


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


_SCREAMING_SNAKE_CASE :					List[Any]			=	(30, 487)
_SCREAMING_SNAKE_CASE :					Any			=	(36, 1024)
_SCREAMING_SNAKE_CASE :					Tuple			=	(100, 100)
_SCREAMING_SNAKE_CASE :					Union[str, Any]			=	100

_SCREAMING_SNAKE_CASE :					str			=	datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE :					List[Any]			=	os.path.split(__file__)
_SCREAMING_SNAKE_CASE :					Any			=	os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					Tuple		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[Any]			)  ->   List[str]:





     '''simple docstring'''





     with ArrowWriter(features=__lowerCamelCase		,path=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)			) as writer:
          for key, record in dummy_data:
               UpperCamelCase__							:  Optional[int]								=     my_features.encode_example(__lowerCamelCase			)
               writer.write(__lowerCamelCase			)
          UpperCamelCase__	,UpperCamelCase__							:  Tuple								=     writer.finalize()







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Union[str, Any]			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[Any]								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     for _ in dataset:
          pass







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					str			)  ->   int:





     '''simple docstring'''





     UpperCamelCase__							:  List[str]								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     dataset.set_format('''numpy'''			)
     for _ in dataset:
          pass







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					Union[str, Any]			)  ->   str:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     10
     UpperCamelCase__							:  Union[str, Any]								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     for i in range(0		,len(__lowerCamelCase			)		,__lowerCamelCase			):
          UpperCamelCase__							:  Dict								=     dataset[i : i + batch_size]







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					List[Any]			)  ->   Dict:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     10
     UpperCamelCase__							:  Any								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     dataset.set_format('''numpy'''			)
     for i in range(0		,len(__lowerCamelCase			)		,__lowerCamelCase			):
          UpperCamelCase__							:  Tuple								=     dataset[i : i + batch_size]







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					str			)  ->   List[str]:





     '''simple docstring'''





     UpperCamelCase__							:  List[str]								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     for col in feats:
          UpperCamelCase__							:  Any								=     dataset[col]







@get_duration
def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					str			)  ->   Dict:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     datasets.Dataset.from_file(
         filename=os.path.join(__lowerCamelCase		,'''beta.arrow'''			)		,info=datasets.DatasetInfo(features=__lowerCamelCase			)			)
     dataset.set_format('''numpy'''			)
     for col in feats:
          UpperCamelCase__							:  int								=     dataset[col]







def 						_lowercase       (     )  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  List[Any]								=     {}
     UpperCamelCase__							:  Optional[Any]								=     (
         read_unformated,
         read_formatted_as_numpy,
         read_batch_unformated,
         read_batch_formatted_as_numpy,
         read_col_unformated,
         read_col_formatted_as_numpy,
     )
     with tempfile.TemporaryDirectory() as tmp_dir:
          UpperCamelCase__							:  List[Any]								=     datasets.Features({'''image''': ArrayaD(__lowerCamelCase		,dtype='''float32'''			)}			)
          UpperCamelCase__							:  List[str]								=     generate_examples(features=__lowerCamelCase		,num_examples=__lowerCamelCase			)
          UpperCamelCase__							:  Optional[int]								=     write(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
          for read_func in read_functions:
               UpperCamelCase__							:  Tuple								=     read_func(__lowerCamelCase		,__lowerCamelCase			)

     with tempfile.TemporaryDirectory() as tmp_dir:
          # don't use fixed length for fair comparison
          # feats = datasets.Features(
          #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
          # )
          UpperCamelCase__							:  List[Any]								=     datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''			)			)			)}			)
          UpperCamelCase__							:  Union[str, Any]								=     generate_examples(
              features=__lowerCamelCase		,num_examples=__lowerCamelCase		,seq_shapes={'''image''': SPEED_TEST_SHAPE}			)
          UpperCamelCase__							:  List[Any]								=     write(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
          for read_func in read_functions:
               UpperCamelCase__							:  Dict								=     read_func(__lowerCamelCase		,__lowerCamelCase			)

     with tempfile.TemporaryDirectory() as tmp_dir:
          # don't use fixed length for fair comparison
          # feats = datasets.Features(
          #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
          # )
          UpperCamelCase__							:  Dict								=     datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''			)			)}			)
          UpperCamelCase__							:  Union[str, Any]								=     generate_examples(
              features=__lowerCamelCase		,num_examples=__lowerCamelCase		,seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,)
          UpperCamelCase__							:  Any								=     write(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
          for read_func in read_functions:
               UpperCamelCase__							:  Any								=     read_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_array_xd()








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


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

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




