


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__UpperCamelCase				:  Any      					=							collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__UpperCamelCase				:  int      					=							'https://storage.googleapis.com/cvdf-datasets/mnist/'
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      int						=   numpy.dtype(numpy.uintaa       ).newbyteorder('''>'''       )
   return numpy.frombuffer(bytestream.read(4       )			,       dtype=__lowercase       )[0]
@deprecated(__lowercase			,       '''Please use tf.data to implement this functionality.'''       )
def      snake_case_						( __lowercase       ):
   print('''Extracting'''			,       f.name       )
   with gzip.GzipFile(fileobj=__lowercase       ) as bytestream:
      UpperCAmelCase_							:      int						=   _readaa(__lowercase       )
      if magic != 2_0_5_1:
         raise ValueError(
             '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)       )
      UpperCAmelCase_							:      int						=   _readaa(__lowercase       )
      UpperCAmelCase_							:      Optional[Any]						=   _readaa(__lowercase       )
      UpperCAmelCase_							:      str						=   _readaa(__lowercase       )
      UpperCAmelCase_							:      Optional[Any]						=   bytestream.read(rows * cols * num_images       )
      UpperCAmelCase_							:      Any						=   numpy.frombuffer(__lowercase			,       dtype=numpy.uinta       )
      UpperCAmelCase_							:      Optional[int]						=   data.reshape(__lowercase			,       __lowercase			,       __lowercase			,       1       )
      return data
@deprecated(__lowercase			,       '''Please use tf.one_hot on tensors.'''       )
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      str						=   labels_dense.shape[0]
   UpperCAmelCase_							:      Union[str, Any]						=   numpy.arange(__lowercase       ) * num_classes
   UpperCAmelCase_							:      int						=   numpy.zeros((num_labels, num_classes)       )
   UpperCAmelCase_							:      Union[str, Any]						=   1
   return labels_one_hot
@deprecated(__lowercase			,       '''Please use tf.data to implement this functionality.'''       )
def      snake_case_						( __lowercase			,       __lowercase=False			,       __lowercase=1_0       ):
   print('''Extracting'''			,       f.name       )
   with gzip.GzipFile(fileobj=__lowercase       ) as bytestream:
      UpperCAmelCase_							:      Any						=   _readaa(__lowercase       )
      if magic != 2_0_4_9:
         raise ValueError(
             '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)       )
      UpperCAmelCase_							:      Any						=   _readaa(__lowercase       )
      UpperCAmelCase_							:      List[Any]						=   bytestream.read(__lowercase       )
      UpperCAmelCase_							:      Union[str, Any]						=   numpy.frombuffer(__lowercase			,       dtype=numpy.uinta       )
      if one_hot:
         return _dense_to_one_hot(__lowercase			,       __lowercase       )
      return labels





class  lowerCAmelCase__:






    '''simple docstring'''





    @deprecated(
        __snake_case       ,   '''Please use alternatives such as official/mnist/_DataSet.py'''
        ''' from tensorflow/models.'''       ,   )
    def __init__(	self				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   List[Any]=False       ,   __snake_case				:   Any=False       ,   __snake_case				:   str=dtypes.floataa       ,   __snake_case				:   Dict=True       ,   __snake_case				:   Dict=None       ,   ):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   random_seed.get_seed(__snake_case	)
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda	)
       UpperCAmelCase_							:      Optional[int]						=   dtypes.as_dtype(__snake_case	).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
          raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype	)
       if fake_data:
          UpperCAmelCase_							:      Dict						=   10_000
          UpperCAmelCase_							:      Any						=   one_hot
       else:
          assert (
              images.shape[0] == labels.shape[0]
          ), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
          UpperCAmelCase_							:      int						=   images.shape[0]

          # Convert shape from [num examples, rows, columns, depth]
          # to [num examples, rows*columns] (assuming depth == 1)
          if reshape:
             assert images.shape[3] == 1
             UpperCAmelCase_							:      Union[str, Any]						=   images.reshape(
                 images.shape[0]       ,   images.shape[1] * images.shape[2]	)
          if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             UpperCAmelCase_							:      Dict						=   images.astype(numpy.floataa	)
             UpperCAmelCase_							:      Dict						=   numpy.multiply(__snake_case       ,   1.0 / 255.0	)
       UpperCAmelCase_							:      List[Any]						=   images
       UpperCAmelCase_							:      Optional[Any]						=   labels
       UpperCAmelCase_							:      List[Any]						=   0
       UpperCAmelCase_							:      Dict						=   0





    @property
    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       return self._images





    @property
    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       return self._labels





    @property
    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       return self._num_examples





    @property
    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       return self._epochs_completed





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   int=False       ,   __snake_case				:   List[Any]=True	):





       '''simple docstring'''
       if fake_data:
          UpperCAmelCase_							:      List[Any]						=   [1] * 784
          UpperCAmelCase_							:      Any						=   [1] + [0] * 9 if self.one_hot else 0
          return (
              [fake_image for _ in range(__snake_case	)],
              [fake_label for _ in range(__snake_case	)],
          )
       UpperCAmelCase_							:      Dict						=   self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
          UpperCAmelCase_							:      Dict						=   numpy.arange(self._num_examples	)
          numpy.random.shuffle(__snake_case	)
          UpperCAmelCase_							:      Dict						=   self.images[perma]
          UpperCAmelCase_							:      List[Any]						=   self.labels[perma]
       # Go to the next epoch
       if start + batch_size > self._num_examples:
          # Finished epoch
          self._epochs_completed += 1
          # Get the rest examples in this epoch
          UpperCAmelCase_							:      Any						=   self._num_examples - start
          UpperCAmelCase_							:      Tuple						=   self._images[start : self._num_examples]
          UpperCAmelCase_							:      int						=   self._labels[start : self._num_examples]
          # Shuffle the data
          if shuffle:
             UpperCAmelCase_							:      Dict						=   numpy.arange(self._num_examples	)
             numpy.random.shuffle(__snake_case	)
             UpperCAmelCase_							:      Tuple						=   self.images[perm]
             UpperCAmelCase_							:      Optional[int]						=   self.labels[perm]
          # Start next epoch
          UpperCAmelCase_							:      Dict						=   0
          UpperCAmelCase_							:      str						=   batch_size - rest_num_examples
          UpperCAmelCase_							:      Dict						=   self._index_in_epoch
          UpperCAmelCase_							:      Dict						=   self._images[start:end]
          UpperCAmelCase_							:      Optional[int]						=   self._labels[start:end]
          return (
              numpy.concatenate((images_rest_part, images_new_part)       ,   axis=0	),
              numpy.concatenate((labels_rest_part, labels_new_part)       ,   axis=0	),
          )
       else:
          self._index_in_epoch += batch_size
          UpperCAmelCase_							:      List[Any]						=   self._index_in_epoch
          return self._images[start:end], self._labels[start:end]





@deprecated(__lowercase			,       '''Please write your own downloading logic.'''       )
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   if not gfile.Exists(__lowercase       ):
      gfile.MakeDirs(__lowercase       )
   UpperCAmelCase_							:      Tuple						=   os.path.join(__lowercase			,       __lowercase       )
   if not gfile.Exists(__lowercase       ):
      urllib.request.urlretrieve(__lowercase			,       __lowercase       )  # noqa: S310
      with gfile.GFile(__lowercase       ) as f:
         UpperCAmelCase_							:      str						=   f.size()
      print('''Successfully downloaded'''			,       __lowercase			,       __lowercase			,       '''bytes.'''       )
   return filepath


@deprecated(
    __lowercase			,       '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def      snake_case_						( __lowercase			,       __lowercase=False			,       __lowercase=False			,       __lowercase=dtypes.floataa			,       __lowercase=True			,       __lowercase=5_0_0_0			,       __lowercase=None			,       __lowercase=DEFAULT_SOURCE_URL			,       ):
   if fake_data:

      def fake():
         return _DataSet(
             []			,       []			,       fake_data=__lowercase			,       one_hot=__lowercase			,       dtype=__lowercase			,       seed=__lowercase       )

      UpperCAmelCase_							:      str						=   fake()
      UpperCAmelCase_							:      Optional[int]						=   fake()
      UpperCAmelCase_							:      List[str]						=   fake()
      return _Datasets(train=__lowercase			,       validation=__lowercase			,       test=__lowercase       )

   if not source_url:  # empty string check
      UpperCAmelCase_							:      Any						=   DEFAULT_SOURCE_URL

   UpperCAmelCase_							:      int						=   '''train-images-idx3-ubyte.gz'''
   UpperCAmelCase_							:      Tuple						=   '''train-labels-idx1-ubyte.gz'''
   UpperCAmelCase_							:      Any						=   '''t10k-images-idx3-ubyte.gz'''
   UpperCAmelCase_							:      List[Any]						=   '''t10k-labels-idx1-ubyte.gz'''

   UpperCAmelCase_							:      List[Any]						=   _maybe_download(
       __lowercase			,       __lowercase			,       source_url + train_images_file       )
   with gfile.Open(__lowercase			,       '''rb'''       ) as f:
      UpperCAmelCase_							:      Union[str, Any]						=   _extract_images(__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   _maybe_download(
       __lowercase			,       __lowercase			,       source_url + train_labels_file       )
   with gfile.Open(__lowercase			,       '''rb'''       ) as f:
      UpperCAmelCase_							:      List[Any]						=   _extract_labels(__lowercase			,       one_hot=__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   _maybe_download(
       __lowercase			,       __lowercase			,       source_url + test_images_file       )
   with gfile.Open(__lowercase			,       '''rb'''       ) as f:
      UpperCAmelCase_							:      Optional[int]						=   _extract_images(__lowercase       )

   UpperCAmelCase_							:      Tuple						=   _maybe_download(
       __lowercase			,       __lowercase			,       source_url + test_labels_file       )
   with gfile.Open(__lowercase			,       '''rb'''       ) as f:
      UpperCAmelCase_							:      Any						=   _extract_labels(__lowercase			,       one_hot=__lowercase       )

   if not 0 <= validation_size <= len(__lowercase       ):
      UpperCAmelCase_							:      Tuple						=   (
          '''Validation size should be between 0 and '''
          F'''{len(__lowercase       )}. Received: {validation_size}.'''
      )
      raise ValueError(__lowercase       )

   UpperCAmelCase_							:      str						=   train_images[:validation_size]
   UpperCAmelCase_							:      Dict						=   train_labels[:validation_size]
   UpperCAmelCase_							:      int						=   train_images[validation_size:]
   UpperCAmelCase_							:      Optional[Any]						=   train_labels[validation_size:]

   UpperCAmelCase_							:      Any						=   {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

   UpperCAmelCase_							:      Tuple						=   _DataSet(__lowercase			,       __lowercase			,       **__lowercase       )
   UpperCAmelCase_							:      Dict						=   _DataSet(__lowercase			,       __lowercase			,       **__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   _DataSet(__lowercase			,       __lowercase			,       **__lowercase       )

   return _Datasets(train=__lowercase			,       validation=__lowercase			,       test=__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)