
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 						_a   :

              """simple docstring"""





              @staticmethod
              def     SCREAMING_SNAKE_CASE  (					*_snake_case       ,					**_snake_case	):
               pass
def 					lowerCamelCase__		(				_lowerCamelCase  )						->str:
 _UpperCAmelCase 				=hashlib.mda(image.tobytes()  )
 return m.hexdigest()[:10]
def 					lowerCamelCase__		(				_lowerCamelCase  )						->Dict:
 _UpperCAmelCase 				=np.array(A__  )
 _UpperCAmelCase 				=npimg.shape
 return {"hash": hashimage(A__  ), "shape": shape}





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

       """simple docstring"""





       snake_case				=dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
       snake_case				=dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case       ,					_snake_case	):
        _UpperCAmelCase 				=MaskGenerationPipeline(model=__a       ,					image_processor=__a	)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case	):
        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	):
        _UpperCAmelCase 				=pipeline("mask-generation"       ,					model="facebook/sam-vit-huge"	)

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

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

        # fmt: off
        self.assertEqual(
            nested_simplify(__a       ,					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     SCREAMING_SNAKE_CASE  (					self	):
        _UpperCAmelCase 				="""facebook/sam-vit-huge"""
        _UpperCAmelCase 				=pipeline("mask-generation"       ,					model=__a	)

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

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

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

          lowerCamelCase_						:   str		= vocab_size
          lowerCamelCase_						:   List[Any]		= hidden_size
          lowerCamelCase_						:   Optional[int]		= num_hidden_layers
          lowerCamelCase_						:   Optional[Any]		= num_attention_heads
          lowerCamelCase_						:   List[Any]		= hidden_act
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   List[Any]		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= type_vocab_size
          lowerCamelCase_						:   Dict		= initializer_range
          lowerCamelCase_						:   List[Any]		= layer_norm_eps
          lowerCamelCase_						:   List[str]		= projection_dim
          lowerCamelCase_						:   Optional[int]		= position_embedding_type





