

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: float
      _lowerCamelCase					: Optional[float]					=					None
      _lowerCamelCase					: Optional[float]					=					None




class      _a				( unittest.TestCase							):




      """simple docstring"""

      def 		__A  (      self       :							int  ):
            A_	     =							ModelOutputTest(a=30  )
            self.assertEqual(x.a	, 30  )
            self.assertIsNone(x.b  )
            self.assertIsNone(x.c  )
            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x.d





      def 		__A  (      self       :							Dict  ):
            A_	     =							ModelOutputTest(a=30	, b=10  )
            self.assertEqual(x[0]	, 30  )
            self.assertEqual(x[1]	, 10  )
            self.assertEqual(x[:2]	, (30, 10)  )
            self.assertEqual(x[:]	, (30, 10)  )

            A_	     =							ModelOutputTest(a=30	, c=10  )
            self.assertEqual(x[0]	, 30  )
            self.assertEqual(x[1]	, 10  )
            self.assertEqual(x[:2]	, (30, 10)  )
            self.assertEqual(x[:]	, (30, 10)  )





      def 		__A  (      self       :							Dict  ):
            A_	     =							ModelOutputTest(a=30	, b=10  )
            self.assertEqual(x["a"]	, 30  )
            self.assertEqual(x["b"]	, 10  )
            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x["c"]

            A_	     =							ModelOutputTest(a=30	, c=10  )
            self.assertEqual(x["a"]	, 30  )
            self.assertEqual(x["c"]	, 10  )
            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x["b"]





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							ModelOutputTest(a=30  )
            self.assertEqual(list(x.keys()  )	, ["a"]  )
            self.assertEqual(list(x.values()  )	, [30]  )
            self.assertEqual(list(x.items()  )	, [("a", 30)]  )
            self.assertEqual(list(UpperCAmelCase  )	, ["a"]  )

            A_	     =							ModelOutputTest(a=30	, b=10  )
            self.assertEqual(list(x.keys()  )	, ["a", "b"]  )
            self.assertEqual(list(x.values()  )	, [30, 10]  )
            self.assertEqual(list(x.items()  )	, [("a", 30), ("b", 10)]  )
            self.assertEqual(list(UpperCAmelCase  )	, ["a", "b"]  )

            A_	     =							ModelOutputTest(a=30	, c=10  )
            self.assertEqual(list(x.keys()  )	, ["a", "c"]  )
            self.assertEqual(list(x.values()  )	, [30, 10]  )
            self.assertEqual(list(x.items()  )	, [("a", 30), ("c", 10)]  )
            self.assertEqual(list(UpperCAmelCase  )	, ["a", "c"]  )

            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x.update({"d": 20}  )
            with self.assertRaises(UpperCAmelCase  ):
                  del x["a"]
            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x.pop("a"  )
            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							x.setdefault("d"	, 32  )





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							ModelOutputTest(a=30  )
            A_	     =							10
            self.assertEqual(x.a	, 10  )
            self.assertEqual(x["a"]	, 10  )





      def 		__A  (      self       :							Tuple  ):
            A_	     =							ModelOutputTest(a=30  )
            A_	     =							10
            self.assertEqual(x.a	, 10  )
            self.assertEqual(x["a"]	, 10  )





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							ModelOutputTest({"a": 30, "b": 10}  )
            self.assertEqual(list(x.keys()  )	, ["a", "b"]  )
            self.assertEqual(x.a	, 30  )
            self.assertEqual(x.b	, 10  )







      def 		__A  (      self       :							Tuple  ):
            A_	     =							ModelOutputTest([("a", 30), ("b", 10)]  )
            self.assertEqual(list(x.keys()  )	, ["a", "b"]  )
            self.assertEqual(x.a	, 30  )
            self.assertEqual(x.b	, 10  )

            with self.assertRaises(UpperCAmelCase  ):
                  A_	     =							ModelOutputTest([("a", 30), (10, 10)]  )

            A_	     =							ModelOutputTest(a=(30, 30)  )
            self.assertEqual(list(x.keys()  )	, ["a"]  )
            self.assertEqual(x.a	, (30, 30)  )


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


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

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

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          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:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      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])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      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])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

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


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