

from unittest import TestCase

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







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											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 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											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 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




