

'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_albert import AlbertTokenizer
else:
 lowerCAmelCase__      		=			None

lowerCAmelCase__      		=			logging.get_logger(__name__)
lowerCAmelCase__      		=			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase__      		=			{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

lowerCAmelCase__      		=			'''▁'''






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Tuple           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Dict           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Tuple           =    AlbertTokenizer






  def __init__(   self     :  Any      ,lowercase__     :  Tuple=None      ,lowercase__     :  str=None      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Optional[Any]=True      ,lowercase__     :  Optional[Any]=False      ,lowercase__     :  Optional[int]="[CLS]"      ,lowercase__     :  Optional[Any]="[SEP]"      ,lowercase__     :  Any="<unk>"      ,lowercase__     :  Union[str, Any]="[SEP]"      ,lowercase__     :  Optional[int]="<pad>"      ,lowercase__     :  Tuple="[CLS]"      ,lowercase__     :  int="[MASK]"      ,**lowercase__     :  List[Any]      ,):
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   __lowercase													=				(
       AddedToken(lowercase__      ,lstrip=lowercase__      ,rstrip=lowercase__      ,normalized=lowercase__    )
       if isinstance(lowercase__      ,lowercase__    )
       else mask_token
   )

   super().__init__(
       lowercase__      ,tokenizer_file=lowercase__      ,do_lower_case=lowercase__      ,remove_space=lowercase__      ,keep_accents=lowercase__      ,bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,pad_token=lowercase__      ,cls_token=lowercase__      ,mask_token=lowercase__      ,**lowercase__      ,)

   __lowercase													=				do_lower_case
   __lowercase													=				remove_space
   __lowercase													=				keep_accents
   __lowercase													=				vocab_file
   __lowercase													=				False if not self.vocab_file else True






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep    ) * [0]
   return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
        '''tokenizer.'''    )

   if not os.path.isdir(lowercase__    ):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

   if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase__    ):
    copyfile(self.vocab_file      ,lowercase__    )

   return (out_vocab_file,)




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

