


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:







   class lowerCAmelCase_						:

    '''simple docstring'''

    @staticmethod
    def 						UpperCamelCase__					(     *_UpperCAmelCase		,				**_UpperCAmelCase ):
          pass






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				hashlib.mda(image.tobytes() )
      return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				DepthEstimationPipeline(model=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
       self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )}		,				_UpperCAmelCase )
       import datasets

       snake_case_	=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''		,				'''image'''		,				split='''test''' )
       snake_case_	=				depth_estimator(
           [
               Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
               '''http://images.cocodataset.org/val2017/000000039769.jpg''',
               # RGBA
               dataset[0]['''file'''],
               # LA
               dataset[1]['''file'''],
               # L
               dataset[2]['''file'''],
           ] )
       self.assertEqual(
           [
               {'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
               {'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
               {'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
               {'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
               {'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
           ]		,				_UpperCAmelCase		,				)



 @require_tf
 @unittest.skip('''Depth estimation is not implemented in TF''' )
 def 						UpperCamelCase__					(     self ):
       pass



 @slow
 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''Intel/dpt-large'''
       snake_case_	=				pipeline('''depth-estimation'''		,				model=_UpperCAmelCase )
       snake_case_	=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
       snake_case_	=				hashimage(outputs['''depth'''] )

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() )		,				29.304 )
       self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() )		,				2.662 )





 @require_torch
 def 						UpperCamelCase__					(     self ):
       # This is highly irregular to have no small tests.
       self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' )



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """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   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      for char in word:
            snake_case_	=				ord(SCREAMING_SNAKE_CASE )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE ):
                  return 0
      return 1






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				set()

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				[]

      for i in range(0 , len(SCREAMING_SNAKE_CASE ) , 100 ):
            snake_case_	=				ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=['''cws'''] ).cws
            snake_case_	=				[get_chinese_word(SCREAMING_SNAKE_CASE ) for r in res]
            ltp_res.extend(SCREAMING_SNAKE_CASE )
      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

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

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

      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )

      return ref_ids






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

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

      snake_case_	=				prepare_ref(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

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


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

   UpperCAmelCase      =   parser.parse_args()
   main(args)