


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       __magic_name__	(     unittest.TestCase):


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Tuple=3							,	lowerCamelCase__							:     str=32							,	lowerCamelCase__							:     str=3							,	lowerCamelCase__							:     Dict=10							,	lowerCamelCase__							:     int=[10, 20, 30, 40]							,	lowerCamelCase__							:     Optional[int]=[1, 1, 2, 1]							,	lowerCamelCase__							:     Optional[int]=True							,	lowerCamelCase__							:     Optional[int]=True							,	lowerCamelCase__							:     Optional[Any]="relu"							,	lowerCamelCase__							:     List[Any]=3							,	lowerCamelCase__							:     Union[str, Any]=None							,	)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   parent
      UpperCamelCase__					:    int          =   batch_size
      UpperCamelCase__					:    Optional[Any]          =   image_size
      UpperCamelCase__					:    Tuple          =   num_channels
      UpperCamelCase__					:    Union[str, Any]          =   embeddings_size
      UpperCamelCase__					:    Optional[Any]          =   hidden_sizes
      UpperCamelCase__					:    Tuple          =   depths
      UpperCamelCase__					:    Union[str, Any]          =   is_training
      UpperCamelCase__					:    int          =   use_labels
      UpperCamelCase__					:    Union[str, Any]          =   hidden_act
      UpperCamelCase__					:    List[str]          =   num_labels
      UpperCamelCase__					:    Union[str, Any]          =   scope
      UpperCamelCase__					:    List[str]          =   len(SCREAMING_SNAKE_CASE_			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      UpperCamelCase__					:    Any          =   self.get_config()

      return config, pixel_values


     def      UpperCAmelCase__	(      self							:     Any			)  ->      int:





      '''simple docstring'''






      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							:     List[str]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     List[Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   FlaxRegNetModel(config=SCREAMING_SNAKE_CASE_			)
      UpperCamelCase__					:    str          =   model(SCREAMING_SNAKE_CASE_			)

      # 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							:     Optional[Any]							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Tuple			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.num_labels
      UpperCamelCase__					:    Optional[Any]          =   FlaxRegNetForImageClassification(config=SCREAMING_SNAKE_CASE_			)
      UpperCamelCase__					:    Optional[int]          =   model(SCREAMING_SNAKE_CASE_			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)			)


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.prepare_config_and_inputs()
      UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   config_and_inputs
      UpperCamelCase__					:    Tuple          =   {'''pixel_values''': pixel_values}
      return config, inputs_dict



@require_flax
class       __magic_name__	(     __lowerCamelCase					,				unittest.TestCase):
     A:		Tuple			=      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     A:		int			=      False
     A:		Union[str, Any]			=      False
     A:		Any			=      False


     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   FlaxRegNetModelTester(self			)
      UpperCamelCase__					:    List[str]          =   ConfigTester(self							,	config_class=SCREAMING_SNAKE_CASE_							,	has_text_modality=SCREAMING_SNAKE_CASE_			)


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





      '''simple docstring'''






      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							:     List[Any]			)  ->      int:





      '''simple docstring'''






      return


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)


     @unittest.skip(reason='''RegNet does not use inputs_embeds'''			)
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      pass


     @unittest.skip(reason='''RegNet does not support input and output embeddings'''			)
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      List[Any]:





      '''simple docstring'''






      pass


     def      UpperCAmelCase__	(      self							:     str			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__       ,				UpperCamelCase__					:    str          =   self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__					:    str          =   ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,	SCREAMING_SNAKE_CASE_			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      def check_hidden_states_output(lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Optional[Any]			):
       UpperCamelCase__					:    int          =   model_class(SCREAMING_SNAKE_CASE_			)

       UpperCamelCase__					:    List[str]          =   model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_			)			)

       UpperCamelCase__					:    Optional[int]          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       UpperCamelCase__					:    Optional[int]          =   self.model_tester.num_stages
       self.assertEqual(len(SCREAMING_SNAKE_CASE_			)							,	expected_num_stages + 1			)

      UpperCamelCase__       ,				UpperCamelCase__					:    Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       UpperCamelCase__					:    str          =   True
       check_hidden_states_output(SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_			)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCamelCase__					:    Optional[Any]          =   True

       check_hidden_states_output(SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_			)


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





      '''simple docstring'''






      UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__			):
        UpperCamelCase__					:    Dict          =   self._prepare_for_class(SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_			)
        UpperCamelCase__					:    Tuple          =   model_class(SCREAMING_SNAKE_CASE_			)

        @jax.jit
        def model_jitted(lowerCamelCase__							:     Any							,	**lowerCamelCase__							:     Tuple			):
         return model(pixel_values=SCREAMING_SNAKE_CASE_							,	**SCREAMING_SNAKE_CASE_			)

        with self.subTest('''JIT Enabled'''			):
         UpperCamelCase__					:    Optional[int]          =   model_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

        with self.subTest('''JIT Disabled'''			):
         with jax.disable_jit():
          UpperCamelCase__					:    Dict          =   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			)



def 				_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






@require_flax
class       __magic_name__	(     unittest.TestCase):


     @cached_property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''			) if is_vision_available() else None


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''			)

      UpperCamelCase__					:    Any          =   self.default_image_processor
      UpperCamelCase__					:    str          =   prepare_img()
      UpperCamelCase__					:    Optional[Any]          =   image_processor(images=SCREAMING_SNAKE_CASE_							,	return_tensors='''np'''			)

      UpperCamelCase__					:    int          =   model(**SCREAMING_SNAKE_CASE_			)

      # verify the logits
      UpperCamelCase__					:    Union[str, Any]          =   (1, 1000)
      self.assertEqual(outputs.logits.shape							,	SCREAMING_SNAKE_CASE_			)

      UpperCamelCase__					:    Tuple          =   jnp.array([-0.4180, -1.5051, -3.4836]			)

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






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       :       Tuple						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Union[str, Any]						       =				{"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase       :       List[Any]						       =				{
    "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       :       int						       =				{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__UpperCamelCase       :       int						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      VOCAB_FILES_NAMES
     A:		Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		str			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Union[str, Any]			=      ["input_ids", "attention_mask"]


     def __init__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Tuple="<s>"							,	lowerCamelCase__							:     Tuple="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     Optional[Any]="<s>"							,	lowerCamelCase__							:     List[str]="<unk>"							,	lowerCamelCase__							:     str="<pad>"							,	lowerCamelCase__							:     int="<mask>"							,	lowerCamelCase__							:     Optional[Dict[str, Any]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      None:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      UpperCamelCase__					:    Optional[int]          =   {} if sp_model_kwargs is None else sp_model_kwargs

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

      UpperCamelCase__					:    Any          =   vocab_file
      UpperCamelCase__					:    str          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(str(lowerCamelCase__			)			)

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

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


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [self.cls_token_id]
      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None							,	lowerCamelCase__							:     bool = False			)  ->      List[int]:





      '''simple docstring'''






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

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


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      return len(self.sp_model			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     str			)  ->      List[str]:





      '''simple docstring'''






      return self.sp_model.encode(lowerCamelCase__							,	out_type=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     List[str]			)  ->      Union[str, Any]:





      '''simple docstring'''






      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      UpperCamelCase__					:    List[str]          =   self.sp_model.PieceToId(lowerCamelCase__			)

      return spm_id if spm_id else self.unk_token_id


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     int			)  ->      List[str]:





      '''simple docstring'''






      if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   []
      UpperCamelCase__					:    Any          =   ''''''
      UpperCamelCase__					:    str          =   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(lowerCamelCase__			) + token
        UpperCamelCase__					:    str          =   True
        UpperCamelCase__					:    Tuple          =   []
       else:
        current_sub_tokens.append(lowerCamelCase__			)
        UpperCamelCase__					:    Any          =   False
      out_string += self.sp_model.decode(lowerCamelCase__			)
      return out_string.strip()


     def __getstate__(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.__dict__.copy()
      UpperCamelCase__					:    int          =   None
      return state


     def __setstate__(      self							:     Tuple							,	lowerCamelCase__							:     Any			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   d

      # for backward compatibility
      if not hasattr(self							,	'''sp_model_kwargs'''			):
       UpperCamelCase__					:    Optional[Any]          =   {}

      UpperCamelCase__					:    Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(self.vocab_file			)


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






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

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

      return (out_vocab_file,)


