

'''simple docstring'''
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


_UpperCAmelCase  :		List[Any]  =		logging.get_logger(__name__)

_UpperCAmelCase  :		List[Any]  =		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_UpperCAmelCase  :		Optional[int]  =		{
    '''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'''
        ),
    },
}

_UpperCAmelCase  :		Tuple  =		{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

_UpperCAmelCase  :		Any  =		'''▁'''

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       VOCAB_FILES_NAMES
 UpperCamelCase__     =       PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase__     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase__     =       ['input_ids', 'attention_mask']







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

   lowercase									={} if sp_model_kwargs is None else sp_model_kwargs

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

   lowercase									=vocab_file
   lowercase									=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
   self.sp_model.Load(str(snake_case_   )   )

   lowercase									={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

   lowercase									=len(self.sp_model   ) - 1
   lowercase									={v: k for k, v in self.fairseq_tokens_to_ids.items()}







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):

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







 def 						_A(       self  , snake_case_  , snake_case_ = None  , snake_case_ = False   ):
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_  , token_ids_a=snake_case_  , already_has_special_tokens=snake_case_   )

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







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   lowercase									=[self.sep_token_id]
   lowercase									=[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   ):
   return len(self.sp_model   )







 def 						_A(       self   ):
   lowercase									={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_   ):
   return self.sp_model.encode(snake_case_  , out_type=snake_case_   )







 def 						_A(       self  , snake_case_   ):
   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowercase									=self.sp_model.PieceToId(snake_case_   )

   return spm_id if spm_id else self.unk_token_id







 def 						_A(       self  , snake_case_   ):
   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=[]
   lowercase									=''''''
   lowercase									=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(snake_case_   ) + token
       lowercase									=True
       lowercase									=[]
     else:
       current_sub_tokens.append(snake_case_   )
       lowercase									=False
   out_string += self.sp_model.decode(snake_case_   )
   return out_string.strip()







 def __getstate__(       self   ):
   lowercase									=self.__dict__.copy()
   lowercase									=None
   return state







 def __setstate__(       self  , snake_case_   ):
   lowercase									=d

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

   lowercase									=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
   self.sp_model.Load(self.vocab_file   )







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   if not os.path.isdir(snake_case_   ):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
     return
   lowercase									=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:
       lowercase									=self.sp_model.serialized_model_proto()
       fi.write(snake_case_   )

   return (out_vocab_file,)




import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 						__a (							unittest.TestCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=30     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=5     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=37     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=10     ,       _SCREAMING_SNAKE_CASE=0.02     ,       )					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     patch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     type_sequence_label_size
									_UpperCAmelCase											=     initializer_range

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (image_size // patch_size) ** 2
									_UpperCAmelCase											=     num_patches + 1
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase											=     ViTConfig(
									    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       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     ,       is_decoder=_SCREAMING_SNAKE_CASE     ,       initializer_range=self.initializer_range     ,       )

									return config, pixel_values
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (self.image_size, self.image_size)
									_UpperCAmelCase											=     (self.patch_size, self.patch_size)
									_UpperCAmelCase											=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, num_patches + 1, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.type_sequence_label_size
									_UpperCAmelCase											=     FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

									# test greyscale images
									_UpperCAmelCase											=     1
									_UpperCAmelCase											=     FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)						,			(
									    _UpperCAmelCase
									)						,			
									)											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class 						__a (							UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[str]           =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
		def       UpperCAmelCase__							(     self		)					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE     ,       hidden_size=37		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     inspect.signature(model.__call__		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase											=     [*signature.parameters.keys()]

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase											=     self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape     ,       output.shape		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									for model_class_name in self.all_model_classes:
																_UpperCAmelCase											=     model_class_name.from_pretrained('google/vit-base-patch16-224'		)
																_UpperCAmelCase											=     model(np.ones((1, 3, 224, 224)		)		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
