmelCase       )

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

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

   elif groups[0] == "decoder":
    __lowerCAmelCase	:			List[Any]         =  re.sub(r"""/mlp/"""       ,       r"""/2/mlp/"""       ,       _lowerCAmelCase       )
    __lowerCAmelCase	:			Dict         =  re.sub(r"""/pre_mlp_layer_norm/"""       ,       r"""/2/layer_norm/"""       ,       _lowerCAmelCase       )

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    __lowerCAmelCase	:			Union[str, Any]         =  new_key.replace(_lowerCAmelCase       ,       _lowerCAmelCase       )

  print(F'''{key} -> {new_key}'''       )
  __lowerCAmelCase	:			Optional[Any]         =  s_dict.pop(_lowerCAmelCase       )

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

   s_dict.pop(_lowerCAmelCase       )

 return s_dict


_UpperCAmelCase       =				{
    '''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 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:Dict       ,       SCREAMING_SNAKE_CASE						:int       )     ->       Any:
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(_lowerCAmelCase       ,       """r"""       ) as f:
  __lowerCAmelCase	:			Optional[Any]         =  f.read()

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

 __lowerCAmelCase	:			Dict         =  re.findall(r"""(.*activations) = \(\'(.*)\',\)"""       ,       _lowerCAmelCase       )[0]
 __lowerCAmelCase	:			Optional[int]         =  str(activation[1]       )

 __lowerCAmelCase	:			Union[str, Any]         =  num_experts
 __lowerCAmelCase	:			str         =  SwitchTransformersConfig(**_lowerCAmelCase       )
 return config






def 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:Union[str, Any]       ,       SCREAMING_SNAKE_CASE						:List[Any]       ,       SCREAMING_SNAKE_CASE						:str=None       ,       SCREAMING_SNAKE_CASE						:Dict="./"       ,       SCREAMING_SNAKE_CASE						:List[str]=8       )     ->       List[Any]:
 # Initialise PyTorch model

 print(F'''Loading flax weights from : {flax_checkpoint_path}'''       )
 __lowerCAmelCase	:			int         =  checkpoints.load_tax_checkpoint(_lowerCAmelCase       )

 if gin_file is not None:
  __lowerCAmelCase	:			int         =  convert_gin_to_config(_lowerCAmelCase       ,       _lowerCAmelCase       )
 else:
  __lowerCAmelCase	:			List[str]         =  SwitchTransformersConfig.from_pretrained(_lowerCAmelCase       )

 __lowerCAmelCase	:			int         =  SwitchTransformersForConditionalGeneration(_lowerCAmelCase       )

 __lowerCAmelCase	:			Dict         =  flax_params["target"]
 __lowerCAmelCase	:			List[Any]         =  flatten_dict(_lowerCAmelCase       ,       sep="""/"""       )
 __lowerCAmelCase	:			Union[str, Any]         =  rename_keys(_lowerCAmelCase       )
 __lowerCAmelCase	:			str         =  unflatten_dict(_lowerCAmelCase       ,       sep="""/"""       )

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

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


if __name__ == "__main__":
      _UpperCAmelCase       =				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       =				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 collections import defaultdict


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      total  # total no of tasks (N)

        # DP table will have a dimension of (2^M)*N
        # initially all values are set to -1
        A_       :					str        =      [
            [-1 for i in range(total + 1	)] for j in range(2 ** len(snake_case	)	)
        ]

        A_       :					Optional[int]        =      defaultdict(snake_case	)  # stores the list of persons for each task

        # final_mask is used to check if all persons are included by setting all bits
        # to 1
        A_       :					Dict        =      (1 << len(snake_case	)) - 1



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




        if mask == self.final_mask:
           return 1

        # if not everyone gets the task and no more tasks are available, return 0
        if task_no > self.total_tasks:
           return 0

        # if case already considered
        if self.dp[mask][task_no] != -1:
           return self.dp[mask][task_no]

        # Number of ways when we don't this task in the arrangement
        A_       :					Tuple        =      self.count_ways_until(snake_case      ,   task_no + 1	)

        # now assign the tasks one by one to all possible persons and recursively
        # assign for the remaining tasks.
        if task_no in self.task:
           for p in self.task[task_no]:
              # if p is already given a task
              if mask & (1 << p):
                 continue

              # assign this task to p and change the mask value. And recursively
              # assign tasks with the new mask value.
              total_ways_util += self.count_ways_until(mask | (1 << p)      ,   task_no + 1	)

        # save the value.
        A_       :					Any        =      total_ways_util

        return self.dp[mask][task_no]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]	):





        '''simple docstring'''




        for i in range(len(snake_case	)	):
           for j in task_performed[i]:
              self.task[j].append(snake_case	)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
        return self.count_ways_until(0      ,   1	)


if __name__ == "__main__":
 _lowerCAmelCase				:   Dict						       =						5  # total no of tasks (the value of N)

 # the list of tasks that can be done by M persons.
 _lowerCAmelCase				:   str						       =						[[1, 3, 4], [1, 2, 5], [3, 4]]
 print(
     AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
         task_performed
     )
 )
