



"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def 					__lowerCamelCase       (							a_					:		Any							)      ->				Tuple:
      def wrapper(*a_					:		Dict			,       **a_					:		str							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  timeit.default_timer()
            __SCREAMING_SNAKE_CASE       :Tuple		    =  func(*lowercase__			,       **lowercase__							)
            __SCREAMING_SNAKE_CASE       :str		    =  timeit.default_timer() - starttime
            return delta

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  func.__name__

      return wrapper



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		str=1_00			,       a_					:		Optional[int]=None							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Dict		    =  []
      __SCREAMING_SNAKE_CASE       :List[str]		    =  seq_shapes or {}
      for i in range(lowercase__							):
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {}
            for col_id, (k, v) in enumerate(features.items()							):
                  if isinstance(lowercase__			,       _ArrayXD							):
                        __SCREAMING_SNAKE_CASE       :List[str]		    =  np.random.rand(*v.shape							).astype(v.dtype							)
                  elif isinstance(lowercase__			,       datasets.Value							):
                        if v.dtype == "string":
                              __SCREAMING_SNAKE_CASE       :Any		    =  'The small grey turtle was surprisingly fast when challenged.'
                        else:
                              __SCREAMING_SNAKE_CASE       :List[str]		    =  np.random.randint(10			,       size=1							).astype(v.dtype							).item()
                  elif isinstance(lowercase__			,       datasets.Sequence							):
                        while isinstance(lowercase__			,       datasets.Sequence							):
                              __SCREAMING_SNAKE_CASE       :Tuple		    =  v.feature
                        __SCREAMING_SNAKE_CASE       :List[Any]		    =  seq_shapes[k]
                        __SCREAMING_SNAKE_CASE       :int		    =  np.random.rand(*lowercase__							).astype(v.dtype							)
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  data

            dummy_data.append((i, example)							)

      return dummy_data







def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Tuple			,       a_					:		Any=1_00			,       a_					:		Dict=None							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  generate_examples(lowercase__			,       num_examples=lowercase__			,       seq_shapes=lowercase__							)

      with ArrowWriter(features=lowercase__			,       path=lowercase__							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE       :str		    =  features.encode_example(lowercase__							)
                  writer.write(lowercase__							)

            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  writer.finalize()

      if not num_final_examples == num_examples:
            raise ValueError(
                f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''							)

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  datasets.Dataset.from_file(filename=lowercase__			,       info=datasets.DatasetInfo(features=lowercase__							)							)

      return dataset






"""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


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "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  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  '''levit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=[1_28, 2_56, 3_84]		,SCREAMING_SNAKE_CASE__=[4, 8, 12]		,SCREAMING_SNAKE_CASE__=[4, 4, 4]		,SCREAMING_SNAKE_CASE__=[16, 16, 16]		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=[2, 2, 2]		,SCREAMING_SNAKE_CASE__=[2, 2, 2]		,SCREAMING_SNAKE_CASE__=0.0_2		,**SCREAMING_SNAKE_CASE__		,)      ->				Tuple:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  image_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  num_channels
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kernel_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  stride
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  padding
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_sizes
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  depths
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  key_dim
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  drop_path_rate
             __SCREAMING_SNAKE_CASE       :Tuple		    =  patch_size
             __SCREAMING_SNAKE_CASE       :int		    =  attention_ratio
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  mlp_ratio
             __SCREAMING_SNAKE_CASE       :str		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [
                 ['''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  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  version.parse('''1.11'''  )




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






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




       @property
       def    _UpperCamelCase					(						self							)      ->				float:





             """simple docstring"""






             return 1E-4