

'''simple docstring'''






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{"vocab_file": "vocab.txt"}

lowerCAmelCase__      		=			{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

lowerCAmelCase__      		=			{
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}


def 	_A  (					A__   ):






 """simple docstring"""



 with open(A__  ,       '''r'''   ) as f:
  __lowercase													=				f.read().splitlines()
  return [l.strip() for l in lines]






class     lowercase_			(UpperCAmelCase_   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						int           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						List[str]           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						List[str]           =    ['input_ids', 'attention_mask']






  def __init__(   self     :  Optional[int]      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]="<unk>"      ,lowercase__     :  List[Any]="<cls>"      ,lowercase__     :  str="<pad>"      ,lowercase__     :  Tuple="<mask>"      ,lowercase__     :  int="<eos>"      ,**lowercase__     :  List[Any]      ,):
   super().__init__(**_lowercase    )
   __lowercase													=				load_vocab_file(_lowercase    )
   __lowercase													=				dict(enumerate(self.all_tokens    )    )
   __lowercase													=				{tok: ind for ind, tok in enumerate(self.all_tokens    )}
   __lowercase													=				unk_token
   __lowercase													=				cls_token
   __lowercase													=				pad_token
   __lowercase													=				mask_token
   __lowercase													=				eos_token
   __lowercase													=				self.all_tokens
   self._create_trie(self.unique_no_split_tokens    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Optional[int]    ):
   return self._id_to_token.get(_lowercase      ,self.unk_token    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Union[str, Any]    ):
   return self._token_to_id.get(_lowercase      ,self._token_to_id.get(self.unk_token    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Dict      ,**lowercase__     :  Tuple    ):
   return text.split()






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Optional[Any]=False    ):
   return len(self._id_to_token    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return {token: i for i, token in enumerate(self.all_tokens    )}






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[str]    ):
   return self._token_to_id.get(_lowercase      ,self._token_to_id.get(self.unk_token    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  Tuple    ):
   return self._id_to_token.get(_lowercase      ,self.unk_token    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[int] = None    ):
   __lowercase													=				[self.cls_token_id]
   __lowercase													=				[self.eos_token_id]  # No sep token in ESM vocabulary
   if token_ids_a is None:
    if self.eos_token_id is None:
     return cls + token_ids_a
    else:
     return cls + token_ids_a + sep
   elif self.eos_token_id is None:
    raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!'''    )
   return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Any = None      ,lowercase__     :  Dict = False    ):
   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         '''You should not supply a second sequence if the provided sequence of '''
         '''ids is already formatted with special tokens for the model.'''    )

    return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
   __lowercase													=				[1] + ([0] * len(_lowercase    )) + [1]
   if token_ids_a is not None:
    mask += [0] * len(_lowercase    ) + [1]
   return mask






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  int      ,lowercase__     :  Dict    ):
   __lowercase													=				os.path.join(_lowercase      ,(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''    )
   with open(_lowercase      ,'''w'''    ) as f:
    f.write('''\n'''.join(self.all_tokens    )    )
   return (vocab_file,)






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   return self.get_vocab_size(with_added_tokens=_lowercase    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  str      ,lowercase__     :  Optional[int] = False    ):
   return super()._add_tokens(_lowercase      ,special_tokens=_lowercase    )




'''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

