
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase__							=				{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase__							=				logging.get_logger(__name__)





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''maskformer'''
							UpperCAmelCase__						     =      {'''hidden_size''': '''mask_feature_size'''}
							UpperCAmelCase__						     =      ['''resnet''', '''swin''']
							UpperCAmelCase__						     =      ['''detr''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 0.1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 0.02       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 20.0       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE_						 = SwinConfig(
											    image_size=3_84       ,       in_channels=3       ,       patch_size=4       ,       embed_dim=1_28       ,       depths=[2, 2, 18, 2]       ,       num_heads=[4, 8, 16, 32]       ,       window_size=12       ,       drop_path_rate=0.3       ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       ,       )

									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = backbone_config.pop('''model_type'''    )
											SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
											    f'Supported model types: {",".join(self.backbones_supported    )}'    )

									if decoder_config is None:
											# fall back to https://huggingface.co/facebook/detr-resnet-50
											SCREAMING_SNAKE_CASE_						 = DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE_						 = (
											    decoder_config.pop('''model_type'''    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else decoder_config.model_type
											)
											if decoder_type not in self.decoders_supported:
													raise ValueError(
													    f'Transformer Decoder {decoder_type} not supported, please use one of'
													    f' {",".join(self.decoders_supported    )}'    )
											if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = backbone_config
									SCREAMING_SNAKE_CASE_						 = decoder_config
									# main feature dimension for the model
									SCREAMING_SNAKE_CASE_						 = fpn_feature_size
									SCREAMING_SNAKE_CASE_						 = mask_feature_size
									# initializer
									SCREAMING_SNAKE_CASE_						 = init_std
									SCREAMING_SNAKE_CASE_						 = init_xavier_std
									# Hungarian matcher && loss
									SCREAMING_SNAKE_CASE_						 = cross_entropy_weight
									SCREAMING_SNAKE_CASE_						 = dice_weight
									SCREAMING_SNAKE_CASE_						 = mask_weight
									SCREAMING_SNAKE_CASE_						 = use_auxiliary_loss
									SCREAMING_SNAKE_CASE_						 = no_object_weight
									SCREAMING_SNAKE_CASE_						 = output_auxiliary_logits

									SCREAMING_SNAKE_CASE_						 = self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.num_hidden_layers
									super().__init__(**SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return cls(
									    backbone_config=SCREAMING_SNAKE_CASE_       ,       decoder_config=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_       ,       )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = copy.deepcopy(self.__dict__    )
									SCREAMING_SNAKE_CASE_						 = self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.__class__.model_type
									return output

"""simple docstring"""




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return x.sum()



def        _lowerCamelCase ( __a    ):  # picklable for multiprocessing
		return i + 1





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







class  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = [1, 2]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [1, 2], '''b''': [3, 4]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 1}, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = [2, 3]
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': [2, 3], '''b''': [4, 5]}
									SCREAMING_SNAKE_CASE_						 = {'''a''': {'''1''': 2}, '''b''': 3}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = 2
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = {'''a''': np.eye(2    ), '''b''': np.zeros(3    ), '''c''': np.ones(2    )}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 2, '''b''': 0, '''c''': 2}
									SCREAMING_SNAKE_CASE_						 = {
									    '''a''': np.eye(2    ).astype(SCREAMING_SNAKE_CASE_    ),
									    '''b''': np.zeros(3    ).astype(SCREAMING_SNAKE_CASE_    ),
									    '''c''': np.ones(2    ).astype(SCREAMING_SNAKE_CASE_    ),
									}
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_    ).items()}       ,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,       )
									self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       map_numpy=SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    ).items()}       ,       {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,       )
									with self.assertRaises(SCREAMING_SNAKE_CASE_    ):  # can't pickle a local lambda
											map_nested(lambda SCREAMING_SNAKE_CASE_    : x + 1       ,       SCREAMING_SNAKE_CASE_       ,       num_proc=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {'''a''': 1, '''b''': 2}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 3, '''b''': 4}
									SCREAMING_SNAKE_CASE_						 = {'''a''': 5, '''b''': 6}
									SCREAMING_SNAKE_CASE_						 = sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]    )
									self.assertEqual(sorted(zip_dict(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									class  snake_case     :
																UpperCAmelCase__						     =      '''bar'''







									SCREAMING_SNAKE_CASE_						 = Foo()
									self.assertEqual(foo.my_attr       ,       '''bar'''    )
									with temporary_assignment(SCREAMING_SNAKE_CASE_       ,       '''my_attr'''       ,       '''BAR'''    ):
											self.assertEqual(foo.my_attr       ,       '''BAR'''    )
									self.assertEqual(foo.my_attr       ,       '''bar'''    )







@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc''',							[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ],							)
def        _lowerCamelCase ( __a,							__a,							__a    ):
		with patch('''datasets.utils.py_utils._single_map_nested'''    ) as mock_single_map_nested, patch(
		    '''datasets.parallel.parallel.Pool'''    ) as mock_multiprocessing_pool:
				SCREAMING_SNAKE_CASE_						 = {F'{i}': i for i in range(__a    )}
				SCREAMING_SNAKE_CASE_						 = map_nested(lambda __a    : x + 10,							__a,							num_proc=__a,							parallel_min_length=16    )
				if expected_num_proc == 1:
						assert mock_single_map_nested.called
						assert not mock_multiprocessing_pool.called
				else:
						assert not mock_single_map_nested.called
						assert mock_multiprocessing_pool.called
						assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class  snake_case     (							__lowercase   ):

							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									import tensorflow as tf
									from tensorflow.keras import layers

									SCREAMING_SNAKE_CASE_						 = layers.Dense(2    )

									def gen_random_output():
											SCREAMING_SNAKE_CASE_						 = tf.random.uniform((1, 3)    )
											return model(SCREAMING_SNAKE_CASE_    ).numpy()

									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_tensorflow=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									def gen_random_output():
											SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(3       ,       2    )
											SCREAMING_SNAKE_CASE_						 = torch.rand(1       ,       3    )
											return model(SCREAMING_SNAKE_CASE_    ).detach().numpy()

									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42       ,       set_pytorch=SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )







							def _lowercase					(self    ):

									"""simple docstring"""







									def gen_random_output():
											return np.random.rand(1       ,       3    )

									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									with temp_seed(42    ):
											SCREAMING_SNAKE_CASE_						 = gen_random_output()
									SCREAMING_SNAKE_CASE_						 = gen_random_output()

									np.testing.assert_equal(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									self.assertGreater(np.abs(outa - outa    ).sum()       ,       0    )







@pytest.mark.parametrize('''input_data''',							[{}]    )
def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).data
		assert output_data == input_data



@pytest.mark.parametrize(
    '''data, expected_output''',							[
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ],							)
def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = NestedDataStructure(__a    ).flatten()
		assert output == expected_output



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = A(x=1,							y='''foobar'''    )
		SCREAMING_SNAKE_CASE_						 = {'''x''': 1, '''y''': '''foobar'''}
		assert asdict(__a    ) == expected_output

		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': A(x=10,							y='''foo'''    )}, '''c''': [A(x=20,							y='''bar'''    )]}
		SCREAMING_SNAKE_CASE_						 = {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
		assert asdict(__a    ) == expected_output

		with pytest.raises(__a    ):
				asdict([1, A(x=10,							y='''foo'''    )]    )



def        _lowerCamelCase ( __a    ):
		return text.split()



def        _lowerCamelCase ( __a    ):
		yield (time.time(), content)
		time.sleep(2    )
		yield (time.time(), content)



def        _lowerCamelCase ( ):
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = list(iflatmap_unordered(__a,							_split_text,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10    )    )
				assert out.count('''hello'''    ) == 10
				assert out.count('''there'''    ) == 10
				assert len(__a    ) == 20

		# check multiprocess from pathos (uses dill for pickling)
		with multiprocess.Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = list(iflatmap_unordered(__a,							_split_text,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10    )    )
				assert out.count('''hello'''    ) == 10
				assert out.count('''there'''    ) == 10
				assert len(__a    ) == 20

		# check that we get items as fast as possible
		with Pool(2    ) as pool:
				SCREAMING_SNAKE_CASE_						 = []
				for yield_time, content in iflatmap_unordered(
				    __a,							_aseconds_generator_of_aitems_with_timing,							kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]    ):
						assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
						out.append(__a    )
				assert out.count('''a'''    ) == 2
				assert out.count('''b'''    ) == 2
				assert len(__a    ) == 4