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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class  _UpperCAmelCase       (			A_,       A_   ):



 '''simple docstring'''




 __A           = "convnext"







 def __init__(						self							:					Optional[int]				,      lowercase_							:					Optional[int]=3				,      lowercase_							:					Optional[Any]=4				,      lowercase_							:					Any=4				,      lowercase_							:					Optional[Any]=None				,      lowercase_							:					int=None				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[str]=0.02				,      lowercase_							:					Optional[Any]=1e-1_2				,      lowercase_							:					Optional[Any]=1e-6				,      lowercase_							:					List[str]=0.0				,      lowercase_							:					List[str]=224				,      lowercase_							:					Optional[Any]=None				,      lowercase_							:					str=None				,      **lowercase_							:					List[str]				,      )		->     int:


   """simple docstring"""







   super().__init__(**_A)

   _UpperCamelCase				=  num_channels
   _UpperCamelCase				=  patch_size
   _UpperCamelCase				=  num_stages
   _UpperCamelCase				=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
   _UpperCamelCase				=  [3, 3, 9, 3] if depths is None else depths
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  layer_scale_init_value
   _UpperCamelCase				=  drop_path_rate
   _UpperCamelCase				=  image_size
   _UpperCamelCase				=  ["stem"] + [f'stage{idx}' for idx in range(1				,      len(self.depths) + 1)]
   _UpperCamelCase	,		_UpperCamelCase				=  get_aligned_output_features_output_indices(
       out_features=_A				,      out_indices=_A				,      stage_names=self.stage_names)




class  _UpperCAmelCase       (			A_   ):



 '''simple docstring'''




 __A           = version.parse('''1.11'''   )







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


   """simple docstring"""







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







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     float:


   """simple docstring"""







   return 1e-5






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

_A					,  _A  					= os.path.split(__file__)
_A  					= os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

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


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

