





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		XLMTokenizer
 UpperCAmelCase__													=		False
 def      A_	(      self  :      str	)			->	List[str]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase__    :      Any					 =		[
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         'w</w>',
         'r</w>',
         't</w>',
         'lo',
         'low',
         'er</w>',
         'low</w>',
         'lowest</w>',
         'newer</w>',
         'wider</w>',
         '<unk>',
     ]
     lowerCamelCase__    :      List[str]					 =		dict(zip(UpperCAmelCase			,     range(len(UpperCAmelCase	)	)	)	)
     lowerCamelCase__    :      Tuple					 =		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

     lowerCamelCase__    :      Optional[Any]					 =		os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']	)
     lowerCamelCase__    :      int					 =		os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']	)
     with open(self.vocab_file			,     'w'	) as fp:
         fp.write(json.dumps(UpperCAmelCase	)	)
     with open(self.merges_file			,     'w'	) as fp:
         fp.write('\n'.join(UpperCAmelCase	)	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Any:
     lowerCamelCase__    :      Tuple					 =		'lower newer'
     lowerCamelCase__    :      Tuple					 =		'lower newer'
     return input_text, output_text
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     lowerCamelCase__    :      Dict					 =		XLMTokenizer(self.vocab_file			,     self.merges_file	)

     lowerCamelCase__    :      Tuple					 =		'lower'
     lowerCamelCase__    :      Any					 =		['low', 'er</w>']
     lowerCamelCase__    :      Dict					 =		tokenizer.tokenize(UpperCAmelCase	)
     self.assertListEqual(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		tokens + ['<unk>']
     lowerCamelCase__    :      Optional[int]					 =		[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase	)			,     UpperCAmelCase	)







 @slow
 def      A_	(      self  :      Optional[Any]	)			->	List[Any]:
     lowerCamelCase__    :      str					 =		XLMTokenizer.from_pretrained('xlm-mlm-en-2048'	)

     lowerCamelCase__    :      List[str]					 =		tokenizer.encode('sequence builders'			,     add_special_tokens=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		tokenizer.encode('multi-sequence build'			,     add_special_tokens=UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,     UpperCAmelCase	)

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]












"""simple docstring"""




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


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[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)
  ):
    A_  :       Union[str, Any]										=  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:
    A_  :       List[str]										=  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:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

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

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

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

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    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
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)