

'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
      from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor


class 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=13		,				UpperCamelCase__=30		,				UpperCamelCase__=2		,				UpperCamelCase__=3		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=32		,				UpperCamelCase__=5		,				UpperCamelCase__=4		,				UpperCamelCase__=37		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=10		,				UpperCamelCase__=0.02		,				UpperCamelCase__=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 MSN, 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       )						->      Optional[int]:





           '''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       )						->      Union[str, Any]:





           '''simple docstring'''
           return ViTMSNConfig(
               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		,				initializer_range=self.initializer_range		,				)



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      ViTMSNModel(config=UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      self.type_sequence_label_size
           A_						=      ViTMSNForImageClassification(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           A_						=      model(UpperCamelCase__		,				labels=UpperCamelCase__       )
           print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""       )
           print("""Labels: {labels}"""       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)       )

           # test greyscale images
           A_						=      1
           A_						=      ViTMSNForImageClassification(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()

           A_						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)       )







    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.prepare_config_and_inputs()
           A_					,			A_					,			A_						=      config_and_inputs
           A_						=      {"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 					A__      (      _snake_case						,						_snake_case						,						unittest.TestCase			):
    lowercase						=		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    lowercase						=		(
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

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



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      ViTMSNModelTester(self       )
           A_						=      ConfigTester(self		,				config_class=UpperCamelCase__		,				has_text_modality=UpperCamelCase__		,				hidden_size=37       )



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""       )
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self       )						->      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(UpperCamelCase__       )
                  self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)       )
                  A_						=      model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(UpperCamelCase__		,				nn.Linear       )       )



    def 					snake_case_			(					self       )						->      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(UpperCamelCase__       )
                  A_						=      inspect.signature(model.forward       )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  A_						=      [*signature.parameters.keys()]

                  A_						=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,				UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						=      ViTMSNModel.from_pretrained(UpperCamelCase__       )
                  self.assertIsNotNone(UpperCamelCase__       )

def       UpperCAmelCase__    (      )			->			Optional[Any]:
       A_						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image







@require_torch
@require_vision
class 					A__      (      unittest.TestCase			):



    @cached_property
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""       ) if is_vision_available() else None







    @slow
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           torch.manual_seed(2       )
           A_						=      ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(UpperCamelCase__       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""pt"""       ).to(UpperCamelCase__       )

           # forward pass
           with torch.no_grad():
                  A_						=      model(**UpperCamelCase__       )

           # verify the logits
           A_						=      torch.Size((1, 1000)       )
           self.assertEqual(outputs.logits.shape		,				UpperCamelCase__       )

           A_						=      torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(UpperCamelCase__       )

           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				UpperCamelCase__		,				atol=1e-4       )       )



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # This defines a "chinese character" as anything in the CJK Unicode block:
       #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
       #
       # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
       # despite its name. The modern Korean Hangul alphabet is a different block,
       # as is Japanese Hiragana and Katakana. Those alphabets are used to write
       # space-separated words, so they are not treated specially and handled
       # like the all of the other languages.
       if (
           (cp >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
              return True

       return False
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              A_						=      ord(UpperCAmelCase__				)
              if not _is_chinese_char(UpperCAmelCase__				):
                     return 0
       return 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      set()

       for token in tokens:
              A_						=      len(UpperCAmelCase__				) > 1 and is_chinese(UpperCAmelCase__				)
              if chinese_word:
                     word_set.add(UpperCAmelCase__				)
       A_						=      list(UpperCAmelCase__				)
       return word_list
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if not chinese_word_set:
              return bert_tokens
       A_						=      max([len(UpperCAmelCase__				) for w in chinese_word_set]				)

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       1,       -1				):
                            A_						=      """""".join(bert_word[start : start + i]				)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1,       start + i				):
                                          A_						=      """##""" + bert_word[j]
                                   A_						=      start + i
                                   A_						=      False
                                   break
              if single_word:
                     start += 1
       return bert_word
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       tasks=["""cws"""]				).cws
              A_						=      [get_chinese_word(UpperCAmelCase__				) for r in res]
              ltp_res.extend(UpperCAmelCase__				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              bert_res.extend(res["""input_ids"""]				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for input_ids, chinese_word in zip(UpperCAmelCase__,       UpperCAmelCase__				):
              A_						=      []
              for id in input_ids:
                     A_						=      bert_tokenizer._convert_id_to_token(UpperCAmelCase__				)
                     input_tokens.append(UpperCAmelCase__				)
              A_						=      add_sub_symbol(UpperCAmelCase__,       UpperCAmelCase__				)
              A_						=      []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(UpperCAmelCase__				):
                     if token[:2] == "##":
                            A_						=      token[2:]
                            # save chinese tokens' pos
                            if len(UpperCAmelCase__				) == 1 and _is_chinese_char(ord(UpperCAmelCase__				)				):
                                   ref_id.append(UpperCAmelCase__				)
              ref_ids.append(UpperCAmelCase__				)

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
       # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
       with open(args.file_name,       """r""",       encoding="""utf-8"""				) as f:
              A_						=      f.readlines()
       A_						=      [line.strip() for line in data if len(UpperCAmelCase__				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       A_						=      LTP(args.ltp				)  # faster in GPU device
       A_						=      BertTokenizer.from_pretrained(args.bert				)

       A_						=      prepare_ref(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       with open(args.save_path,       """w""",       encoding="""utf-8"""				) as f:
              A_						=      [json.dumps(UpperCAmelCase__				) + """\n""" for ref in ref_ids]
              f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser(description='''prepare_chinese_ref''')
      parser.add_argument(
          '''--file_name''',
          required=False,
          type=str,
          default='''./resources/chinese-demo.txt''',
          help='''file need process, same as training data in lm''',
      )
      parser.add_argument(
          '''--ltp''',
          required=False,
          type=str,
          default='''./resources/ltp''',
          help='''resources for LTP tokenizer, usually a path''',
      )
      parser.add_argument(
          '''--bert''',
          required=False,
          type=str,
          default='''./resources/robert''',
          help='''resources for Bert tokenizer''',
      )
      parser.add_argument(
          '''--save_path''',
          required=False,
          type=str,
          default='''./resources/ref.txt''',
          help='''path to save res''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
