





'''simple docstring'''

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''glpn'''


    def __init__(		self ,		A=3 ,		A=4 ,		A=[2, 2, 2, 2] ,		A=[8, 4, 2, 1] ,		A=[32, 64, 160, 256] ,		A=[7, 3, 3, 3] ,		A=[4, 2, 2, 2] ,		A=[1, 2, 5, 8] ,		A=[4, 4, 4, 4] ,		A="gelu" ,		A=0.0 ,		A=0.0 ,		A=0.02 ,		A=0.1 ,		A=1e-6 ,		A=64 ,		A=10 ,		A=-1 ,		**A ,		)		->   Any:
        super().__init__(**A       )

        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			num_encoder_blocks
        _SCREAMING_SNAKE_CASE									=			depths
        _SCREAMING_SNAKE_CASE									=			sr_ratios
        _SCREAMING_SNAKE_CASE									=			hidden_sizes
        _SCREAMING_SNAKE_CASE									=			patch_sizes
        _SCREAMING_SNAKE_CASE									=			strides
        _SCREAMING_SNAKE_CASE									=			mlp_ratios
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			drop_path_rate
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			decoder_hidden_size
        _SCREAMING_SNAKE_CASE									=			max_depth
        _SCREAMING_SNAKE_CASE									=			head_in_index










'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



