


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 		_snake_case  :




      @staticmethod
      def 	SCREAMING_SNAKE_CASE__    (		*_lowerCamelCase ,  **_lowerCamelCase   ):
            pass
def     __lowerCamelCase						(			UpperCAmelCase_						:		Image						):




      """simple docstring"""





      a						:List[str]										=	hashlib.mda(image.tobytes()						)
      return m.hexdigest()[:10]
def     __lowerCamelCase						(			UpperCAmelCase_						:		Image						):




      """simple docstring"""





      a						:Optional[Any]										=	np.array(UpperCAmelCase_						)
      a						:Union[str, Any]										=	npimg.shape
      return {"hash": hashimage(UpperCAmelCase_						), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 		_snake_case  (				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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	MaskGenerationPipeline(model=_lowerCamelCase ,  image_processor=_lowerCamelCase   )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       pass




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




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

       a						:List[Any]										=	image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' ,  points_per_batch=256   )

       # Shortening by hashing
       a						:List[Any]										=	[]
       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''': (480, 640)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
               {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9967},
               {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
               {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9909},
               {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9879},
               {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9834},
               {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9716},
               {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9612},
               {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9599},
               {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9552},
               {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9532},
               {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9516},
               {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9499},
               {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9483},
               {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9464},
               {'''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''': 0.9408},
               {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9335},
               {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9326},
               {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9262},
               {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8999},
               {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8986},
               {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8984},
               {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8873},
               {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8871}
           ] ,  )
       # fmt: on




 @require_torch
 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	'''facebook/sam-vit-huge'''
       a						:Union[str, Any]										=	pipeline('''mask-generation''' ,  model=_lowerCamelCase   )

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

       # Shortening by hashing
       a						:Dict										=	[]
       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''': (480, 640)}, '''scores''': 1.0444},
               {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0210},
               {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
               {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
               {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
           ] ,  )









from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





