
'''simple docstring'''







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:







  class UpperCAmelCase__      :





    """simple docstring"""

    @staticmethod
    def     __lowercase						(						*_a :	Union[str, Any]				,**_a :	str			):


       '''simple docstring'''






       pass





def    UpperCAmelCase_						(__a :       int					):






   """simple docstring"""

   _a							:      Tuple     =  hashlib.mda(image.tobytes()					)
   return m.hexdigest()[:1_0]





def    UpperCAmelCase_						(__a :       Optional[Any]					):






   """simple docstring"""

   _a							:      Optional[Any]     =  np.array(__snake_case					)
   _a							:      Optional[Any]     =  npimg.shape
   return {"hash": hashimage(__snake_case					), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class UpperCAmelCase__      (					unittest.TestCase      ):





  """simple docstring"""

  __UpperCAmelCase							:							List[str]    					=   dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
  __UpperCAmelCase							:							List[str]    					=   dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )


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


     '''simple docstring'''






     _a							:      Tuple     =  MaskGenerationPipeline(model=__a				,image_processor=__a			)
     return image_segmenter, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]


  def     __lowercase						(						self :	Optional[int]				,_a :	str				,_a :	int			):


     '''simple docstring'''






     pass


  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'			)
  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     pass


  @slow
  @require_torch
  def     __lowercase						(						self :	Union[str, Any]			):


     '''simple docstring'''






     _a							:      Optional[Any]     =  pipeline('mask-generation'				,model='facebook/sam-vit-huge'			)

     _a							:      Any     =  image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'				,points_per_batch=256			)

     # Shortening by hashing
     _a							:      Optional[int]     =  []
     for i, o in enumerate(outputs['masks']			):
        new_outupt += [{"mask": mask_to_test_readable(__a			), "scores": outputs["scores"][i]}]

     # fmt: off
     self.assertEqual(
         nested_simplify(__a				,decimals=4			)				,[
             {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
             {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
             {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
             {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
             {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
             {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
             {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
             {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
             {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
             {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
             {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
             {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
             {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
             {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
             {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
             {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
             {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
             {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
             {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
             {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
             {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
             {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
             {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
             {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
             {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
             {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
             {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
             {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
             {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
             {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
         ]				,)
     # fmt: on


  @require_torch
  @slow
  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     _a							:      Dict     =  'facebook/sam-vit-huge'
     _a							:      Union[str, Any]     =  pipeline('mask-generation'				,model=__a			)

     _a							:      str     =  image_segmenter(
         'http://images.cocodataset.org/val2017/000000039769.jpg'				,pred_iou_thresh=1				,points_per_batch=256			)

     # Shortening by hashing
     _a							:      Union[str, Any]     =  []
     for i, o in enumerate(outputs['masks']			):
        new_outupt += [{"mask": mask_to_test_readable(__a			), "scores": outputs["scores"][i]}]

     self.assertEqual(
         nested_simplify(__a				,decimals=4			)				,[
             {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
             {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
             {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
             {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
             {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
         ]				,)





"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					if not chinese_word_set:
										return bert_tokens
					_UpperCamelCase									=    max([len(__snake_case      ) for w in chinese_word_set]      )

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

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

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

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

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					with open(args.file_name,	'''r''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    f.readlines()
					_UpperCamelCase									=    [line.strip() for line in data if len(__snake_case      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					_UpperCamelCase									=    LTP(args.ltp      )  # faster in GPU device
					_UpperCamelCase									=    BertTokenizer.from_pretrained(args.bert      )

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

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


if __name__ == "__main__":
				_a								=						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""",
				)

				_a								=						parser.parse_args()
				main(args)






