






"""simple docstring"""





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						logging.get_logger(__name__)







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Any=False )      ->				List[Any]:
  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.' )
    raise

  if not is_sharded:
    lowerCamelCase_         =						os.path.abspath(_lowerCamelCase )
    logger.info(F'''Loading PyTorch weights from {pt_path}''' )

    lowerCamelCase_         =						torch.load(_lowerCamelCase		,				map_location='cpu' )
    logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.''' )

    lowerCamelCase_         =						convert_pytorch_state_dict_to_flax(_lowerCamelCase		,				_lowerCamelCase )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    lowerCamelCase_         =						convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase		,				_lowerCamelCase )
  return flax_state_dict







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple[str]		,				_lowerCamelCase       :		np.ndarray		,				_lowerCamelCase       :		Dict[str, jnp.ndarray]		,				_lowerCamelCase       :		str		,				)      ->				(Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCamelCase       :		Tuple[str] ) -> bool:
    return len(set(_lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0

  # layer norm
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    lowerCamelCase_         =						pt_tensor.transpose(2		,				3		,				1		,				0 )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCamelCase_         =						pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
    lowerCamelCase_         =						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

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

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  lowerCamelCase_         =						None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    lowerCamelCase_         =						pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    lowerCamelCase_         =						pt_tuple_key[-2] + '_v'
  if name is not None:
    lowerCamelCase_         =						pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Dict )      ->				List[Any]:
  # convert pytorch tensor to numpy
  lowerCamelCase_         =						{k: v.numpy() for k, v in pt_state_dict.items()}

  lowerCamelCase_         =						flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    lowerCamelCase_         =						flax_model.params['params']
  else:
    lowerCamelCase_         =						flax_model.params
  lowerCamelCase_         =						flatten_dict(_lowerCamelCase )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    lowerCamelCase_         =						flatten_dict(flax_model.params['batch_stats'] )
    random_flax_state_dict.update(_lowerCamelCase )

  lowerCamelCase_         =						{}

  lowerCamelCase_         =						(model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
  )
  lowerCamelCase_         =						(model_prefix in flax_model_params) and (
      model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowerCamelCase_         =						tuple(pt_key.split('.' ) )

    # remove base model prefix if necessary
    lowerCamelCase_         =						pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCamelCase_         =						pt_tuple_key[1:]

    # Correctly rename weight parameters
    lowerCamelCase_     ,	lowerCamelCase_         =						rename_key_and_reshape_tensor(
        _lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )

    # add model prefix if necessary
    lowerCamelCase_         =						(model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCamelCase_         =						(model_prefix,) + flax_key

    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}.''' )

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCamelCase		,				_lowerCamelCase )
        continue

      # also add unexpected weight so that warning is thrown
      lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )

    else:
      # also add unexpected weight so that warning is thrown
      lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )

  return unflatten_dict(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		List[str] )      ->				Tuple:
  import torch

  # Load the index
  lowerCamelCase_         =						{}
  for shard_file in shard_filenames:
    # load using msgpack utils
    lowerCamelCase_         =						torch.load(_lowerCamelCase )
    lowerCamelCase_         =						{k: v.numpy() for k, v in pt_state_dict.items()}

    lowerCamelCase_         =						flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      lowerCamelCase_         =						flax_model.params['params']

      lowerCamelCase_         =						flatten_dict(_lowerCamelCase )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats'] ) )
    else:
      lowerCamelCase_         =						flax_model.params
      lowerCamelCase_         =						flatten_dict(_lowerCamelCase )

    lowerCamelCase_         =						(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
    )
    lowerCamelCase_         =						(model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      lowerCamelCase_         =						tuple(pt_key.split('.' ) )

      # remove base model prefix if necessary
      lowerCamelCase_         =						pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        lowerCamelCase_         =						pt_tuple_key[1:]

      # Correctly rename weight parameters
      lowerCamelCase_     ,	lowerCamelCase_         =						rename_key_and_reshape_tensor(
          _lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      # add model prefix if necessary
      lowerCamelCase_         =						(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        lowerCamelCase_         =						(model_prefix,) + flax_key

      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}.''' )

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )
          continue
        if "var" in flax_key[-1]:
          lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCamelCase		,				_lowerCamelCase )
          continue

        # also add unexpected weight so that warning is thrown
        lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )

      else:
        # also add unexpected weight so that warning is thrown
        lowerCamelCase_         =						jnp.asarray(_lowerCamelCase )
  return unflatten_dict(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Union[str, Any] )      ->				Union[str, Any]:
  lowerCamelCase_         =						os.path.abspath(_lowerCamelCase )
  logger.info(F'''Loading Flax weights from {flax_checkpoint_path}''' )

  # import correct flax class
  lowerCamelCase_         =						getattr(_lowerCamelCase		,				'Flax' + model.__class__.__name__ )

  # load flax weight dict
  with open(_lowerCamelCase		,				'rb' ) as state_f:
    try:
      lowerCamelCase_         =						from_bytes(_lowerCamelCase		,				state_f.read() )
    except UnpicklingError:
      raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. ''' )

  return load_flax_weights_in_pytorch_model(_lowerCamelCase		,				_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		str )      ->				int:

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.' )
    raise

  # check if we have bf16 weights
  lowerCamelCase_         =						flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase : x.dtype == jnp.bfloataa		,				_lowerCamelCase ) ).values()
  if any(_lowerCamelCase ):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
        'before loading those in PyTorch model.' )
    lowerCamelCase_         =						jax.tree_util.tree_map(
        lambda _lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params		,				_lowerCamelCase )

  lowerCamelCase_         =						flatten_dict(_lowerCamelCase )
  lowerCamelCase_         =						pt_model.state_dict()

  lowerCamelCase_         =						(pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split('.' )[0] for k in pt_model_dict.keys()}
  )
  lowerCamelCase_         =						(pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split('.' )[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						set(pt_model_dict.keys() )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    lowerCamelCase_         =						flax_key_tuple[0] == pt_model.base_model_prefix
    lowerCamelCase_         =						'.'.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCamelCase_         =						flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCamelCase_         =						(pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCamelCase ) not in pt_model_dict:
      # conv layer
      lowerCamelCase_         =						flax_key_tuple[:-1] + ('weight',)
      lowerCamelCase_         =						jnp.transpose(_lowerCamelCase		,				(3, 2, 0, 1) )
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase ) not in pt_model_dict:
      # linear layer
      lowerCamelCase_         =						flax_key_tuple[:-1] + ('weight',)
      lowerCamelCase_         =						flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      lowerCamelCase_         =						flax_key_tuple[:-1] + ('weight',)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      lowerCamelCase_         =						flax_key_tuple[:-1] + ('running_mean',)
    elif "var" in flax_key_tuple[-1]:
      lowerCamelCase_         =						flax_key_tuple[:-1] + ('running_var',)

    if "batch_stats" in flax_state:
      lowerCamelCase_         =						'.'.join(flax_key_tuple[1:] )  # Remove the params/batch_stats header
    else:
      lowerCamelCase_         =						'.'.join(_lowerCamelCase )

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    lowerCamelCase_         =						{}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      lowerCamelCase_         =						key.split('.' )
      lowerCamelCase_         =						None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        lowerCamelCase_         =						key_components[-2] + '_g'
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        lowerCamelCase_         =						key_components[-2] + '_v'
      if name is not None:
        lowerCamelCase_         =						key_components[:-3] + [name]
        lowerCamelCase_         =						'.'.join(_lowerCamelCase )
        lowerCamelCase_         =						key

    if flax_key in special_pt_names:
      lowerCamelCase_         =						special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
            F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
      else:
        # add weight to pytorch dict
        lowerCamelCase_         =						np.asarray(_lowerCamelCase ) if not isinstance(_lowerCamelCase		,				np.ndarray ) else flax_tensor
        lowerCamelCase_         =						torch.from_numpy(_lowerCamelCase )
        # remove from missing keys
        missing_keys.remove(_lowerCamelCase )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_lowerCamelCase )

  pt_model.load_state_dict(_lowerCamelCase )

  # re-transform missing_keys to list
  lowerCamelCase_         =						list(_lowerCamelCase )

  if len(_lowerCamelCase ) > 0:
    logger.warning(
        'Some weights of the Flax model were not used when initializing the PyTorch model'
        F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
        F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
        ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
        F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
        ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
        ' FlaxBertForSequenceClassification model).' )
  else:
    logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n''' )
  if len(_lowerCamelCase ) > 0:
    logger.warning(
        F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
        F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
        ' use it for predictions and inference.' )
  else:
    logger.warning(
        F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
        'If your task is similar to the task the model of the checkpoint was trained on, '
        F'''you can already use {pt_model.__class__.__name__} for predictions without further training.''' )

  return pt_model












"""simple docstring"""





import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     List[str]						= MobileBertTokenizer
    SCREAMING_SNAKE_CASE						:     int						= MobileBertTokenizerFast
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= True
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= True
    SCREAMING_SNAKE_CASE						:     Dict						= filter_non_english
    SCREAMING_SNAKE_CASE						:     str						= """google/mobilebert-uncased"""






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Dict:
      super().setUp()

      lowerCamelCase_         =						[
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      lowerCamelCase_         =						os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['vocab_file']					)
      with open(self.vocab_file       ,						'w'       ,						encoding='utf-8'					) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

      lowerCamelCase_         =						[
          (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
          for tokenizer_def in self.tokenizers_list
      ]






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		int:
      lowerCamelCase_         =						'UNwant\u00E9d,running'
      lowerCamelCase_         =						'unwanted, running'
      return input_text, output_text






    def    UpperCamelCase       (       self     :							Dict					)     ->		Any:
      lowerCamelCase_         =						self.tokenizer_class(self.vocab_file					)

      lowerCamelCase_         =						tokenizer.tokenize('UNwant\u00E9d,running'					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						['un', '##want', '##ed', ',', 'runn', '##ing']					)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)       ,						[9, 6, 7, 12, 10, 11]					)






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      if not self.test_rust_tokenizer:
        return

      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      lowerCamelCase_         =						'UNwant\u00E9d,running'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      # With lower casing
      lowerCamelCase_         =						self.get_tokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.get_rust_tokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						'UNwant\u00E9d,running'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		str:
      lowerCamelCase_         =						BasicTokenizer()

      self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'					)       ,						['ah', '\u535A', '\u63A8', 'zz']					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)       ,						['hello', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Any:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hällo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['h\u00E9llo']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Union[str, Any]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hallo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		List[str]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['hallo', '!', 'how', 'are', 'you', '?']					)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)       ,						['hello']					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Any:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)       ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Tuple:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['HäLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[Any]:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)       ,						['HaLLo', '!', 'how', 'Are', 'yoU', '?']					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		str:
      lowerCamelCase_         =						BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE       ,						never_split=['[UNK]']					)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'					)       ,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Any:
      lowerCamelCase_         =						['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

      lowerCamelCase_         =						{}
      for i, token in enumerate(__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						i
      lowerCamelCase_         =						WordpieceTokenizer(vocab=__SCREAMING_SNAKE_CASE       ,						unk_token='[UNK]'					)

      self.assertListEqual(tokenizer.tokenize(''					)       ,						[]					)

      self.assertListEqual(tokenizer.tokenize('unwanted running'					)       ,						['un', '##want', '##ed', 'runn', '##ing']					)

      self.assertListEqual(tokenizer.tokenize('unwantedX running'					)       ,						['[UNK]', 'runn', '##ing']					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Any:
      self.assertTrue(_is_whitespace(' '					)					)
      self.assertTrue(_is_whitespace('\t'					)					)
      self.assertTrue(_is_whitespace('\r'					)					)
      self.assertTrue(_is_whitespace('\n'					)					)
      self.assertTrue(_is_whitespace('\u00A0'					)					)

      self.assertFalse(_is_whitespace('A'					)					)
      self.assertFalse(_is_whitespace('-'					)					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      self.assertTrue(_is_control('\u0005'					)					)

      self.assertFalse(_is_control('A'					)					)
      self.assertFalse(_is_control(' '					)					)
      self.assertFalse(_is_control('\t'					)					)
      self.assertFalse(_is_control('\r'					)					)






    def    UpperCamelCase       (       self     :							str					)     ->		Optional[Any]:
      self.assertTrue(_is_punctuation('-'					)					)
      self.assertTrue(_is_punctuation('$'					)					)
      self.assertTrue(_is_punctuation('`'					)					)
      self.assertTrue(_is_punctuation('.'					)					)

      self.assertFalse(_is_punctuation('A'					)					)
      self.assertFalse(_is_punctuation(' '					)					)






    def    UpperCamelCase       (       self     :							int					)     ->		List[Any]:
      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
      self.assertListEqual([tokenizer.tokenize(__SCREAMING_SNAKE_CASE					) for t in ['Test', '\xad', 'test']]       ,						[['[UNK]'], [], ['[UNK]']]					)

      self.assertListEqual(
          [rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					) for t in ['Test', '\xad', 'test']]       ,						[['[UNK]'], [], ['[UNK]']]					)






    @slow
    def    UpperCamelCase       (       self     :							Dict					)     ->		List[str]:
      lowerCamelCase_         =						self.tokenizer_class.from_pretrained('google/mobilebert-uncased'					)

      lowerCamelCase_         =						tokenizer.encode('sequence builders'       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer.encode('multi-sequence build'       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      assert encoded_sentence == [101] + text + [102]
      assert encoded_pair == [101] + text + [102] + text_a + [102]






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Union[str, Any]:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
          lowerCamelCase_         =						tokenizer_r.encode_plus(
              __SCREAMING_SNAKE_CASE       ,						return_attention_mask=__SCREAMING_SNAKE_CASE       ,						return_token_type_ids=__SCREAMING_SNAKE_CASE       ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE       ,						)

          lowerCamelCase_         =						tokenizer_r.do_lower_case if hasattr(__SCREAMING_SNAKE_CASE       ,						'do_lower_case'					) else False
          lowerCamelCase_         =						(
              [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), 'A'),
                  ((1, 2), ','),
                  ((3, 5), 'na'),
                  ((5, 6), '##ï'),
                  ((6, 8), '##ve'),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), 'Allen'),
                  ((21, 23), '##NL'),
                  ((23, 24), '##P'),
                  ((25, 33), 'sentence'),
                  ((33, 34), '.'),
                  ((0, 0), tokenizer_r.sep_token),
              ]
              if not do_lower_case
              else [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), 'a'),
                  ((1, 2), ','),
                  ((3, 8), 'naive'),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), 'allen'),
                  ((21, 23), '##nl'),
                  ((23, 24), '##p'),
                  ((25, 33), 'sentence'),
                  ((33, 34), '.'),
                  ((0, 0), tokenizer_r.sep_token),
              ]
          )

          self.assertEqual(
              [e[1] for e in expected_results]       ,						tokenizer_r.convert_ids_to_tokens(tokens['input_ids']					)					)
          self.assertEqual([e[0] for e in expected_results]       ,						tokens['offset_mapping']					)






    def    UpperCamelCase       (       self     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						['的', '人', '有']
      lowerCamelCase_         =						''.join(__SCREAMING_SNAKE_CASE					)
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						True
          lowerCamelCase_         =						self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_p.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_r.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)

          # it is expected that each Chinese character is not preceded by "##"
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						False
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer_r.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						tokenizer_p.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)

          # it is expected that only the first Chinese character is not preceded by "##".
          lowerCamelCase_         =						[
              F'''##{token}''' if idx != 0 else token for idx, token in enumerate(__SCREAMING_SNAKE_CASE					)
          ]
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)




