




'''simple docstring'''







import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.bert.modeling_flax_bert import (
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForNextSentencePrediction,
           FlaxBertForPreTraining,
           FlaxBertForQuestionAnswering,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertModel,
       )
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=7      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=99      ,	snake_case__=32      ,	snake_case__=5      ,	snake_case__=4      ,	snake_case__=37      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=16      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=4      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       parent
            _lowerCAmelCase    :							Union[str, Any]   			=       batch_size
            _lowerCAmelCase    :							Union[str, Any]   			=       seq_length
            _lowerCAmelCase    :							Any   			=       is_training
            _lowerCAmelCase    :							List[Any]   			=       use_attention_mask
            _lowerCAmelCase    :							Any   			=       use_token_type_ids
            _lowerCAmelCase    :							Optional[Any]   			=       use_labels
            _lowerCAmelCase    :							Tuple   			=       vocab_size
            _lowerCAmelCase    :							Union[str, Any]   			=       hidden_size
            _lowerCAmelCase    :							int   			=       num_hidden_layers
            _lowerCAmelCase    :							List[Any]   			=       num_attention_heads
            _lowerCAmelCase    :							Union[str, Any]   			=       intermediate_size
            _lowerCAmelCase    :							List[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[Any]   			=       hidden_dropout_prob
            _lowerCAmelCase    :							int   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							int   			=       max_position_embeddings
            _lowerCAmelCase    :							Optional[int]   			=       type_vocab_size
            _lowerCAmelCase    :							Dict   			=       type_sequence_label_size
            _lowerCAmelCase    :							List[str]   			=       initializer_range
            _lowerCAmelCase    :							Optional[Any]   			=       num_choices







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

            _lowerCAmelCase    :							Dict   			=       None
            if self.use_attention_mask:
                   _lowerCAmelCase    :							List[Any]   			=       random_attention_mask([self.batch_size, self.seq_length]   )

            _lowerCAmelCase    :							Any   			=       None
            if self.use_token_type_ids:
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

            _lowerCAmelCase    :							Tuple   			=       BertConfig(
                vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=snake_case__      ,	initializer_range=self.initializer_range      ,	)

            return config, input_ids, token_type_ids, attention_mask







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase    :							Dict   			=       config_and_inputs
            _lowerCAmelCase    :							Optional[Any]   			=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
            return config, inputs_dict







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase    :							Any   			=       config_and_inputs

            _lowerCAmelCase    :							Optional[Any]   			=       True
            _lowerCAmelCase    :							Dict   			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
            _lowerCAmelCase    :							int   			=       ids_tensor([self.batch_size, self.seq_length]      ,	vocab_size=2   )

            return (
                config,
                input_ids,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
            )
@require_flax
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    True

     __magic_name__    			=    (
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       FlaxBertModelTester(self   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FlaxBertModel.from_pretrained('bert-base-cased'   )
            _lowerCAmelCase    :							List[Any]   			=       model(np.ones((1, 1)   )   )
            self.assertIsNotNone(snake_case__   )





'''simple docstring'''







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

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase    :     int					=						logging.get_logger(__name__)
lowerCAmelCase    :     Union[str, Any]					=						{"""vocab_file""": """spiece.model"""}

lowerCAmelCase    :     Optional[int]					=						{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowerCAmelCase    :     Union[str, Any]					=						{
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    ["input_ids", "attention_mask"]







     def __init__( self      ,	snake_case__      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       {} if sp_model_kwargs is None else sp_model_kwargs

            _lowerCAmelCase    :							List[Any]   			=       kwargs.get('name_or_path'   )
            if name_or_path is None:
                   logger.warning(
                       'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                       ' you are testing the model, this can safely be ignored'   )
                   _lowerCAmelCase    :							Any   			=       'None'

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            _lowerCAmelCase    :							str   			=       '<|endoftext|>' if eos_token is None else eos_token
            _lowerCAmelCase    :							Tuple   			=       '<unk>' if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                   _lowerCAmelCase    :							List[str]   			=       unk_token if pad_token is None else pad_token
                   _lowerCAmelCase    :							Optional[int]   			=       eos_token if bos_token is None else bos_token
            else:
                   _lowerCAmelCase    :							Tuple   			=       '<pad>' if pad_token is None else pad_token
                   _lowerCAmelCase    :							Union[str, Any]   			=       '<s>' if bos_token is None else bos_token

            super().__init__(
                do_lower_case=snake_case__      ,	remove_space=snake_case__      ,	keep_accents=snake_case__      ,	bos_token=snake_case__      ,	eos_token=snake_case__      ,	unk_token=snake_case__      ,	pad_token=snake_case__      ,	sp_model_kwargs=self.sp_model_kwargs      ,	**snake_case__      ,	)

            _lowerCAmelCase    :							Union[str, Any]   			=       do_lower_case
            _lowerCAmelCase    :							Optional[int]   			=       remove_space
            _lowerCAmelCase    :							Any   			=       keep_accents
            _lowerCAmelCase    :							Optional[int]   			=       vocab_file

            _lowerCAmelCase    :							Dict   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(snake_case__   )

            # Used for whitespace normalization in input texts
            # fmt : off
            _lowerCAmelCase    :							Optional[Any]   			=       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            _lowerCAmelCase    :							Optional[Any]   			=       re.compile(
                F'[{"".join(map(snake_case__      ,	list(range(0      ,	9   )   ) + list(range(11      ,	32   )   ) + list(range(127      ,	160   )   ) + [160, 173, 8203]   )   )}]'   )







     def __getstate__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.__dict__.copy()
            _lowerCAmelCase    :							int   			=       None
            return state







     def __setstate__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       d

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

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







     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def a	( self   ):

            '''simple docstring'''

            return len(self.sp_model   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.non_printing_characters_re.sub(''      ,	snake_case__   )

            # Normalize whitespaces
            _lowerCAmelCase    :							Tuple   			=       ''.join([char if char not in self.whitespaces else ' ' for char in text]   )

            # NFC Unicode normalization
            _lowerCAmelCase    :							Union[str, Any]   			=       unicodedata.normalize('NFC'      ,	snake_case__   )
            return text







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.preprocess_text(snake_case__   )
            return self.sp_model.encode(snake_case__      ,	out_type=snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.PieceToId(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.IdToPiece(snake_case__   )







     @staticmethod
     def a	( snake_case__   ):

            '''simple docstring'''

            return out_string







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       []
            _lowerCAmelCase    :							Optional[Any]   			=       ''
            _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:
                          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                          if not prev_is_special:
                                 out_string += " "

                          out_string += self.sp_model.decode(snake_case__   ) + token
                          _lowerCAmelCase    :							Union[str, Any]   			=       True
                          _lowerCAmelCase    :							List[Any]   			=       []
                   else:
                          current_sub_tokens.append(snake_case__   )
                          _lowerCAmelCase    :							List[Any]   			=       False
            out_string += self.sp_model.decode(snake_case__   )

            return out_string







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

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

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case__   ) and os.path.isfile(self.vocab_file   ):
                   copyfile(self.vocab_file      ,	snake_case__   )
            elif not os.path.isfile(self.vocab_file   ):
                   with open(snake_case__      ,	'wb'   ) as fi:
                          _lowerCAmelCase    :							Any   			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case__   )

            return (out_vocab_file,)







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            if isinstance(snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Optional[Any]   			=       self.preprocess_text(snake_case__   )
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )
            else:
                   _lowerCAmelCase    :							Tuple   			=       [self.preprocess_text(snake_case__   ) for t in text]
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )

            if return_tensors is True or return_tensors == "pt":
                   _lowerCAmelCase    :							int   			=       torch.tensor(snake_case__   )

            return token_ids







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.decode(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
            _lowerCAmelCase    :							str   			=       (
                F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(snake_case__   ) + F'{self.bos_token}Bot:'
            )
            return self.encode(text=snake_case__   )

