





'''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_barthez import BarthezTokenizer
else:
					snake_case_     =	None

snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case_     =	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_     =	{
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

snake_case_     =	"""▁"""


class   _lowercase						(							a				):
			_UpperCamelCase       		=   VOCAB_FILES_NAMES
			_UpperCamelCase       		=   PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase       		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase       		=   ["""input_ids""", """attention_mask"""]
			_UpperCamelCase       		=   BarthezTokenizer


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

							super().__init__(
							    _UpperCAmelCase    ,   tokenizer_file=_UpperCAmelCase    ,   bos_token=_UpperCAmelCase    ,   eos_token=_UpperCAmelCase    ,   unk_token=_UpperCAmelCase    ,   sep_token=_UpperCAmelCase    ,   cls_token=_UpperCAmelCase    ,   pad_token=_UpperCAmelCase    ,   mask_token=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )

							A       :       Optional[int]							=						vocab_file
							A       :       Any							=						False if not self.vocab_file else True


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

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A       :       Union[str, Any]							=						[self.cls_token_id]
							A       :       List[str]							=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


			def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
							A       :       int							=						[self.sep_token_id]
							A       :       List[Any]							=						[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]


			def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = 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(_UpperCAmelCase					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
							A       :       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					):
											copyfile(self.vocab_file    ,   _UpperCAmelCase					)

							return (out_vocab_file,)







'''simple docstring'''






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

     snake_case_     =	"""0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Any=None		)		->  List[Any]:
    if rng is None:
        A       :       int							=						random.Random()

    A       :       int							=						1
    for dim in shape:
        total_dims *= dim

    A       :       Union[str, Any]							=						[]
    for _ in range(UpperCamelCase__		):
        values.append(rng.randint(0    ,       vocab_size - 1		)		)

    A       :       Union[str, Any]							=						np.array(UpperCamelCase__    ,       dtype=jnp.intaa		).reshape(UpperCamelCase__		)

    return output
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Optional[Any]=None		)		->  Dict:
    A       :       Union[str, Any]							=						ids_tensor(UpperCamelCase__    ,       vocab_size=2    ,       rng=UpperCamelCase__		)
    # make sure that at least one token is attended to for each batch
    A       :       Any							=						1
    return attn_mask


@require_flax
class   _lowercase						:
   _UpperCamelCase       		=   None
   _UpperCamelCase       		=   ()


   def 					snake_case				( self					):
       A,	A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()

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

       A       :       Optional[Any]							=						jnp.ones_like(_UpperCAmelCase					)
       A       :       int							=						attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       A       :       Optional[int]							=						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()`
           A       :       str							=						config.eos_token_id
       return config, input_ids, attention_mask, max_length


   @is_pt_flax_cross_test
   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Union[str, Any]							=						max_length
       A       :       str							=						0

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
           A       :       int							=						getattr(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       Union[str, Any]							=						pt_model_class(_UpperCAmelCase					).eval()
           A       :       Tuple							=						load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,   flax_model.params					)

           A       :       Union[str, Any]							=						flax_model.generate(_UpperCAmelCase					).sequences
           A       :       Optional[int]							=						pt_model.generate(torch.tensor(_UpperCAmelCase    ,   dtype=torch.long					)					)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               A       :       List[Any]							=						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       str							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Dict							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       List[str]							=						True
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Tuple							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Tuple							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[str]							=						self._get_input_ids_and_config()
       A       :       Any							=						False
       A       :       str							=						max_length
       A       :       Optional[int]							=						2

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       List[Any]							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[int]							=						self._get_input_ids_and_config()
       A       :       Dict							=						False
       A       :       List[Any]							=						max_length
       A       :       Optional[int]							=						2
       A       :       int							=						2

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       List[str]							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[0]    ,   input_ids.shape[0] * config.num_return_sequences					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       Any							=						True
       A       :       Any							=						max_length
       A       :       Any							=						0.8
       A       :       List[Any]							=						10
       A       :       List[Any]							=						0.3
       A       :       Union[str, Any]							=						1
       A       :       Any							=						8
       A       :       List[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       List[Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       int							=						max_length
       A       :       Dict							=						1
       A       :       Tuple							=						8
       A       :       Optional[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       str							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       Optional[int]							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       int							=						self._get_input_ids_and_config()
       A       :       List[Any]							=						max_length
       A       :       Optional[Any]							=						2
       A       :       Tuple							=						1
       A       :       List[Any]							=						8
       A       :       Dict							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Union[str, Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Union[str, Any]							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Dict							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Union[str, Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       int							=						False
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Dict							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Optional[int]							=						True
       A       :       int							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Union[str, Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Optional[Any]							=						jit(model.generate					)
           A       :       Dict							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

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


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[int]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Any							=						2
       A       :       Any							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Optional[Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[str]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

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







@require_flax
class   _lowercase						(							unittest.TestCase				):


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

       A       :       Union[str, Any]							=						'''Hello world'''
       A       :       Union[str, Any]							=						tokenizer(_UpperCAmelCase    ,   return_tensors='''np'''					).input_ids

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

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(_UpperCAmelCase    ,   '''foo'''					):
           A       :       List[Any]							=						{'''foo''': '''bar'''}
           model.generate(_UpperCAmelCase    ,   **_UpperCAmelCase					)
