
"""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   _lowercase						:


      '''simple docstring'''





      @staticmethod
      def  lowerCAmelCase__       (      *__UpperCamelCase ,			**__UpperCamelCase  )->					Tuple:
           pass



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						hashlib.mda(image.tobytes()		)
     return m.hexdigest()[:10]



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						np.array(lowerCamelCase__		)
     UpperCAmelCase__							: List[Any] 							=						npimg.shape
     return {"hash": hashimage(lowerCamelCase__		), "shape": shape}







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


     '''simple docstring'''





     _A								=							dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
     _A								=							dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					int:
          UpperCAmelCase__							: Dict 							=						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  )->					Any:
          pass



     @require_tf
     @unittest.skip("Image segmentation not implemented in TF"  )
     def  lowerCAmelCase__       (      self  )->					Dict:
          pass



     @slow
     @require_torch
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						pipeline("mask-generation" ,			model="facebook/sam-vit-huge"  )

          UpperCAmelCase__							: Union[str, Any] 							=						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" ,			points_per_batch=2_56  )

          # Shortening by hashing
          UpperCAmelCase__							: str 							=						[]
          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": (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  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: int 							=						"facebook/sam-vit-huge"
          UpperCAmelCase__							: Optional[Any] 							=						pipeline("mask-generation" ,			model=__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						image_segmenter(
              "http://images.cocodataset.org/val2017/000000039769.jpg" ,			pred_iou_thresh=1 ,			points_per_batch=2_56  )

          # Shortening by hashing
          UpperCAmelCase__							: int 							=						[]
          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": (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},
              ] ,			)





"""simple docstring"""




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__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						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

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



