

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
lowercase_             = {
    '/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   (    A__			:     Any  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =list(s_dict.keys()  )
      for key in keys:
            _lowercase			 =r'.*/layers_(\d+)'
            _lowercase			 =key
            if re.match(__lowercase   ,     __lowercase  ):
                  _lowercase			 =re.sub(r'layers_(\d+)'   ,     r'block/\1/layer'   ,     __lowercase  )

            _lowercase			 =r'(encoder|decoder)\/'

            if re.match(__lowercase   ,     __lowercase  ):
                  _lowercase			 =re.match(__lowercase   ,     __lowercase  ).groups()
                  if groups[0] == "encoder":
                        _lowercase			 =re.sub(r'/mlp/'   ,     r'/1/mlp/'   ,     __lowercase  )
                        _lowercase			 =re.sub(r'/pre_mlp_layer_norm/'   ,     r'/1/layer_norm/'   ,     __lowercase  )

                  elif groups[0] == "decoder":
                        _lowercase			 =re.sub(r'/mlp/'   ,     r'/2/mlp/'   ,     __lowercase  )
                        _lowercase			 =re.sub(r'/pre_mlp_layer_norm/'   ,     r'/2/layer_norm/'   ,     __lowercase  )

        # 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(__lowercase   ,     __lowercase  )

            print(F'''{key} -> {new_key}'''  )
            _lowercase			 =s_dict.pop(__lowercase  )

      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(__lowercase  ):
                        _lowercase			 =expert_weihts[idx]
                        print(F'''{key} -> {key.replace("expert/"   ,     "nested fstring"  )}'''  )

                  s_dict.pop(__lowercase  )

      return s_dict


lowercase_             = {
    '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   (    A__			:     int   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      import regex as re

      with open(__lowercase   ,     'r'  ) as f:
            _lowercase			 =f.read()

      _lowercase			 =re.findall(r'(.*) = ([0-9.]*)'   ,     __lowercase  )
      _lowercase			 ={}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  _lowercase			 =float(__lowercase  ) if '.' in value else int(__lowercase  )

      _lowercase			 =re.findall(r'(.*activations) = \(\'(.*)\',\)'   ,     __lowercase  )[0]
      _lowercase			 =str(activation[1]  )

      _lowercase			 =num_experts
      _lowercase			 =SwitchTransformersConfig(**__lowercase  )
      return config
def   a   (    A__			:     Union[str, Any]   ,     A__			:     int   ,     A__			:     Tuple=None   ,     A__			:     Tuple="./"   ,     A__			:     Tuple=8  )					->   Union[str, Any]:





      """simple docstring"""



      print(F'''Loading flax weights from : {flax_checkpoint_path}'''  )
      _lowercase			 =checkpoints.load_tax_checkpoint(__lowercase  )

      if gin_file is not None:
            _lowercase			 =convert_gin_to_config(__lowercase   ,     __lowercase  )
      else:
            _lowercase			 =SwitchTransformersConfig.from_pretrained(__lowercase  )

      _lowercase			 =SwitchTransformersForConditionalGeneration(__lowercase  )

      _lowercase			 =flax_params['target']
      _lowercase			 =flatten_dict(__lowercase   ,     sep='/'  )
      _lowercase			 =rename_keys(__lowercase  )
      _lowercase			 =unflatten_dict(__lowercase   ,     sep='/'  )

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

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


if __name__ == "__main__":
  lowercase_             = 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')
  lowercase_             = 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,
  )





def   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(A__  )  ):
            if indegree[i] == 0:
                  queue.append(A__  )

      while queue:
            _lowercase			 =queue.pop(0  )
            cnt += 1
            topo.append(A__  )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(A__  )

      if cnt != len(A__  ):
            print('Cycle exists'  )
      else:
            print(A__  )


# Adjacency List of Graph
lowercase_             =     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


