

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor






class 							A_			(		unittest.TestCase ):


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any]=7      ,  UpperCAmelCase      :		List[Any]=3      ,  UpperCAmelCase      :		int=1_8      ,  UpperCAmelCase      :		Union[str, Any]=3_0      ,  UpperCAmelCase      :		Optional[int]=4_0_0      ,  UpperCAmelCase      :		str=True      ,  UpperCAmelCase      :		str=None      ,  UpperCAmelCase      :		List[str]=True      ,  UpperCAmelCase      :		List[Any]=None      ,  )							->     str:
      __lowerCAmelCase:       str            =				size if size is not None else {'shortest_edge': 2_0}
      __lowerCAmelCase:       str            =				crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      __lowerCAmelCase:       Dict            =				parent
      __lowerCAmelCase:       Optional[int]            =				batch_size
      __lowerCAmelCase:       List[str]            =				num_channels
      __lowerCAmelCase:       str            =				image_size
      __lowerCAmelCase:       List[str]            =				min_resolution
      __lowerCAmelCase:       Dict            =				max_resolution
      __lowerCAmelCase:       Dict            =				do_resize
      __lowerCAmelCase:       Dict            =				size
      __lowerCAmelCase:       List[Any]            =				do_center_crop
      __lowerCAmelCase:       int            =				crop_size


     def   UpperCAmelCase  (							self      :		List[str] )							->     Any:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }







@require_torch
@require_vision
class 							A_			(		snake_case__							,     unittest.TestCase ):
     _lowercase      :					Union[str, Any]       =     MobileNetVaImageProcessor if is_vision_available() else None


     def   UpperCAmelCase  (							self      :		str )							->     int:
      __lowerCAmelCase:       int            =				MobileNetVaImageProcessingTester(self )


     @property
     def   UpperCAmelCase  (							self      :		str )							->     str:
      return self.image_processor_tester.prepare_image_processor_dict()


     def   UpperCAmelCase  (							self      :		int )							->     str:
      __lowerCAmelCase:       List[str]            =				self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'do_resize' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'size' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'do_center_crop' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'center_crop' ) )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     str:
      __lowerCAmelCase:       Optional[Any]            =				self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size      ,  {'shortest_edge': 2_0} )
      self.assertEqual(image_processor.crop_size      ,  {'height': 1_8, 'width': 1_8} )

      __lowerCAmelCase:       Dict            =				self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4 )
      self.assertEqual(image_processor.size      ,  {'shortest_edge': 4_2} )
      self.assertEqual(image_processor.crop_size      ,  {'height': 8_4, 'width': 8_4} )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     List[str]:
      pass


     def   UpperCAmelCase  (							self      :		str )							->     int:
      # Initialize image_processing
      __lowerCAmelCase:       Optional[Any]            =				self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      __lowerCAmelCase:       Optional[int]            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  Image.Image )

      # Test not batched input
      __lowerCAmelCase:       Union[str, Any]            =				image_processing(image_inputs[0]      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       int            =				image_processing(UpperCAmelCase      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )


     def   UpperCAmelCase  (							self      :		int )							->     Optional[int]:
      # Initialize image_processing
      __lowerCAmelCase:       Optional[Any]            =				self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      __lowerCAmelCase:       List[str]            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase      ,  numpify=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  np.ndarray )

      # Test not batched input
      __lowerCAmelCase:       Optional[Any]            =				image_processing(image_inputs[0]      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       Any            =				image_processing(UpperCAmelCase      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Union[str, Any]:
      # Initialize image_processing
      __lowerCAmelCase:       Tuple            =				self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      __lowerCAmelCase:       List[Any]            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase      ,  torchify=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  torch.Tensor )

      # Test not batched input
      __lowerCAmelCase:       Any            =				image_processing(image_inputs[0]      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       List[Any]            =				image_processing(UpperCAmelCase      ,  return_tensors='pt' ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )








import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_a			=       1_0_0_0_0_0_0_0_0_0_0_0
_a			=       1_0_0_0_0

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







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   datasets.Dataset:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				pa.Table.from_pydict({'col': [0] * chunk_size}		)
 __lowerCAmelCase:       Dict            =				pa.concat_tables([table] * (num_examples // chunk_size)		)
 return datasets.Dataset(SCREAMING_SNAKE_CASE   , fingerprint='table_100B'		)






@dataclass
class 							A_			:
     _lowercase      :					int
     _lowercase      :					int
     _lowercase      :					int
     _lowercase      :					int


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Dict:
      __lowerCAmelCase:       Dict            =				np.random.default_rng(self.seed )
      __lowerCAmelCase:       List[Any]            =				rng.integers(low=self.low      ,  high=self.high      ,  size=self.size ).tolist()


     def __iter__(							self      :		Any )							->     Optional[int]:
      return iter(self._sampled_values )


     def __len__(							self      :		Union[str, Any] )							->     Dict:
      return self.size







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				dataset[0]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Dict            =				dataset[-1]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				dataset[range(len(SCREAMING_SNAKE_CASE		) // 2   , len(SCREAMING_SNAKE_CASE		) // 2 + 10_24		)]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:       int            =				dataset[RandIter(0   , len(SCREAMING_SNAKE_CASE		)   , 10_24   , seed=42		)]







def _a			(	)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       str            =				{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCAmelCase:       Tuple            =				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
 print('generating dataset'		)
 __lowerCAmelCase:       str            =				generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE   , chunk_size=SCREAMING_SNAKE_CASE		)
 print('Functions'		)
 for func in functions:
  print(func.__name__		)
  __lowerCAmelCase:       Dict            =				func(SCREAMING_SNAKE_CASE		)

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


if __name__ == "__main__":  # useful to run the profiler
  benchmark_table_100B()





