


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__UpperCamelCase				:  List[Any]      					=							logging.get_logger(__name__)
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Tuple						=   R'''\w+[.]\d+'''
   UpperCAmelCase_							:      Optional[int]						=   re.findall(__lowercase			,       __lowercase       )
   for pat in pats:
      UpperCAmelCase_							:      Tuple						=   key.replace(__lowercase			,       '''_'''.join(pat.split('''.'''       )       )       )
   return key
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Union[str, Any]						=   pt_tuple_key[:-1] + ('''scale''',)
   if (
       any('''norm''' in str_ for str_ in pt_tuple_key       )
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      UpperCAmelCase_							:      int						=   pt_tuple_key[:-1] + ('''scale''',)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      UpperCAmelCase_							:      Optional[Any]						=   pt_tuple_key[:-1] + ('''scale''',)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      UpperCAmelCase_							:      int						=   pt_tuple_key[:-1] + ('''embedding''',)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   UpperCAmelCase_							:      Tuple						=   pt_tuple_key[:-1] + ('''kernel''',)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      UpperCAmelCase_							:      Tuple						=   pt_tensor.transpose(2			,       3			,       1			,       0       )
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   UpperCAmelCase_							:      List[Any]						=   pt_tuple_key[:-1] + ('''kernel''',)
   if pt_tuple_key[-1] == "weight":
      UpperCAmelCase_							:      str						=   pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   UpperCAmelCase_							:      Union[str, Any]						=   pt_tuple_key[:-1] + ('''weight''',)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   UpperCAmelCase_							:      List[Any]						=   pt_tuple_key[:-1] + ('''bias''',)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor


def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=4_2       ):
   # Step 1: Convert pytorch tensor to numpy
   UpperCAmelCase_							:      str						=   {k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   UpperCAmelCase_							:      Dict						=   flax_model.init_weights(PRNGKey(__lowercase       )       )

   UpperCAmelCase_							:      Optional[int]						=   flatten_dict(__lowercase       )
   UpperCAmelCase_							:      Tuple						=   {}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      UpperCAmelCase_							:      Any						=   rename_key(__lowercase       )
      UpperCAmelCase_							:      int						=   tuple(renamed_pt_key.split('''.'''       )       )

      # Correctly rename weight parameters
      UpperCAmelCase_		,      UpperCAmelCase_							:      List[Any]						=   rename_key_and_reshape_tensor(__lowercase			,       __lowercase			,       __lowercase       )

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''       )

        # also add unexpected weight so that warning is thrown
      UpperCAmelCase_							:      str						=   jnp.asarray(__lowercase       )

   return unflatten_dict(__lowercase       )



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)