






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


A__			:    List[str]         =				logging.get_logger(__name__)

A__			:    Optional[Any]         =				{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= """yolos"""




				def __init__(  self		:					Any,						lowerCamelCase		:					Tuple=768,						lowerCamelCase		:					Optional[Any]=12,						lowerCamelCase		:					Any=12,						lowerCamelCase		:					Any=3_072,						lowerCamelCase		:					Optional[Any]="gelu",						lowerCamelCase		:					List[Any]=0.0,						lowerCamelCase		:					List[str]=0.0,						lowerCamelCase		:					List[Any]=0.02,						lowerCamelCase		:					str=1E-12,						lowerCamelCase		:					List[Any]=[512, 864],						lowerCamelCase		:					Any=16,						lowerCamelCase		:					List[str]=3,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Optional[int]=100,						lowerCamelCase		:					Optional[int]=True,						lowerCamelCase		:					Optional[int]=False,						lowerCamelCase		:					Optional[Any]=1,						lowerCamelCase		:					List[str]=5,						lowerCamelCase		:					List[Any]=2,						lowerCamelCase		:					List[Any]=5,						lowerCamelCase		:					Any=2,						lowerCamelCase		:					Dict=0.1,						**lowerCamelCase		:					int,						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)

								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    intermediate_size
								lowercase__     				=    hidden_act
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    initializer_range
								lowercase__     				=    layer_norm_eps
								lowercase__     				=    image_size
								lowercase__     				=    patch_size
								lowercase__     				=    num_channels
								lowercase__     				=    qkv_bias
								lowercase__     				=    num_detection_tokens
								lowercase__     				=    use_mid_position_embeddings
								lowercase__     				=    auxiliary_loss
								# Hungarian matcher
								lowercase__     				=    class_cost
								lowercase__     				=    bbox_cost
								lowercase__     				=    giou_cost
								# Loss coefficients
								lowercase__     				=    bbox_loss_coefficient
								lowercase__     				=    giou_loss_coefficient
								lowercase__     				=    eos_coefficient



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= version.parse("""1.11"""			)




				@property
				def        lowercase__     (  self		:					List[Any]						):

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




				@property
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return 1E-4




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return 12












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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




