





'''simple docstring'''






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


snake_case_     			=						50_00_00

snake_case_			,	snake_case_     			=						os.path.split(__file__)
snake_case_     			=						os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def 				_lowerCamelCase(	UpperCamelCase__     :			datasets.Dataset		)		->	Dict:
    A							:							str				=    dataset.select(range(0     ,							len(UpperCamelCase__		)     ,							2		)		)
@get_duration
def 				_lowerCamelCase(	UpperCamelCase__     :			datasets.Dataset		)		->	Optional[Any]:
    A							:							Dict				=    dataset.sort('''numbers'''		)
@get_duration
def 				_lowerCamelCase(	UpperCamelCase__     :			datasets.Dataset		)		->	str:
    A							:							int				=    dataset.shuffle()
@get_duration
def 				_lowerCamelCase(	UpperCamelCase__     :			datasets.Dataset		)		->	int:
    A							:							Optional[int]				=    dataset.train_test_split(0.1		)
@get_duration
def 				_lowerCamelCase(	UpperCamelCase__     :			datasets.Dataset     ,							UpperCamelCase__     :			Tuple=10		)		->	List[str]:
    for shard_id in range(UpperCamelCase__		):
        A							:							Optional[Any]				=    dataset.shard(UpperCamelCase__     ,							UpperCamelCase__		)
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Optional[int]				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
    A							:							int				=    (select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''		)
        A							:							str				=    datasets.Features({'''text''': datasets.Value('''string'''		), '''numbers''': datasets.Value('''float32'''		)}		)
        A							:							Tuple				=    generate_example_dataset(
            os.path.join(UpperCamelCase__     ,							'''dataset.arrow'''		)     ,							UpperCamelCase__     ,							num_examples=UpperCamelCase__		)
        print('''Functions'''		)
        for func in functions:
            print(func.__name__		)
            A							:							Optional[int]				=    func(UpperCamelCase__		)

    with open(UpperCamelCase__     ,							'''wb'''		) as f:
        f.write(json.dumps(UpperCamelCase__		).encode('''utf-8'''		)		)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_indices_mapping()







'''simple docstring'''






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


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class 	_lowercase    (				a					):
   _UpperCamelCase									= """mobilevit"""


   def __init__(			self     , _UpperCAmelCase=3     , _UpperCAmelCase=256     , _UpperCAmelCase=2     , _UpperCAmelCase=[144, 192, 240]     , _UpperCAmelCase=[16, 32, 64, 96, 128, 160, 640]     , _UpperCAmelCase=4     , _UpperCAmelCase=2.0     , _UpperCAmelCase=4.0     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.0     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=1E-5     , _UpperCAmelCase=True     , _UpperCAmelCase=256     , _UpperCAmelCase=[6, 12, 18]     , _UpperCAmelCase=0.1     , _UpperCAmelCase=255     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Union[str, Any]				=    num_channels
       A							:							Union[str, Any]				=    image_size
       A							:							List[str]				=    patch_size
       A							:							Any				=    hidden_sizes
       A							:							Dict				=    neck_hidden_sizes
       A							:							int				=    num_attention_heads
       A							:							List[str]				=    mlp_ratio
       A							:							List[Any]				=    expand_ratio
       A							:							Dict				=    hidden_act
       A							:							List[Any]				=    conv_kernel_size
       A							:							List[str]				=    output_stride
       A							:							Dict				=    hidden_dropout_prob
       A							:							List[str]				=    attention_probs_dropout_prob
       A							:							Optional[Any]				=    classifier_dropout_prob
       A							:							Any				=    initializer_range
       A							:							Union[str, Any]				=    layer_norm_eps
       A							:							Union[str, Any]				=    qkv_bias

       # decode head attributes for semantic segmentation
       A							:							int				=    aspp_out_channels
       A							:							Any				=    atrous_rates
       A							:							Tuple				=    aspp_dropout_prob
       A							:							List[Any]				=    semantic_loss_ignore_index







class 	_lowercase    (				a					):
   _UpperCamelCase									= version.parse("""1.11"""					)


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


   @property
   def        snake_case					(			self       ):
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]       )
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]       )


   @property
   def        snake_case					(			self       ):
       return 1E-4
