






from typing import List

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


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

__UpperCAmelCase							:   Dict							=  {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}


class 					_snake_case						(			_A ):
	_A          =   'efficientformer'






	def __init__(		self					,UpperCamelCase = [3, 2, 6, 4]					,UpperCamelCase = [48, 96, 224, 448]					,UpperCamelCase = [True, True, True, True]					,UpperCamelCase = 448					,UpperCamelCase = 32					,UpperCamelCase = 4					,UpperCamelCase = 7					,UpperCamelCase = 5					,UpperCamelCase = 8					,UpperCamelCase = 4					,UpperCamelCase = 0.0					,UpperCamelCase = 16					,UpperCamelCase = 3					,UpperCamelCase = 3					,UpperCamelCase = 3					,UpperCamelCase = 2					,UpperCamelCase = 1					,UpperCamelCase = 0.0					,UpperCamelCase = 1					,UpperCamelCase = True					,UpperCamelCase = True					,UpperCamelCase = 1E-5					,UpperCamelCase = "gelu"					,UpperCamelCase = 0.02					,UpperCamelCase = 1E-12					,UpperCamelCase = 224					,UpperCamelCase = 1E-05					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )

					snake_case__      :Tuple         =   hidden_act
					snake_case__      :Any         =   hidden_dropout_prob
					snake_case__      :Any         =   hidden_sizes
					snake_case__      :Union[str, Any]         =   num_hidden_layers
					snake_case__      :List[Any]         =   num_attention_heads
					snake_case__      :Tuple         =   initializer_range
					snake_case__      :Tuple         =   layer_norm_eps
					snake_case__      :List[Any]         =   patch_size
					snake_case__      :Optional[Any]         =   num_channels
					snake_case__      :Dict         =   depths
					snake_case__      :Union[str, Any]         =   mlp_expansion_ratio
					snake_case__      :Optional[int]         =   downsamples
					snake_case__      :Union[str, Any]         =   dim
					snake_case__      :Optional[Any]         =   key_dim
					snake_case__      :Dict         =   attention_ratio
					snake_case__      :List[str]         =   resolution
					snake_case__      :List[str]         =   pool_size
					snake_case__      :Optional[Any]         =   downsample_patch_size
					snake_case__      :Dict         =   downsample_stride
					snake_case__      :Dict         =   downsample_pad
					snake_case__      :Optional[int]         =   drop_path_rate
					snake_case__      :str         =   num_metaad_blocks
					snake_case__      :int         =   distillation
					snake_case__      :Tuple         =   use_layer_scale
					snake_case__      :List[Any]         =   layer_scale_init_value
					snake_case__      :Union[str, Any]         =   image_size
					snake_case__      :Union[str, Any]         =   batch_norm_eps









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )