




import inspect
import unittest

import numpy as np

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

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


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor
class 						_A (  unittest.TestCase				):






  def __init__(  self		:     int				,						_A		:     Optional[int]				,						_A		:     List[Any]=100				,						_A		:     str=13				,						_A		:     Optional[Any]=30				,						_A		:     List[Any]=2				,						_A		:     List[Any]=3				,						_A		:     Any=True				,						_A		:     Dict=True				,						_A		:     Dict=32				,						_A		:     int=5				,						_A		:     Union[str, Any]=4				,						_A		:     Union[str, Any]=37				,						_A		:     str="gelu"				,						_A		:     int=0.1				,						_A		:     Dict=0.1				,						_A		:     int=10				,						_A		:     List[str]=0.02				,						_A		:     Dict=3				,						)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       List[str]       =			parent
       lowercase :       Tuple       =			vocab_size
       lowercase :       Union[str, Any]       =			batch_size
       lowercase :       int       =			image_size
       lowercase :       Tuple       =			patch_size
       lowercase :       Any       =			num_channels
       lowercase :       str       =			is_training
       lowercase :       Any       =			use_labels
       lowercase :       str       =			hidden_size
       lowercase :       Optional[Any]       =			num_hidden_layers
       lowercase :       Optional[Any]       =			num_attention_heads
       lowercase :       Optional[Any]       =			intermediate_size
       lowercase :       List[str]       =			hidden_act
       lowercase :       Union[str, Any]       =			hidden_dropout_prob
       lowercase :       Optional[int]       =			attention_probs_dropout_prob
       lowercase :       Tuple       =			type_sequence_label_size
       lowercase :       Union[str, Any]       =			initializer_range

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       lowercase :       str       =			(image_size // patch_size) ** 2
       lowercase :       str       =			num_patches + 1






  def  __a      (  self		:     str				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[Any]       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       lowercase :       Optional[int]       =			None
       if self.use_labels:
            lowercase :       int       =			ids_tensor([self.batch_size]				,						self.type_sequence_label_size				)

       lowercase :       Dict       =			BeitConfig(
           vocab_size=self.vocab_size				,						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=_A				,						initializer_range=self.initializer_range				,						)

       return config, pixel_values, labels






  def  __a      (  self		:     Optional[int]				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any				)		->   Dict:






       """simple docstring"""







       lowercase :       Tuple       =			FlaxBeitModel(config=_A				)
       lowercase :       Any       =			model(_A				)
       self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)				)






  def  __a      (  self		:     List[str]				,						_A		:     str				,						_A		:     Dict				,						_A		:     str				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Optional[int]       =			FlaxBeitForMaskedImageModeling(config=_A				)
       lowercase :       Tuple       =			model(_A				)
       self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length - 1, self.vocab_size)				)






  def  __a      (  self		:     str				,						_A		:     Any				,						_A		:     Optional[Any]				,						_A		:     List[str]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[int]       =			self.type_sequence_label_size
       lowercase :       int       =			FlaxBeitForImageClassification(config=_A				)
       lowercase :       List[str]       =			model(_A				)
       self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       lowercase :       List[Any]       =			1
       lowercase :       Optional[Any]       =			FlaxBeitForImageClassification(_A				)

       lowercase :       List[Any]       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       lowercase :       str       =			model(_A				)






  def  __a      (  self		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       Tuple       =			self.prepare_config_and_inputs()
       (
           (
           lowercase
       )      ,	(
           lowercase
       )      ,	(
           lowercase
       )      ,	
       ) :       Any       =			config_and_inputs
       lowercase :       int       =			{'''pixel_values''': pixel_values}
       return config, inputs_dict


@require_flax
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      str			=      (
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )






  def  __a      (  self		:     Any				)		->   None:






       """simple docstring"""







       lowercase :       Optional[int]       =			FlaxBeitModelTester(self				)
       lowercase :       Tuple       =			ConfigTester(self				,						config_class=_A				,						has_text_modality=_A				,						hidden_size=37				)






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       self.config_tester.run_common_tests()






  def  __a      (  self		:     Any				)		->   Tuple:






       """simple docstring"""







       lowercase      ,	lowercase :       Union[str, Any]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            lowercase :       Any       =			model_class(_A				)
            lowercase :       List[str]       =			inspect.signature(model.__call__				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            lowercase :       Optional[Any]       =			[*signature.parameters.keys()]

            lowercase :       str       =			['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,						_A				)






  def  __a      (  self		:     Optional[Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase      ,	lowercase :       Union[str, Any]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       Optional[int]       =			self._prepare_for_class(_A				,						_A				)
                 lowercase :       Dict       =			model_class(_A				)

                 @jax.jit
                 def model_jitted(_A		:     Optional[int]				,						**_A		:     List[Any]				):
                      return model(pixel_values=_A				,						**_A				)

                 with self.subTest('''JIT Enabled'''				):
                      lowercase :       List[Any]       =			model_jitted(**_A				).to_tuple()

                 with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                           lowercase :       Union[str, Any]       =			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  __a      (  self		:     List[str]				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A				)






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






       """simple docstring"""







       lowercase :       Dict       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_A				)






  def  __a      (  self		:     Any				)		->   List[Any]:






       """simple docstring"""







       lowercase :       List[Any]       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_A				)






  @slow
  def  __a      (  self		:     int				)		->   Optional[int]:






       """simple docstring"""







       for model_class_name in self.all_model_classes:
            lowercase :       int       =			model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''				)
            lowercase :       List[Any]       =			model(np.ones((1, 3, 224, 224)				)				)
            self.assertIsNotNone(_A				)






def  snake_case(    )			->							Tuple:





     '''simple docstring'''


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



@require_vision
@require_flax
class 						_A (  unittest.TestCase				):






  @cached_property
  def  __a      (  self		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''				) if is_vision_available() else None






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       str       =			FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''				)

       lowercase :       Dict       =			self.default_image_processor
       lowercase :       str       =			prepare_img()
       lowercase :       str       =			image_processor(images=_A				,						return_tensors='''np'''				).pixel_values

       # prepare bool_masked_pos
       lowercase :       Any       =			np.ones((1, 196)				,						dtype=_A				)

       # forward pass
       lowercase :       str       =			model(pixel_values=_A				,						bool_masked_pos=_A				)
       lowercase :       Tuple       =			outputs.logits

       # verify the logits
       lowercase :       List[Any]       =			(1, 196, 8_192)
       self.assertEqual(logits.shape				,						_A				)

       lowercase :       Optional[int]       =			np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]				)

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]				,						_A				,						atol=1E-2				)				)






  @slow
  def  __a      (  self		:     Any				)		->   Any:






       """simple docstring"""







       lowercase :       int       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''				)

       lowercase :       Union[str, Any]       =			self.default_image_processor
       lowercase :       int       =			prepare_img()
       lowercase :       Union[str, Any]       =			image_processor(images=_A				,						return_tensors='''np'''				)

       # forward pass
       lowercase :       Optional[int]       =			model(**_A				)
       lowercase :       str       =			outputs.logits

       # verify the logits
       lowercase :       Dict       =			(1, 1_000)
       self.assertEqual(logits.shape				,						_A				)

       lowercase :       Union[str, Any]       =			np.array([-1.2_385, -1.0_987, -1.0_108]				)

       self.assertTrue(np.allclose(logits[0, :3]				,						_A				,						atol=1E-4				)				)

       lowercase :       int       =			281
       self.assertEqual(logits.argmax(-1				).item()				,						_A				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''				)

       lowercase :       int       =			self.default_image_processor
       lowercase :       List[Any]       =			prepare_img()
       lowercase :       Tuple       =			image_processor(images=_A				,						return_tensors='''np'''				)

       # forward pass
       lowercase :       str       =			model(**_A				)
       lowercase :       Any       =			outputs.logits

       # verify the logits
       lowercase :       Any       =			(1, 21_841)
       self.assertEqual(logits.shape				,						_A				)

       lowercase :       Optional[int]       =			np.array([1.6_881, -0.2_787, 0.5_901]				)

       self.assertTrue(np.allclose(logits[0, :3]				,						_A				,						atol=1E-4				)				)

       lowercase :       List[Any]       =			2_396
       self.assertEqual(logits.argmax(-1				).item()				,						_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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_						=			{
    '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'
        ),
    },
}

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

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






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






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[Any]       =			{} 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				,						)

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

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

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






  def  __a      (  self		:     Tuple				,						_A		:     List[int]				,						_A		:     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]
       lowercase :       Any       =			[self.cls_token_id]
       lowercase :       int       =			[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







       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		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       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  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{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		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Dict:






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       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(_A				) + token
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






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






       """simple docstring"""







       lowercase :       str       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

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

       lowercase :       List[Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)






  def  __a      (  self		:     str				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       if not os.path.isdir(_A				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
       lowercase :       Any       =			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				) 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:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)