





"""simple docstring"""






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=30    ,  a__=2    ,  a__=3    ,  a__=True    ,  a__=True    ,  a__=32    ,  a__=2    ,  a__=4    ,  a__=37    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=10    ,  a__=0.02    ,  a__=3    ,  a__=None    ,  a__=2    ,  ):
              _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
              _UpperCAmelCase     =     scope
              _UpperCAmelCase     =     encoder_stride

              # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
              _UpperCAmelCase     =     (image_size // patch_size) ** 2
              _UpperCAmelCase     =     num_patches + 2






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

              _UpperCAmelCase     =     None
              if self.use_labels:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.type_sequence_label_size     )

              _UpperCAmelCase     =     self.get_config()

              return config, pixel_values, labels






       def 							__A (   self     ):
              return DeiTConfig(
                  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    ,  encoder_stride=self.encoder_stride    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     TFDeiTModel(config=a__     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     TFDeiTForMaskedImageModeling(config=a__     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(
                  result.reconstruction.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

              # test greyscale images
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     TFDeiTForMaskedImageModeling(a__     )

              _UpperCAmelCase     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.reconstruction.shape    ,  (self.batch_size, 1, self.image_size, self.image_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.type_sequence_label_size
              _UpperCAmelCase     =     TFDeiTForImageClassification(a__     )
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)     )

              # test greyscale images
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     TFDeiTForImageClassification(a__     )

              _UpperCAmelCase     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
              _UpperCAmelCase     =     model(a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)     )






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



@require_tf
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       lowerCAmelCase__						 =  (
           {
               """feature-extraction""": TFDeiTModel,
               """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     TFDeiTModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  has_text_modality=a__    ,  hidden_size=37     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       @unittest.skip(reason='DeiT does not use inputs_embeds'     )
       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(a__     )
                     self.assertIsInstance(model.get_input_embeddings()    ,  (tf.keras.layers.Layer)     )
                     _UpperCAmelCase     =     model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(a__    ,  tf.keras.layers.Dense     )     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     model_class(a__     )
                     _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]    ,  a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__=False     ):
              _UpperCAmelCase     =     super()._prepare_for_class(a__    ,  a__    ,  return_labels=a__     )

              if return_labels:
                     if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
                            del inputs_dict["labels"]

              return inputs_dict






       @slow
       def 							__A (   self     ):
              for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     TFDeiTModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )


def       __lowerCamelCase    (       )		->       Tuple:






       """simple docstring"""




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



@require_tf
@require_vision
class 	lowerCAmelCase					(      unittest.TestCase							):






       @cached_property
       def 							__A (   self     ):
              return (
                  DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'     )
                  if is_vision_available()
                  else None
              )






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'     )

              _UpperCAmelCase     =     self.default_image_processor
              _UpperCAmelCase     =     prepare_img()
              _UpperCAmelCase     =     image_processor(images=a__    ,  return_tensors='tf'     )

              # forward pass
              _UpperCAmelCase     =     model(**a__     )

              # verify the logits
              _UpperCAmelCase     =     tf.TensorShape((1, 10_00)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     tf.constant([-1.0_266, 0.1_912, -1.2_861]     )

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













"""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


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							'''▁'''

lowerCAmelCase_			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

lowerCAmelCase_			=							{'''vinai/bartpho-syllable''': 1_024}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]






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

              _UpperCAmelCase     =     {} 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__    ,  )

              _UpperCAmelCase     =     vocab_file
              _UpperCAmelCase     =     monolingual_vocab_file
              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.Load(str(a__     )     )

              # Load the reduced vocab

              # Keep order of special tokens for backward compatibility
              _UpperCAmelCase     =     {}
              _UpperCAmelCase     =     0
              for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                     if str(a__     ) not in self.fairseq_tokens_to_ids:
                            _UpperCAmelCase     =     cnt
                            cnt += 1
              with open(a__    ,  'r'    ,  encoding='utf-8'     ) as f:
                     for line in f.readlines():
                            _UpperCAmelCase     =     line.strip().split()[0]
                            _UpperCAmelCase     =     len(self.fairseq_tokens_to_ids     )
              if str(a__     ) not in self.fairseq_tokens_to_ids:
                     _UpperCAmelCase     =     len(self.fairseq_tokens_to_ids     )

              _UpperCAmelCase     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(   self     ):
              _UpperCAmelCase     =     self.__dict__.copy()
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
              return state






       def __setstate__(   self    ,  a__     ):
              _UpperCAmelCase     =     d

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

              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






       def 							__A (   self    ,  a__    ,  a__ = None     ):

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






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = False     ):

              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    ,  a__    ,  a__ = None     ):
              _UpperCAmelCase     =     [self.sep_token_id]
              _UpperCAmelCase     =     [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.fairseq_ids_to_tokens     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {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    ,  a__     ):
              return self.sp_model.encode(a__    ,  out_type=a__     )






       def 							__A (   self    ,  a__     ):
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              else:
                     return self.unk_token_id






       def 							__A (   self    ,  a__     ):
              return self.fairseq_ids_to_tokens[index]






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     ''.join(a__     ).replace(a__    ,  ' '     ).strip()
              return out_string






       def 							__A (   self    ,  a__    ,  a__ = None     ):
              if not os.path.isdir(a__     ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
                     return
              _UpperCAmelCase     =     os.path.join(
                  a__    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
              _UpperCAmelCase     =     os.path.join(
                  a__    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
                            _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
                            fi.write(a__     )

              if os.path.abspath(self.monolingual_vocab_file     ) != os.path.abspath(
                  a__     ) and os.path.isfile(self.monolingual_vocab_file     ):
                     copyfile(self.monolingual_vocab_file    ,  a__     )
              elif not os.path.isfile(self.monolingual_vocab_file     ):
                     with open(a__    ,  'w'    ,  encoding='utf-8'     ) as fp:
                            for token in self.fairseq_tokens_to_ids:
                                   if token not in self.all_special_tokens:
                                          fp.write(f"""{str(a__     )} \n"""     )

              return out_vocab_file, out_monolingual_vocab_file






