



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_snake_case	=						5_0000
_snake_case	=						5000

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






@get_duration
def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Dict				     =					dataset[i]






@get_duration
def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for i in range(0       ,				len(_lowerCamelCase						)       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					dataset[i : i + batch_size]






@get_duration
def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							with dataset.formatted_as(type=_lowerCamelCase						):
														for i in range(_lowerCamelCase						):
																					_lowerCAmelCase  :							Union[str, Any]				     =					dataset[i]






@get_duration
def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							with dataset.formatted_as(type=_lowerCamelCase						):
														for i in range(0       ,				_lowerCamelCase       ,				_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					dataset[i : i + batch_size]






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					{"num examples": SPEED_TEST_N_EXAMPLES}
							_lowerCAmelCase  :							Optional[Any]				     =					[
							    (read, {"length": SMALL_TEST}),
							    (read, {"length": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
							    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
							    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
							    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
							    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
							    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
							    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
							]

							_lowerCAmelCase  :							Optional[Any]				     =					[
							    (read, {"length": SMALL_TEST}),
							    (read, {"length": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
							    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
							    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
							    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
							    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("generating dataset"						)
														_lowerCAmelCase  :							Dict				     =					datasets.Features(
														    {"list": datasets.Sequence(datasets.Value("float32"						)						), "numbers": datasets.Value("float32"						)}						)
														_lowerCAmelCase  :							str				     =					generate_example_dataset(
														    os.path.join(_lowerCamelCase       ,				"dataset.arrow"						)       ,				_lowerCamelCase       ,				num_examples=_lowerCamelCase       ,				seq_shapes={"list": (100,)}       ,				)
														print("first set of iterations"						)
														for func, kwargs in functions:
																					print(func.__name__       ,				str(_lowerCamelCase						)						)
																					_lowerCAmelCase  :							List[str]				     =					func(_lowerCamelCase       ,				**_lowerCamelCase						)

														print("shuffling dataset"						)
														_lowerCAmelCase  :							Tuple				     =					dataset.shuffle()
														print("Second set of iterations (after shuffling"						)
														for func, kwargs in functions_shuffled:
																					print("shuffled "       ,				func.__name__       ,				str(_lowerCamelCase						)						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					func(
																					    _lowerCamelCase       ,				**_lowerCamelCase						)

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


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





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
