





import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {"""vocab_file""": """sentencepiece.model"""}

snake_case 				=       {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

snake_case 				=       {
    """google/rembert""": 2_5_6,
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : str      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







       def __init__(    self	:	Any						,__A	:	Optional[int]						,__A	:	Tuple=False						,__A	:	Union[str, Any]=True						,__A	:	str=True						,__A	:	Any="[CLS]"						,__A	:	Any="[SEP]"						,__A	:	str="[UNK]"						,__A	:	Union[str, Any]="[SEP]"						,__A	:	str="[PAD]"						,__A	:	Dict="[CLS]"						,__A	:	List[Any]="[MASK]"						,**__A	:	List[Any]						,)						->					Optional[int]:
         super().__init__(
             do_lower_case=__a						,remove_space=__a						,keep_accents=__a						,bos_token=__a						,eos_token=__a						,unk_token=__a						,sep_token=__a						,pad_token=__a						,cls_token=__a						,mask_token=__a						,**__a						,)

         _lowercase							= do_lower_case
         _lowercase							= remove_space
         _lowercase							= keep_accents
         _lowercase							= vocab_file

         _lowercase							= spm.SentencePieceProcessor()
         self.sp_model.Load(__a  )







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					Dict:
         return len(self.sp_model  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Any:
         _lowercase							= {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab







       def __getstate__(    self	:	Any  )						->					Union[str, Any]:
         _lowercase							= self.__dict__.copy()
         _lowercase							= None
         return state







       def __setstate__(    self	:	List[Any]						,__A	:	Union[str, Any]  )						->					str:
         _lowercase							= d
         _lowercase							= spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Union[str, Any]						,__A	:	Union[str, Any]=False  )						->					Union[str, Any]:
         _lowercase							= self.sp_model.EncodeAsPieces(__a  )
         return pieces







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Any  )						->					Union[str, Any]:
         return self.sp_model.PieceToId(__a  )







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict  )						->					Dict:
         return self.sp_model.IdToPiece(__a  )







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	Tuple  )						->					Tuple:
         _lowercase							= self.sp_model.decode_pieces(__a  )
         return out_string







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Dict						,__A	:	Union[str, Any] = None  )						->					List[int]:
         _lowercase							= [self.sep_token_id]
         _lowercase							= [self.cls_token_id]
         if token_ids_a is None:
           return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	List[Any]						,__A	:	List[str] = None						,__A	:	Tuple = False  )						->					List[int]:

         if already_has_special_tokens:
           if token_ids_a is not None:
             raise ValueError(
                 'You should not supply a second sequence if the provided sequence of '
                 'ids is already formatted with special tokens for the model.'  )
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
           return [1] + ([0] * len(__a  )) + [1] + ([0] * len(__a  )) + [1]
         return [1] + ([0] * len(__a  )) + [1]







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Dict						,__A	:	List[str] = None  )						->					List[int]:
         _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  ) * [0] + len(token_ids_a + sep  ) * [1]







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Dict						,__A	:	Optional[Any] = None  )						->					Tuple[str]:
         if not os.path.isdir(__a  ):
           logger.error('Vocabulary path ({}) should be a directory'.format(__a  )  )
           return
         _lowercase							= os.path.join(
             __a						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ):
           copyfile(self.vocab_file						,__a  )

         return (out_vocab_file,)












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[str]						,__A	:	str						,__A	:	Dict=3						,__A	:	str=32						,__A	:	int=3						,__A	:	Union[str, Any]=10						,__A	:	Optional[Any]=[10, 20, 30, 40]						,__A	:	List[str]=[1, 1, 2, 1]						,__A	:	Optional[int]=True						,__A	:	Optional[Any]=True						,__A	:	int="relu"						,__A	:	Union[str, Any]=3						,__A	:	List[Any]=None						,)						->					int:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= image_size
         _lowercase							= num_channels
         _lowercase							= embeddings_size
         _lowercase							= hidden_sizes
         _lowercase							= depths
         _lowercase							= is_training
         _lowercase							= use_labels
         _lowercase							= hidden_act
         _lowercase							= num_labels
         _lowercase							= scope
         _lowercase							= len(__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         _lowercase							= self.get_config()

         return config, pixel_values







       def    __UpperCAmelCase (    self	:	Any  )						->					int:
         return RegNetConfig(
             num_channels=self.num_channels						,embeddings_size=self.embeddings_size						,hidden_sizes=self.hidden_sizes						,depths=self.depths						,hidden_act=self.hidden_act						,num_labels=self.num_labels						,image_size=self.image_size						,)







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Any						,__A	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRegNetModel(config=__A  )
         _lowercase							= model(__A  )

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape						,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,)







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Optional[int]						,__A	:	Dict  )						->					Any:
         _lowercase							= self.num_labels
         _lowercase							= FlaxRegNetForImageClassification(config=__A  )
         _lowercase							= model(__A  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'pixel_values': pixel_values}
         return config, inputs_dict





@require_flax
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Tuple      =  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False







       def    __UpperCAmelCase (    self	:	Dict  )						->					None:
         _lowercase							= FlaxRegNetModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=__A						,has_text_modality=__A  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Union[str, Any]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Any:
         return







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A  )







       @unittest.skip(reason='RegNet does not use inputs_embeds'  )
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         pass







       @unittest.skip(reason='RegNet does not support input and output embeddings'  )
       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         pass







       def    __UpperCAmelCase (    self	:	Dict  )						->					List[str]:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase							= ['pixel_values']
           self.assertListEqual(arg_names[:1]						,__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[str]:
         def check_hidden_states_output(__A	:	Optional[Any]						,__A	:	Optional[int]						,__A	:	str  ):
           _lowercase							= model_class(__A  )

           _lowercase							= model(**self._prepare_for_class(__A						,__A  )  )

           _lowercase							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase							= self.model_tester.num_stages
           self.assertEqual(len(__A  )						,expected_num_stages + 1  )

         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= True
           check_hidden_states_output(__A						,__A						,__A  )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase							= True

           check_hidden_states_output(__A						,__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					int:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
             _lowercase							= self._prepare_for_class(__A						,__A  )
             _lowercase							= model_class(__A  )

             @jax.jit
             def model_jitted(__A	:	Any						,**__A	:	Union[str, Any]  ):
               return model(pixel_values=__A						,**__A  )

             with self.subTest('JIT Enabled'  ):
               _lowercase							= model_jitted(**__A  ).to_tuple()

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

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

def 							SCREAMING_SNAKE_CASE__       (  )      ->						str:
  _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image







@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @cached_property
       def    __UpperCAmelCase (    self	:	str  )						->					Any:
         return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'  ) if is_vision_available() else None







       @slow
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'  )

         _lowercase							= self.default_image_processor
         _lowercase							= prepare_img()
         _lowercase							= image_processor(images=__A						,return_tensors='np'  )

         _lowercase							= model(**__A  )

         # verify the logits
         _lowercase							= (1, 1000)
         self.assertEqual(outputs.logits.shape						,__A  )

         _lowercase							= jnp.array([-0.4180, -1.5051, -3.4836]  )

         self.assertTrue(jnp.allclose(outputs.logits[0, :3]						,__A						,atol=1e-4  )  )