





"""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					:





             @staticmethod
             def     _snake_case			(       *lowercase      ,						**lowercase )		->     Union[str, Any]:
                   pass


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Image			):







      '''simple docstring'''




      lowerCAmelCase  						=       hashlib.mda(image.tobytes()			)
      return m.hexdigest()[:10]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Image			):







      '''simple docstring'''




      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       npimg.shape
      return {"hash": hashimage(SCREAMING_SNAKE_CASE			), "shape": shape}






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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[Any]:
            lowerCAmelCase  						=       MaskGenerationPipeline(model=lowercase      ,						image_processor=lowercase )
            return image_segmenter, [
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
            ]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     List[Any]:
            pass





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





      @slow
      @require_torch
      def     _snake_case			(       self )		->     str:
            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(lowercase ), "scores": outputs["scores"][i]}]

            # fmt: off
            self.assertEqual(
                nested_simplify(lowercase      ,						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     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       """facebook/sam-vit-huge"""
            lowerCAmelCase  						=       pipeline("""mask-generation"""      ,						model=lowercase )

            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(lowercase ), "scores": outputs["scores"][i]}]

            self.assertEqual(
                nested_simplify(lowercase      ,						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},
                ]      ,						)











'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
