

'''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       snake_case (				*__lowercase      :			Optional[Any]    ,   **__lowercase      :			Dict   ):







                   """simple docstring"""







                   pass

def 					__UpperCamelCase				(						lowercase__    :	Image			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	Image			):






     '''simple docstring'''





     __lowercase      		=np.array(lowercase__			)
     __lowercase      		=npimg.shape
     return {"hash": hashimage(lowercase__			), "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       snake_case (				self      :			List[str]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=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      :			Optional[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            pass







       @require_tf
       @unittest.skip('Image segmentation not implemented in TF'   )
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            pass







       @slow
       @require_torch
       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=pipeline('mask-generation'    ,   model='facebook/sam-vit-huge'   )

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

            # Shortening by hashing
            __lowercase      		=[]
            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_4_4_4},
                    {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
                    {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
                    {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
                    {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
                    {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
                    {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
                    {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
                    {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
                    {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
                    {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
                    {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
                    {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
                    {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
                    {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
                    {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
                    {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
                    {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
                    {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
                    {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
                    {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
                    {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
                    {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
                    {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
                    {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
                    {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
                    {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
                    {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
                    {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
                    {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
                ]    ,   )
            # fmt: on







       @require_torch
       @slow
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		='facebook/sam-vit-huge'
            __lowercase      		=pipeline('mask-generation'    ,   model=__lowercase   )

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

            # Shortening by hashing
            __lowercase      		=[]
            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_4_4_4},
                    {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
                    {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
                    {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
                    {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
                ]    ,   )





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


