

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch







class     __lowercase (TensorFormatter[Mapping, """torch.Tensor""", Mapping]					):
 def __init__(				self ,				A_=None ,				**A_   )   ->Any:



       '''simple docstring'''





       super().__init__(features=__lowerCamelCase   )
       __lowerCAmelCase   :		List[str]   =   torch_tensor_kwargs
       import torch  # noqa import torch at initialization
 def       UpperCamelCase__    (				self ,				A_   )   ->Union[str, Any]:



       '''simple docstring'''





       import torch

       if isinstance(__lowerCamelCase ,				__lowerCamelCase   ) and column:
             if all(
                 isinstance(__lowerCamelCase ,				torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                 for x in column   ):
                   return torch.stack(__lowerCamelCase   )
       return column
 def       UpperCamelCase__    (				self ,				A_   )   ->List[Any]:



       '''simple docstring'''





       import torch

       if isinstance(__lowerCamelCase ,				(str, bytes, type(__lowerCamelCase   ))   ):
             return value
       elif isinstance(__lowerCamelCase ,				(np.character, np.ndarray)   ) and np.issubdtype(value.dtype ,				np.character   ):
             return value.tolist()

       __lowerCAmelCase   :		Union[str, Any]   =   {}

       if isinstance(__lowerCamelCase ,				(np.number, np.ndarray)   ) and np.issubdtype(value.dtype ,				np.integer   ):
             __lowerCAmelCase   :		str   =   {'''dtype''': torch.intaa}
       elif isinstance(__lowerCamelCase ,				(np.number, np.ndarray)   ) and np.issubdtype(value.dtype ,				np.floating   ):
             __lowerCAmelCase   :		Optional[int]   =   {'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(__lowerCamelCase ,				PIL.Image.Image   ):
                   __lowerCAmelCase   :		int   =   np.asarray(__lowerCamelCase   )
       return torch.tensor(__lowerCamelCase ,				**{**default_dtype, **self.torch_tensor_kwargs}   )
 def       UpperCamelCase__    (				self ,				A_   )   ->Dict:



       '''simple docstring'''





       import torch

       # support for torch, tf, jax etc.
       if hasattr(__lowerCamelCase ,				'''__array__'''   ) and not isinstance(__lowerCamelCase ,				torch.Tensor   ):
             __lowerCAmelCase   :		List[str]   =   data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__lowerCamelCase ,				np.ndarray   ):
             if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__lowerCamelCase   ) for substruct in data_struct]   )
       elif isinstance(__lowerCamelCase ,				(list, tuple)   ):
             return self._consolidate([self.recursive_tensorize(__lowerCamelCase   ) for substruct in data_struct]   )
       return self._tensorize(__lowerCamelCase   )
 def       UpperCamelCase__    (				self ,				A_   )   ->List[str]:



       '''simple docstring'''





       return map_nested(self._recursive_tensorize ,				__lowerCamelCase ,				map_list=__lowerCamelCase   )
 def       UpperCamelCase__    (				self ,				A_   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Union[str, Any]   =   self.numpy_arrow_extractor().extract_row(__lowerCamelCase   )
       __lowerCAmelCase   :		List[Any]   =   self.python_features_decoder.decode_row(__lowerCamelCase   )
       return self.recursive_tensorize(__lowerCamelCase   )
 def       UpperCamelCase__    (				self ,				A_   )   ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   self.numpy_arrow_extractor().extract_column(__lowerCamelCase   )
       __lowerCAmelCase   :		List[Any]   =   self.python_features_decoder.decode_column(__lowerCamelCase ,				pa_table.column_names[0]   )
       __lowerCAmelCase   :		List[Any]   =   self.recursive_tensorize(__lowerCamelCase   )
       __lowerCAmelCase   :		Dict   =   self._consolidate(__lowerCamelCase   )
       return column





 def       UpperCamelCase__    (				self ,				A_   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   self.numpy_arrow_extractor().extract_batch(__lowerCamelCase   )
       __lowerCAmelCase   :		Dict   =   self.python_features_decoder.decode_batch(__lowerCamelCase   )
       __lowerCAmelCase   :		Optional[int]   =   self.recursive_tensorize(__lowerCamelCase   )
       for column_name in batch:
             __lowerCAmelCase   :		Any   =   self._consolidate(batch[column_name]   )
       return batch








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     SCREAMING_SNAKE_CASE__		:
           @staticmethod
           def        A__      (						*__lowerCamelCase	:		List[str]       ,						**__lowerCamelCase	:		Optional[int]						):



             """simple docstring"""
             pass
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      hashlib.mda(image.tobytes()				)
  return m.hexdigest()[:10]
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      np.array(__lowerCAmelCase				)
  lowerCAmelCase__										=      npimg.shape
  return {"hash": hashimage(__lowerCAmelCase				), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     lowercase_ :      Any				= dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
     lowercase_ :      str				= dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      MaskGenerationPipeline(model=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[int]						):



       """simple docstring"""
       pass
     @require_tf
     @unittest.skip('''Image segmentation not implemented in TF'''						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       pass
     @slow
     @require_torch
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      pipeline('''mask-generation'''       ,						model='''facebook/sam-vit-huge'''						)

       lowerCAmelCase__										=      image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,						points_per_batch=2_56						)

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

       # fmt: off
       self.assertEqual(
           nested_simplify(__lowerCamelCase       ,						decimals=4						)       ,						[
               {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.021},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
               {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9967},
               {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.993},
               {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9909},
               {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9879},
               {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9834},
               {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9716},
               {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9612},
               {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9599},
               {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9552},
               {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9532},
               {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9516},
               {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9499},
               {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9483},
               {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9464},
               {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
               {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
               {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9408},
               {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9335},
               {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9326},
               {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9262},
               {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8999},
               {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8986},
               {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8984},
               {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8873},
               {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8871}
           ]       ,						)
       # fmt: on



     @require_torch
     @slow
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''facebook/sam-vit-huge'''
       lowerCAmelCase__										=      pipeline('''mask-generation'''       ,						model=__lowerCamelCase						)

       lowerCAmelCase__										=      image_segmenter(
           '''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,						pred_iou_thresh=1       ,						points_per_batch=2_56						)

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

       self.assertEqual(
           nested_simplify(__lowerCamelCase       ,						decimals=4						)       ,						[
               {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0210},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
           ]       ,						)

