



'''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  __lowerCAmelCase     :




   @staticmethod
   def        lowerCamelCase      (						*__UpperCAmelCase	,				**__UpperCAmelCase				):



      '''simple docstring'''
      pass





def 		a__			(		_UpperCamelCase			: List[str]							):
   __lowerCamelCase													=    hashlib.mda(image.tobytes()							)
   return m.hexdigest()[:10]





def 		a__			(		_UpperCamelCase			: Any							):
   __lowerCamelCase													=    np.array(lowerCamelCase__							)
   __lowerCamelCase													=    npimg.shape
   return {"hash": hashimage(lowerCamelCase__							), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class  __lowerCAmelCase     (	unittest.TestCase   ):
 lowerCAmelCase__       					=  dict(
     (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
 lowerCAmelCase__       					=  dict(
     (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    MaskGenerationPipeline(model=__UpperCAmelCase	,				image_processor=__UpperCAmelCase				)
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    pass




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



    '''simple docstring'''
    pass




 @slow
 @require_torch
 def        lowerCamelCase      (						self				):



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

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

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

    # fmt: off
    self.assertEqual(
        nested_simplify(__UpperCAmelCase	,				decimals=4				)	,				[
            {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_444},
            {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
            {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_167},
            {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_132},
            {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_053},
            {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9_967},
            {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
            {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9_909},
            {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9_879},
            {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9_834},
            {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9_716},
            {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9_612},
            {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9_599},
            {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9_552},
            {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9_532},
            {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9_516},
            {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9_499},
            {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9_483},
            {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9_464},
            {'''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''': 0.9_408},
            {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9_335},
            {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9_326},
            {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9_262},
            {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8_999},
            {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8_986},
            {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8_984},
            {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8_873},
            {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8_871}
        ]	,				)
    # fmt: on




 @require_torch
 @slow
 def        lowerCamelCase      (						self				):



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

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

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

    self.assertEqual(
        nested_simplify(__UpperCAmelCase	,				decimals=4				)	,				[
            {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0_444},
            {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_210},
            {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0_167},
            {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0_132},
            {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0_053},
        ]	,				)











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






