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

_UpperCAmelCase   :					Union[str, Any]    =			{
    "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   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			int      	=							"mobilevit"



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

				UpperCamelCase            =	num_channels
				UpperCamelCase            =	image_size
				UpperCamelCase            =	patch_size
				UpperCamelCase            =	hidden_sizes
				UpperCamelCase            =	neck_hidden_sizes
				UpperCamelCase            =	num_attention_heads
				UpperCamelCase            =	mlp_ratio
				UpperCamelCase            =	expand_ratio
				UpperCamelCase            =	hidden_act
				UpperCamelCase            =	conv_kernel_size
				UpperCamelCase            =	output_stride
				UpperCamelCase            =	hidden_dropout_prob
				UpperCamelCase            =	attention_probs_dropout_prob
				UpperCamelCase            =	classifier_dropout_prob
				UpperCamelCase            =	initializer_range
				UpperCamelCase            =	layer_norm_eps
				UpperCamelCase            =	qkv_bias

				# decode head attributes for semantic segmentation
				UpperCamelCase            =	aspp_out_channels
				UpperCamelCase            =	atrous_rates
				UpperCamelCase            =	aspp_dropout_prob
				UpperCamelCase            =	semantic_loss_ignore_index






class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Dict      	=							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			)    ->   Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "image-classification":
						return OrderedDict([('logits', {0: 'batch'})]			)
				else:
						return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]			)



		@property
		def __UpperCamelCase					(					self			)    ->   float:
				"""simple docstring"""
				return 1e-4


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

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


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
