




'''simple docstring'''



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase_							:	List[Any]	     =		5_0_0_0_0
UpperCAmelCase_							:	Optional[Any]	     =		5_0_0_0

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







@get_duration
def       _UpperCamelCase			(_lowerCamelCase				:  datasets.Dataset ,						_lowerCamelCase				:  Optional[int]						)-> Optional[int]:




     '''simple docstring'''





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







@get_duration
def       _UpperCamelCase			(_lowerCamelCase				:  datasets.Dataset ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[str]						)-> str:




     '''simple docstring'''





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







@get_duration
def       _UpperCamelCase			(_lowerCamelCase				:  datasets.Dataset ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  Dict						)-> str:




     '''simple docstring'''





     with dataset.formatted_as(type=_lowerCamelCase						):
          for i in range(_lowerCamelCase						):
               __snake_case            =	dataset[i]







@get_duration
def       _UpperCamelCase			(_lowerCamelCase				:  datasets.Dataset ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Tuple						)-> str:




     '''simple docstring'''





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







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	{'''num examples''': SPEED_TEST_N_EXAMPLES}
     __snake_case            =	[
         (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''': 1_00}),
         (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
         (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''': 10_00}),
     ]

     __snake_case            =	[
         (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''': 1_00}),
         (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
         (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''': 10_00}),
     ]
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('''generating dataset'''						)
          __snake_case            =	datasets.Features(
              {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
          __snake_case            =	generate_example_dataset(
              os.path.join(_lowerCamelCase ,						'''dataset.arrow'''						) ,						_lowerCamelCase ,						num_examples=_lowerCamelCase ,						seq_shapes={'''list''': (1_00,)} ,						)
          print('''first set of iterations'''						)
          for func, kwargs in functions:
               print(func.__name__ ,						str(_lowerCamelCase						)						)
               __snake_case            =	func(_lowerCamelCase ,						**_lowerCamelCase						)

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












'''simple docstring'''



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


UpperCAmelCase_							:	int	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Dict	     =		{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase):
       __lowercase	:   List[Any]										=      '''swin'''

       __lowercase	:   str										=      {
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=224     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=96     ,	__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]     ,	__SCREAMING_SNAKE_CASE=[3, 6, 12, 24]     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=4.0     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	image_size
            __snake_case            =	patch_size
            __snake_case            =	num_channels
            __snake_case            =	embed_dim
            __snake_case            =	depths
            __snake_case            =	len(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	num_heads
            __snake_case            =	window_size
            __snake_case            =	mlp_ratio
            __snake_case            =	qkv_bias
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	drop_path_rate
            __snake_case            =	hidden_act
            __snake_case            =	use_absolute_embeddings
            __snake_case            =	layer_norm_eps
            __snake_case            =	initializer_range
            __snake_case            =	encoder_stride
            # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __snake_case            =	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE	) - 1)	)
            __snake_case            =	['''stem'''] + [F'''stage{idx}''' for idx in range(1     ,	len(__SCREAMING_SNAKE_CASE	) + 1	)]
            __snake_case       ,     __snake_case            =	get_aligned_output_features_output_indices(
                out_features=__SCREAMING_SNAKE_CASE     ,	out_indices=__SCREAMING_SNAKE_CASE     ,	stage_names=self.stage_names	)





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[int]										=      version.parse('''1.11''')


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



            '''simple docstring'''




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




       @property
       def    lowerCAmelCase   ( self	)   ->	float:



            '''simple docstring'''




            return 1E-4






