




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


lowercase__				:     Union[str, Any] 		=						logging.get_logger(__name__)

lowercase__				:     int 		=						"▁"

lowercase__				:     Optional[int] 		=						{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

lowercase__				:     List[Any] 		=						{
    "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",
    },
}

lowercase__				:     Tuple 		=						{"vinai/bartpho-syllable": 1024}
class 		UpperCAmelCase   (				_UpperCAmelCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   VOCAB_FILES_NAMES
						lowerCAmelCase_     		=   PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_     		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_     		=   ['''input_ids''', '''attention_mask''']






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






							"""simple docstring"""






							snake_case_		    =     AddedToken(lowercase_       ,	lstrip=lowercase_       ,	rstrip=lowercase_						) if isinstance(lowercase_       ,	lowercase_						) else mask_token

							snake_case_		    =     {} if sp_model_kwargs is None else sp_model_kwargs

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

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

							# Load the reduced vocab

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

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






						def __getstate__(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self.__dict__.copy()
							snake_case_		    =     None
							snake_case_		    =     self.sp_model.serialized_model_proto()
							return state






						def __setstate__(						self							:     Union[str, Any]       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     d

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

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






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     List[int]       ,	__lowercase							:     Optional[List[int]] = None						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     int       ,	__lowercase							:     List[int]       ,	__lowercase							:     Optional[List[int]] = None       ,	__lowercase							:     bool = False						):






							"""simple docstring"""






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

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






						def        snake_case__						(						self							:     Any       ,	__lowercase							:     List[int]       ,	__lowercase							:     Optional[List[int]] = None						):






							"""simple docstring"""






							snake_case_		    =     [self.sep_token_id]
							snake_case_		    =     [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        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							return len(self.fairseq_ids_to_tokens						)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     {self.convert_ids_to_tokens(lowercase_						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     str						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     int						):






							"""simple docstring"""






							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							else:
								return self.unk_token_id






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     str						):






							"""simple docstring"""






							return self.fairseq_ids_to_tokens[index]






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     "".join(lowercase_						).replace(lowercase_       ,	" "						).strip()
							return out_string






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     str       ,	__lowercase							:     Optional[str] = None						):






							"""simple docstring"""






							if not os.path.isdir(lowercase_						):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
								return
							snake_case_		    =     os.path.join(
							    lowercase_       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							snake_case_		    =     os.path.join(
							    lowercase_       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]       ,	)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						) and os.path.isfile(self.vocab_file						):
								copyfile(self.vocab_file       ,	lowercase_						)
							elif not os.path.isfile(self.vocab_file						):
								with open(lowercase_       ,	"wb"						) as fi:
									snake_case_		    =     self.sp_model.serialized_model_proto()
									fi.write(lowercase_						)

							if os.path.abspath(self.monolingual_vocab_file						) != os.path.abspath(
							    lowercase_						) and os.path.isfile(self.monolingual_vocab_file						):
								copyfile(self.monolingual_vocab_file       ,	lowercase_						)
							elif not os.path.isfile(self.monolingual_vocab_file						):
								with open(lowercase_       ,	"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(lowercase_						)} \n"						)

							return out_vocab_file, out_monolingual_vocab_file



from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Union[str, Any]=1_3,lowercase_ :       Tuple=3_0,lowercase_ :       List[Any]=2,lowercase_ :       Optional[int]=3,lowercase_ :       Union[str, Any]=True,lowercase_ :       Tuple=True,lowercase_ :       Any=3_2,lowercase_ :       List[str]=2,lowercase_ :       Optional[int]=4,lowercase_ :       Union[str, Any]=3_7,lowercase_ :       Tuple="gelu",lowercase_ :       str=0.1,lowercase_ :       Tuple=0.1,lowercase_ :       Union[str, Any]=1_0,lowercase_ :       int=0.02,lowercase_ :       List[Any]=3,lowercase_ :       Any=None,)->    Dict:





        '''simple docstring'''







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

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


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





        '''simple docstring'''







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

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

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        return 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=lowercase_,initializer_range=self.initializer_range,)


    def 						snake_case__     (      self :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   TFViTModel(config=lowercase_		)
        A__          =   model(lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        A__          =   (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   self.type_sequence_label_size
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   model(lowercase_,labels=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A__          =   1
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







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




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


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





        '''simple docstring'''







        A__          =   TFViTModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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


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





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


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





        '''simple docstring'''







        A__          =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
        self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






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


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







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='tf'		)

        # forward pass
        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   tf.TensorShape((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

        tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4		)

