


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   _A		       =						'0.12'  # assumed parallelism: 8


if is_torch_available():
   import torch

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=None				):
     if rng is None:
          __UpperCamelCase    							=random.Random()

     __UpperCamelCase    							=1
     for dim in shape:
          total_dims *= dim

     __UpperCamelCase    							=[]
     for _ in range(SCREAMING_SNAKE_CASE__				):
          values.append(rng.randint(0			,     vocab_size - 1				)				)

     __UpperCamelCase    							=np.array(SCREAMING_SNAKE_CASE__			,     dtype=jnp.intaa				).reshape(SCREAMING_SNAKE_CASE__				)

     return output

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]=None				):
     __UpperCamelCase    							=ids_tensor(SCREAMING_SNAKE_CASE__			,     vocab_size=2			,     rng=SCREAMING_SNAKE_CASE__				)
     # make sure that at least one token is attended to for each batch
     __UpperCamelCase    							=1
     return attn_mask






@require_flax
class     UpperCAmelCase__       :



    """simple docstring"""






    UpperCAmelCase__						:		str      =  None
    UpperCAmelCase__						:		Dict      =  ()






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         __UpperCamelCase    							=2
         __UpperCamelCase    							=inputs['input_ids'].shape[-1] // 2
         __UpperCamelCase    							=inputs['input_ids'][:max_batch_size, :sequence_length]

         __UpperCamelCase    							=jnp.ones_like(A_    )
         __UpperCamelCase    							=attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __UpperCamelCase    							=input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
              # hack to allow generate for models such as GPT2 as is done in `generate()`
              __UpperCamelCase    							=config.eos_token_id
         return config, input_ids, attention_mask, max_length






    @is_pt_flax_cross_test
    def _a					(			self    )  ->       Any:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=0

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model_class.__name__[4:]  # Skip the "Flax" at the beginning
              __UpperCamelCase    							=getattr(A_					,   A_    )
              __UpperCamelCase    							=pt_model_class(A_    ).eval()
              __UpperCamelCase    							=load_flax_weights_in_pytorch_model(A_					,   flax_model.params    )

              __UpperCamelCase    							=flax_model.generate(A_    ).sequences
              __UpperCamelCase    							=pt_model.generate(torch.tensor(A_					,   dtype=torch.long    )    )

              if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                   __UpperCamelCase    							=flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

              self.assertListEqual(pt_generation_outputs.numpy().tolist()					,   flax_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2
         __UpperCamelCase    							=2

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[0]					,   input_ids.shape[0] * config.num_return_sequences    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=0.8
         __UpperCamelCase    							=10
         __UpperCamelCase    							=0.3
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()
         __UpperCamelCase    							=max_length
         __UpperCamelCase    							=2
         __UpperCamelCase    							=1
         __UpperCamelCase    							=8
         __UpperCamelCase    							=9

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=False
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=True
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=self._get_input_ids_and_config()

         # pad attention mask on the left
         __UpperCamelCase    							=attention_mask.at[(0, 0)].set(0    )

         __UpperCamelCase    							=2
         __UpperCamelCase    							=max_length

         for model_class in self.all_generative_model_classes:
              __UpperCamelCase    							=model_class(A_    )

              __UpperCamelCase    							=model.generate(A_					,   attention_mask=A_    ).sequences
              self.assertEqual(generation_outputs.shape[-1]					,   A_    )

              __UpperCamelCase    							=jit(model.generate    )
              __UpperCamelCase    							=jit_generate(A_					,   attention_mask=A_    ).sequences

              self.assertListEqual(generation_outputs.tolist()					,   jit_generation_outputs.tolist()    )






@require_flax
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'    )
         __UpperCamelCase    							=FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'    )

         __UpperCamelCase    							='Hello world'
         __UpperCamelCase    							=tokenizer(A_					,   return_tensors='np'    ).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(A_					,   'do_samples'    ):
              model.generate(A_					,   do_samples=A_    )

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(A_					,   'foo'    ):
              __UpperCamelCase    							={'foo': 'bar'}
              model.generate(A_					,   **A_    )









import os
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		       =						'▁'

_A		       =						{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_A		       =						{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_A		       =						{'vinai/bartpho-syllable': 1024}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		str      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_="<mask>"					,   A_ = None					,   **A_					,   )  ->       None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else mask_token

         __UpperCamelCase    							={} if sp_model_kwargs is None else sp_model_kwargs

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

         __UpperCamelCase    							=vocab_file
         __UpperCamelCase    							=monolingual_vocab_file
         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         # Load the reduced vocab

         # Keep order of special tokens for backward compatibility
         __UpperCamelCase    							={}
         __UpperCamelCase    							=0
         for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(A_    ) not in self.fairseq_tokens_to_ids:
                   __UpperCamelCase    							=cnt
                   cnt += 1
         with open(A_					,   'r'					,   encoding='utf-8'    ) as f:
              for line in f.readlines():
                   __UpperCamelCase    							=line.strip().split()[0]
                   __UpperCamelCase    							=len(self.fairseq_tokens_to_ids    )
         if str(A_    ) not in self.fairseq_tokens_to_ids:
              __UpperCamelCase    							=len(self.fairseq_tokens_to_ids    )

         __UpperCamelCase    							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self    )  ->       Any:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=d

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

         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __UpperCamelCase    							=[self.cls_token_id]
         __UpperCamelCase    							=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:

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

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






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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 + sep + token_ids_a + sep    ) * [0]






    @property
    def _a					(			self    )  ->       Any:
         return len(self.fairseq_ids_to_tokens    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							={self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab






    def _a					(			self					,   A_    )  ->       List[str]:
         return self.sp_model.encode(A_					,   out_type=A_    )






    def _a					(			self					,   A_    )  ->       str:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id






    def _a					(			self					,   A_    )  ->       int:
         return self.fairseq_ids_to_tokens[index]






    def _a					(			self					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=''.join(A_    ).replace(A_					,   ' '    ).strip()
         return out_string






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
              return
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']					,   )

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

         if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
             A_    ) and os.path.isfile(self.monolingual_vocab_file    ):
              copyfile(self.monolingual_vocab_file					,   A_    )
         elif not os.path.isfile(self.monolingual_vocab_file    ):
              with open(A_					,   'w'					,   encoding='utf-8'    ) as fp:
                   for token in self.fairseq_tokens_to_ids:
                        if token not in self.all_special_tokens:
                             fp.write(f'{str(A_    )} \n'    )

         return out_vocab_file, out_monolingual_vocab_file





