




class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      0
       lowerCAmelCase__										=      0
       lowerCAmelCase__										=      {}
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		List[Any]						):



       """simple docstring"""
       if vertex not in self.adjacency:
         lowerCAmelCase__										=      {}
         self.num_vertices += 1
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       self.add_vertex(__lowerCamelCase						)
       self.add_vertex(__lowerCamelCase						)

       if head == tail:
         return

       lowerCAmelCase__										=      weight
       lowerCAmelCase__										=      weight
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_edges()
       for edge in edges:
         lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      edge
         edges.remove((tail, head, weight)						)
       for i in range(len(__lowerCamelCase						)						):
         lowerCAmelCase__										=      list(edges[i]						)

       edges.sort(key=lambda __lowerCamelCase						: e[2]						)
       for i in range(len(__lowerCamelCase						) - 1						):
         if edges[i][2] >= edges[i + 1][2]:
           lowerCAmelCase__										=      edges[i][2] + 1
       for edge in edges:
         lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      edge
         lowerCAmelCase__										=      weight
         lowerCAmelCase__										=      weight
     def __str__(						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''''''
       for tail in self.adjacency:
         for head in self.adjacency[tail]:
           lowerCAmelCase__										=      self.adjacency[head][tail]
           string += F"""{head} -> {tail} == {weight}\n"""
       return string.rstrip('''\n'''						)
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      []
       for tail in self.adjacency:
         for head in self.adjacency[tail]:
           output.append((tail, head, self.adjacency[head][tail])						)
       return output
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       return self.adjacency.keys()
     @staticmethod
     def        A__      (						__lowerCamelCase	:		int=None       ,						__lowerCamelCase	:		int=None						):



       """simple docstring"""
       lowerCAmelCase__										=      Graph()
       if vertices is None:
         lowerCAmelCase__										=      []
       if edges is None:
         lowerCAmelCase__										=      []
       for vertex in vertices:
         g.add_vertex(__lowerCamelCase						)
       for edge in edges:
         g.add_edge(*__lowerCamelCase						)
       return g





     class     SCREAMING_SNAKE_CASE__		:
          def __init__(						self	:		List[Any]						):



            """simple docstring"""
            lowerCAmelCase__										=      {}
            lowerCAmelCase__										=      {}
          def __len__(						self	:		str						):



            """simple docstring"""
            return len(self.parent						)
          def        A__      (						self	:		str       ,						__lowerCamelCase	:		Dict						):



            """simple docstring"""
            if item in self.parent:
              return self.find(__lowerCamelCase						)

            lowerCAmelCase__										=      item
            lowerCAmelCase__										=      0
            return item
          def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any						):



            """simple docstring"""
            if item not in self.parent:
              return self.make_set(__lowerCamelCase						)
            if item != self.parent[item]:
              lowerCAmelCase__										=      self.find(self.parent[item]						)
            return self.parent[item]



          def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Union[str, Any]						):



            """simple docstring"""
            lowerCAmelCase__										=      self.find(__lowerCamelCase						)
            lowerCAmelCase__										=      self.find(__lowerCamelCase						)

            if roota == roota:
              return roota

            if self.rank[roota] > self.rank[roota]:
              lowerCAmelCase__										=      roota
              return roota

            if self.rank[roota] < self.rank[roota]:
              lowerCAmelCase__										=      roota
              return roota

            if self.rank[roota] == self.rank[roota]:
              self.rank[roota] += 1
              lowerCAmelCase__										=      roota
              return roota
            return None



     @staticmethod
     def        A__      (						__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      graph.num_vertices

       lowerCAmelCase__										=      Graph.UnionFind()
       lowerCAmelCase__										=      []
       while num_components > 1:
         lowerCAmelCase__										=      {}
         for vertex in graph.get_vertices():
           lowerCAmelCase__										=      -1

         lowerCAmelCase__										=      graph.get_edges()
         for edge in edges:
           lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      edge
           edges.remove((tail, head, weight)						)
         for edge in edges:
           lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      edge
           lowerCAmelCase__										=      union_find.find(__lowerCamelCase						)
           lowerCAmelCase__										=      union_find.find(__lowerCamelCase						)
           if seta != seta:
             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
               lowerCAmelCase__										=      [head, tail, weight]

             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
               lowerCAmelCase__										=      [head, tail, weight]
         for vertex in cheap_edge:
           if cheap_edge[vertex] != -1:
             lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__										=      cheap_edge[vertex]
             if union_find.find(__lowerCamelCase						) != union_find.find(__lowerCamelCase						):
               union_find.union(__lowerCamelCase       ,						__lowerCamelCase						)
               mst_edges.append(cheap_edge[vertex]						)
               lowerCAmelCase__										=      num_components - 1
       lowerCAmelCase__										=      Graph.build(edges=__lowerCamelCase						)
       return mst







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__  :  Tuple					=       data_utils.TransfoXLTokenizer
__magic_name__  :  int					=       data_utils.TransfoXLCorpus
__magic_name__  :  List[str]					=       data_utils
__magic_name__  :  str					=       data_utils
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(__lowerCAmelCase   , '''rb'''				) as fp:
      lowerCAmelCase__										=      pickle.load(__lowerCAmelCase   , encoding='''latin1'''				)
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
    print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""				)
    lowerCAmelCase__										=      corpus.vocab.__dict__
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

    lowerCAmelCase__										=      corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
    print(F"""Save dataset to {pytorch_dataset_dump_path}"""				)
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)

    print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase__										=      TransfoXLConfig()
    else:
      lowerCAmelCase__										=      TransfoXLConfig.from_json_file(__lowerCAmelCase				)
    print(F"""Building PyTorch model from configuration: {config}"""				)
    lowerCAmelCase__										=      TransfoXLLMHeadModel(__lowerCAmelCase				)

    lowerCAmelCase__										=      load_tf_weights_in_transfo_xl(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)
    # Save pytorch-model
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    print(F"""Save PyTorch model to {os.path.abspath(__lowerCAmelCase				)}"""				)
    torch.save(model.state_dict()   , __lowerCAmelCase				)
    print(F"""Save configuration file to {os.path.abspath(__lowerCAmelCase				)}"""				)
    with open(__lowerCAmelCase   , '''w'''   , encoding='''utf-8'''				) as f:
      f.write(config.to_json_string()				)


if __name__ == "__main__":
      __magic_name__  :  int					=       argparse.ArgumentParser()
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
      )
      parser.add_argument(
          """--tf_checkpoint_path""",
          default="""""",
          type=str,
          help="""An optional path to a TensorFlow checkpoint path to be converted.""",
      )
      parser.add_argument(
          """--transfo_xl_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--transfo_xl_dataset_file""",
          default="""""",
          type=str,
          help="""An optional dataset file to be converted in a vocabulary.""",
      )
      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(
          args.tf_checkpoint_path,
          args.transfo_xl_config_file,
          args.pytorch_dump_folder_path,
          args.transfo_xl_dataset_file,
      )

