"""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 		__A							:






          @staticmethod
          def 					__A						(	*a__       ,  **a__						):
            pass





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image			) ->   str:
  _lowerCAmelCase		: int						    =  hashlib.mda(image.tobytes()			)
  return m.hexdigest()[:10]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image			) ->   Dict:
  _lowerCAmelCase		: Any						    =  np.array(_lowerCamelCase			)
  _lowerCAmelCase		: Tuple						    =  npimg.shape
  return {"hash": hashimage(_lowerCamelCase			), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    List[str]	      =	dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
     _UpperCamelCase							:    Tuple	      =	dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  MaskGenerationPipeline(model=a__       ,  image_processor=a__						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]






     def 					__A						(	self       ,  a__       ,  a__						):
       pass






     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF"""						)
     def 					__A						(	self						):
       pass






     @slow
     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  pipeline("""mask-generation"""       ,  model="""facebook/sam-vit-huge"""						)

       _lowerCAmelCase		: Optional[Any]						    =  image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""       ,  points_per_batch=256						)

       # Shortening by hashing
       _lowerCAmelCase		: 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""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9_9_6_7},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_9_3},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9_9_0_9},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9_8_7_9},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9_8_3_4},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9_7_1_6},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9_6_1_2},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9_5_9_9},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9_5_5_2},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9_5_3_2},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9_5_1_6},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9_4_9_9},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9_4_8_3},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9_4_6_4},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9_4_0_8},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9_3_3_5},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9_3_2_6},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9_2_6_2},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8_9_9_9},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8_9_8_6},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8_9_8_4},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8_8_7_3},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8_8_7_1}
           ]       ,  )
       # fmt: on






     @require_torch
     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  """facebook/sam-vit-huge"""
       _lowerCAmelCase		: Dict						    =  pipeline("""mask-generation"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""       ,  pred_iou_thresh=1       ,  points_per_batch=256						)

       # Shortening by hashing
       _lowerCAmelCase		: 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""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1_0},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
           ]       ,  )





"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    str	      =	CpmAntTokenizer
     _UpperCamelCase							:    List[Any]	      =	False






     def 					__A						(	self						):
       super().setUp()

       _lowerCAmelCase		: Dict						    =  [
           """<d>""",
           """</d>""",
           """<s>""",
           """</s>""",
           """</_>""",
           """<unk>""",
           """<pad>""",
           """</n>""",
           """我""",
           """是""",
           """C""",
           """P""",
           """M""",
           """A""",
           """n""",
           """t""",
       ]
       _lowerCAmelCase		: int						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as vocab_writer:
         vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)






     @tooslow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""						)
       _lowerCAmelCase		: Optional[Any]						    =  """今天天气真好！"""
       _lowerCAmelCase		: Any						    =  ["""今天""", """天气""", """真""", """好""", """！"""]
       _lowerCAmelCase		: str						    =  tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  """今天天气真好！"""
       _lowerCAmelCase		: Optional[Any]						    =  [tokenizer.bos_token] + tokens

       _lowerCAmelCase		: Optional[int]						    =  [6, 9802, 14962, 2082, 831, 244]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokenizer.decode(a__						)
       self.assertEqual(a__       ,  a__						)




