


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


snake_case							:       Any     			=      5_00_00
snake_case							:       int     			=      50_00

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






@get_duration
def 							__lowercase   ( __lowerCAmelCase							:				datasets.Dataset					,    __lowerCAmelCase							:				Dict   ):
							for i in range(__lowerCAmelCase   ):
														a__             =     dataset[i]






@get_duration
def 							__lowercase   ( __lowerCAmelCase							:				datasets.Dataset					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any   ):
							for i in range(0					,    len(__lowerCAmelCase   )					,    __lowerCAmelCase   ):
														a__             =     dataset[i : i + batch_size]






@get_duration
def 							__lowercase   ( __lowerCAmelCase							:				datasets.Dataset					,    __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				int   ):
							with dataset.formatted_as(type=__lowerCAmelCase   ):
														for i in range(__lowerCAmelCase   ):
																					a__             =     dataset[i]






@get_duration
def 							__lowercase   ( __lowerCAmelCase							:				datasets.Dataset					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				str   ):
							with dataset.formatted_as(type=__lowerCAmelCase   ):
														for i in range(0					,    __lowerCAmelCase					,    __lowerCAmelCase   ):
																					a__             =     dataset[i : i + batch_size]






def 							__lowercase   ( ):
							a__             =     {'num examples': SPEED_TEST_N_EXAMPLES}
							a__             =     [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0_0}),
							    (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': 1_0}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0_0_0}),
							]

							a__             =     [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_0_0_0}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_0_0_0}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'   )
														a__             =     datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'   )   ), 'numbers': datasets.Value('float32'   )}   )
														a__             =     generate_example_dataset(
														    os.path.join(__lowerCAmelCase					,    'dataset.arrow'   )					,    __lowerCAmelCase					,    num_examples=__lowerCAmelCase					,    seq_shapes={'list': (1_0_0,)}					,    )
														print('first set of iterations'   )
														for func, kwargs in functions:
																					print(func.__name__					,    str(__lowerCAmelCase   )   )
																					a__             =     func(__lowerCAmelCase					,    **__lowerCAmelCase   )

														print('shuffling dataset'   )
														a__             =     dataset.shuffle()
														print('Second set of iterations (after shuffling'   )
														for func, kwargs in functions_shuffled:
																					print('shuffled '					,    func.__name__					,    str(__lowerCAmelCase   )   )
																					a__             =     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()








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


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




