





"""simple docstring"""





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__A											=						logging.get_logger(__name__)

__A											=						{"""vocab_file""": """spiece.model"""}

__A											=						{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__A											=						{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:int						    =   VOCAB_FILES_NAMES
       __magic_name__	:Optional[int]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[int]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:List[str]						    =   ["""input_ids""", """attention_mask"""]
       __magic_name__	:List[int]						    =   []






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="[SEP]"       ,					__UpperCAmelCase="[MASK]"       ,					__UpperCAmelCase="[CLS]"       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else bos_token
         lowerCAmelCase__     :Any											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else eos_token
         lowerCAmelCase__     :Optional[int]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else unk_token
         lowerCAmelCase__     :List[Any]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else pad_token
         lowerCAmelCase__     :Tuple											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else cls_token
         lowerCAmelCase__     :str											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowerCAmelCase__     :str											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else mask_token

         lowerCAmelCase__     :List[Any]											=				{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					sep_token=__UpperCAmelCase       ,					mask_token=__UpperCAmelCase       ,					cls_token=__UpperCAmelCase       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Optional[int]											=				vocab_file

         lowerCAmelCase__     :Tuple											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.sp_model.get_piece_size()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				{self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def __getstate__(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.__dict__.copy()
         lowerCAmelCase__     :Union[str, Any]											=				None
         return state






       def __setstate__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				d

         # for backward compatibility
         if not hasattr(self       ,					'sp_model_kwargs' ):
           lowerCAmelCase__     :str											=				{}

         lowerCAmelCase__     :List[Any]											=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.encode(__UpperCAmelCase       ,					out_type=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return self.sp_model.piece_to_id(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.sp_model.IdToPiece(__UpperCAmelCase )
         return token






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				[]
         lowerCAmelCase__     :Dict											=				''
         lowerCAmelCase__     :Tuple											=				False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(__UpperCAmelCase ) + token
             lowerCAmelCase__     :str											=				True
             lowerCAmelCase__     :Any											=				[]
           else:
             current_sub_tokens.append(__UpperCAmelCase )
             lowerCAmelCase__     :List[str]											=				False
         out_string += self.sp_model.decode(__UpperCAmelCase )
         return out_string.strip()






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				kwargs.pop('use_source_tokenizer'       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				self.convert_ids_to_tokens(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         lowerCAmelCase__     :List[str]											=				[]
         lowerCAmelCase__     :List[Any]											=				[]
         for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
             continue
           if token in self.added_tokens_encoder:
             if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )
               lowerCAmelCase__     :List[Any]											=				[]
             sub_texts.append(__UpperCAmelCase )
           else:
             current_sub_text.append(__UpperCAmelCase )
         if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase ) )

         # Mimic the behavior of the Rust tokenizer:
         # No space before [MASK] and [SEP]
         if spaces_between_special_tokens:
           lowerCAmelCase__     :Optional[Any]											=				re.sub(R' (\[(MASK|SEP)\])'       ,					R'\1'       ,					' '.join(__UpperCAmelCase ) )
         else:
           lowerCAmelCase__     :int											=				''.join(__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				(
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
           lowerCAmelCase__     :int											=				self.clean_up_tokenization(__UpperCAmelCase )
           return clean_text
         else:
           return text






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

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

         if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file       ,					__UpperCAmelCase )
         elif not os.path.isfile(self.vocab_file ):
           with open(__UpperCAmelCase       ,					'wb' ) as fi:
             lowerCAmelCase__     :Optional[int]											=				self.sp_model.serialized_model_proto()
             fi.write(__UpperCAmelCase )

         return (out_vocab_file,)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowerCAmelCase__     :Optional[int]											=				[self.cls_token_id]
         lowerCAmelCase__     :Any											=				[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False ):



         '''simple docstring'''

         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__UpperCAmelCase       ,					token_ids_a=__UpperCAmelCase       ,					already_has_special_tokens=__UpperCAmelCase )

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :Dict											=				[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]







"""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


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






         @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name='text' ),) )
         def 	snake_case		(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

           lowerCAmelCase__     :Optional[Any]											=				tf.cast(input_ids_dense > 0       ,					tf.intaa )
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           lowerCAmelCase__     :int											=				self.model(input_ids=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :int											=				[
             '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',
         ]
         lowerCAmelCase__     :str											=				list(zip(self.test_sentences       ,					self.test_sentences[::-1] ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

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

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :List[str]											=				tf_outputs[key].numpy()

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				ModelToSave(tokenizer=__UpperCAmelCase )
           lowerCAmelCase__     :Union[str, Any]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				model.serving(__UpperCAmelCase )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowerCAmelCase__     :Union[str, Any]											=				Path(__UpperCAmelCase ) / 'saved.model'
             tf.saved_model.save(__UpperCAmelCase       ,					__UpperCAmelCase       ,					signatures={'serving_default': model.serving} )
             lowerCAmelCase__     :str											=				tf.saved_model.load(__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				loaded_model.signatures['serving_default'](__UpperCAmelCase )['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 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase )  # Build model with some sample inputs

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
