




'''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    __lowercase						:







   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      ,							)				->    int:
     __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 		UpperCamelCase__			(	self      )				->    Dict:
     __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 		UpperCamelCase__			(	self      )				->    str:
     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[Any]:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple:
     __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 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.prepare_config_and_inputs()
     __a	,       __a	,       __a			=					config_and_inputs
     __a			=					{'pixel_values': pixel_values}
     return config, inputs_dict





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

   _a							=      False
   _a							=      False
   _a							=      False
   _a							=      False







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					ViTMSNModelTester(self      )
     __a			=					ConfigTester(self      ,							config_class=UpperCamelCase      ,							has_text_modality=UpperCamelCase      ,							hidden_size=37      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     self.config_tester.run_common_tests()







   @unittest.skip(reason='ViTMSN does not use inputs_embeds'      )
   def 		UpperCamelCase__			(	self      )				->    str:
     pass







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __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 		UpperCamelCase__			(	self      )				->    Any:
     __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 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __a			=					ViTMSNModel.from_pretrained(UpperCamelCase      )
       self.assertIsNotNone(UpperCamelCase      )


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
  return image





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







   @cached_property
   def 		UpperCamelCase__			(	self      )				->    Dict:
     return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'      ) if is_vision_available() else None







   @slow
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     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([-0.0_803, -0.4_454, -0.2_375]      ).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 import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # 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 >= 0X4e00 and cp <= 0X9fff)
      or (cp >= 0X3400 and cp <= 0X4dbf)  #
      or (cp >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # word like '180' or '身高' or '神'
  for char in word:
    __a			=					ord(a_			)
    if not _is_chinese_char(a_			):
      return 0
  return 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

  for token in tokens:
    __a			=					len(a_			) > 1 and is_chinese(a_			)
    if chinese_word:
      word_set.add(a_			)
  __a			=					list(a_			)
  return word_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for i in range(0				,					len(a_			)				,					100			):
    __a			=					bert_tokenizer(lines[i : i + 100]				,					add_special_tokens=a_				,					truncation=a_				,					max_length=512			)
    bert_res.extend(res['input_ids']			)
  assert len(a_			) == len(a_			)

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

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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(a_			) > 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(a_				,					a_				,					a_			)

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


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

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



