






"""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_							      = {
    '''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
    # See all ViT models at https://huggingface.co/models?filter=vit
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vit'''





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Any=768      ,      lowerCAmelCase_   :					str=12      ,      lowerCAmelCase_   :					Optional[int]=12      ,      lowerCAmelCase_   :					List[str]=3_072      ,      lowerCAmelCase_   :					Optional[int]="gelu"      ,      lowerCAmelCase_   :					List[str]=0.0      ,      lowerCAmelCase_   :					Dict=0.0      ,      lowerCAmelCase_   :					Optional[int]=0.0_2      ,      lowerCAmelCase_   :					Optional[Any]=1e-12      ,      lowerCAmelCase_   :					Any=224      ,      lowerCAmelCase_   :					str=16      ,      lowerCAmelCase_   :					Dict=3      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					List[str]=16      ,      **lowerCAmelCase_   :					Union[str, Any]      ,      )		->     Dict:
				super().__init__(**lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   hidden_size
				UpperCAmelCase_	:					str     	=   num_hidden_layers
				UpperCAmelCase_	:					Optional[Any]     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					Optional[int]     	=   hidden_act
				UpperCAmelCase_	:					str     	=   hidden_dropout_prob
				UpperCAmelCase_	:					str     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Dict     	=   initializer_range
				UpperCAmelCase_	:					Tuple     	=   layer_norm_eps
				UpperCAmelCase_	:					Tuple     	=   image_size
				UpperCAmelCase_	:					Union[str, Any]     	=   patch_size
				UpperCAmelCase_	:					Tuple     	=   num_channels
				UpperCAmelCase_	:					Optional[int]     	=   qkv_bias
				UpperCAmelCase_	:					Tuple     	=   encoder_stride







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     float:
				return 1e-4











"""simple docstring"""
import timeit

import numpy as np

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

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

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

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   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}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



