



'''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


__a  			=						logging.get_logger(__name__)

__a  			=						"▁"

__a  			=						{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

__a  			=						{
    "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",
    },
}

__a  			=						{"vinai/bartpho-syllable": 1024}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						VOCAB_FILES_NAMES
    lowercase   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase   =						["input_ids", "attention_mask"]




    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	int					,	snake_case_   :	Optional[Any]="<s>"					,	snake_case_   :	Tuple="</s>"					,	snake_case_   :	List[Any]="</s>"					,	snake_case_   :	Optional[Any]="<s>"					,	snake_case_   :	List[str]="<unk>"					,	snake_case_   :	str="<pad>"					,	snake_case_   :	Optional[Any]="<mask>"					,	snake_case_   :	Optional[Dict[str, Any]] = None					,	**snake_case_   :	Tuple					,	):
          # Mask token behave like a normal word, i.e. include the space before it
          snake_case__    :       Union[str, Any]       		= AddedToken(snake_case_					,	lstrip=snake_case_					,	rstrip=snake_case_		) if isinstance(snake_case_					,	snake_case_		) else mask_token

          snake_case__    :       int       		= {} if sp_model_kwargs is None else sp_model_kwargs

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

          snake_case__    :       Any       		= vocab_file
          snake_case__    :       List[str]       		= monolingual_vocab_file
          snake_case__    :       Tuple       		= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(str(snake_case_		)		)

          # Load the reduced vocab

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

          snake_case__    :       List[str]       		= {v: k for k, v in self.fairseq_tokens_to_ids.items()}




    def __getstate__(       self   :	Tuple		):
          snake_case__    :       Dict       		= self.__dict__.copy()
          snake_case__    :       Dict       		= None
          snake_case__    :       Tuple       		= self.sp_model.serialized_model_proto()
          return state




    def __setstate__(       self   :	str					,	snake_case_   :	Tuple		):
          snake_case__    :       int       		= d

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

          snake_case__    :       str       		= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None		):

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




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None					,	snake_case_   :	bool = False		):

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

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




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None		):
          snake_case__    :       List[str]       		= [self.sep_token_id]
          snake_case__    :       Tuple       		= [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 lowerCamelCase			(       self   :	Tuple		):
          return len(self.fairseq_ids_to_tokens		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[int]       		= {self.convert_ids_to_tokens(snake_case_		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab




    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	str		):
          return self.sp_model.encode(snake_case_					,	out_type=snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Union[str, Any]		):
          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          else:
                return self.unk_token_id




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Tuple		):
          return self.fairseq_ids_to_tokens[index]




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	Tuple		):
          snake_case__    :       List[Any]       		= """""".join(snake_case_		).replace(snake_case_					,	""" """		).strip()
          return out_string




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	str					,	snake_case_   :	Optional[str] = None		):
          if not os.path.isdir(snake_case_		):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
                return
          snake_case__    :       Optional[int]       		= os.path.join(
              snake_case_					,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
          snake_case__    :       int       		= os.path.join(
              snake_case_					,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]					,	)

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

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

          return out_vocab_file, out_monolingual_vocab_file





'''simple docstring'''





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	Optional[int]=3					,	snake_case_   :	List[Any]=True					,	snake_case_   :	Dict=True					,	snake_case_   :	Optional[Any]=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	Dict=224					,	snake_case_   :	Dict=1_000					,	snake_case_   :	Tuple=[3, 3, 6, 4]					,	snake_case_   :	Tuple=[48, 56, 112, 220]					,	):
          snake_case__    :       str       		= parent
          snake_case__    :       Dict       		= batch_size
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       Any       		= is_training
          snake_case__    :       Union[str, Any]       		= use_labels
          snake_case__    :       Tuple       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= num_labels
          snake_case__    :       Any       		= image_size
          snake_case__    :       str       		= layer_depths
          snake_case__    :       Union[str, Any]       		= embed_dims




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          snake_case__    :       int       		= None
          if self.use_labels:
                snake_case__    :       List[Any]       		= ids_tensor([self.batch_size]					,	self.num_labels		)

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          return SwiftFormerConfig(
              depths=self.layer_depths					,	embed_dims=self.embed_dims					,	mlp_ratio=4					,	downsamples=[True, True, True, True]					,	hidden_act="""gelu"""					,	num_labels=self.num_labels					,	down_patch_size=3					,	down_stride=2					,	down_pad=1					,	drop_rate=0.0					,	drop_path_rate=0.0					,	use_layer_scale=snake_case_					,	layer_scale_init_value=1E-5					,	)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       Dict       		= SwiftFormerModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       List[str]       		= model(snake_case_		)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.embed_dims[-1], 7, 7)		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
          snake_case__    :       Any       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__		,							snake_case__    :       Dict       		= self.model_tester.prepare_config_and_inputs_for_common()

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

                snake_case__    :       Optional[int]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                with torch.no_grad():
                      snake_case__    :       Tuple       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 8
                self.assertEqual(len(snake_case_		)					,	snake_case_		)  # TODO

                # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                # with the width and height being successively divided by 2, after every 2 blocks
                for i in range(len(snake_case_		)		):
                      self.assertEqual(
                          hidden_states[i].shape					,	torch.Size(
                              [
                                  self.model_tester.batch_size,
                                  self.model_tester.embed_dims[i // 2],
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                              ]		)					,	)

          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= True
                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case__    :       str       		= True

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, Any]       		= copy.deepcopy(snake_case_		)
                for key in configs_no_init.__dict__.keys():
                      if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                            setattr(snake_case_					,	snake_case_					,	1E-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

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

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(config=snake_case_		)
                for name, param in model.named_parameters():
                      if param.requires_grad:
                            self.assertIn(
                                ((param.data.mean() * 1E9) / 1E9).round().item()					,	[0.0, 1.0]					,	msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,	)




    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def lowerCamelCase			(       self   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	List[str]		):
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)
