


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      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A=7						,    __A=3						,    __A=18						,    __A=30						,    __A=400						,    __A=True						,    __A=None						,    __A=True						,    __A=None						,    ):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    size if size is not None else {"shortest_edge": 20}
     lowerCamelCase			: Optional[Any]   =    crop_size if crop_size is not None else {"height": 18, "width": 18}
     lowerCamelCase			: Optional[int]   =    parent
     lowerCamelCase			: Tuple   =    batch_size
     lowerCamelCase			: int   =    num_channels
     lowerCamelCase			: Optional[Any]   =    image_size
     lowerCamelCase			: Optional[Any]   =    min_resolution
     lowerCamelCase			: Tuple   =    max_resolution
     lowerCamelCase			: List[str]   =    do_resize
     lowerCamelCase			: Optional[int]   =    size
     lowerCamelCase			: Optional[Any]   =    do_center_crop
     lowerCamelCase			: Tuple   =    crop_size






   def _snake_case   (		self			):






     """simple docstring"""





     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      UpperCAmelCase_ ( UpperCamelCase		,			unittest.TestCase   ):

   '''simple docstring'''




   __A			:				Optional[Any]							=		MobileNetVaImageProcessor if is_vision_available() else None






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    MobileNetVaImageProcessingTester(self			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     return self.image_processor_tester.prepare_image_processor_dict()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.image_processing_class(**self.image_processor_dict			)
     self.assertTrue(hasattr(__A						,    "do_resize"			)			)
     self.assertTrue(hasattr(__A						,    "size"			)			)
     self.assertTrue(hasattr(__A						,    "do_center_crop"			)			)
     self.assertTrue(hasattr(__A						,    "center_crop"			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    self.image_processing_class.from_dict(self.image_processor_dict			)
     self.assertEqual(image_processor.size						,    {"shortest_edge": 20}			)
     self.assertEqual(image_processor.crop_size						,    {"height": 18, "width": 18}			)

     lowerCamelCase			: Optional[int]   =    self.image_processing_class.from_dict(self.image_processor_dict						,    size=42						,    crop_size=84			)
     self.assertEqual(image_processor.size						,    {"shortest_edge": 42}			)
     self.assertEqual(image_processor.crop_size						,    {"height": 84, "width": 84}			)






   def _snake_case   (		self			):






     """simple docstring"""





     pass






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.image_processing_class(**self.image_processor_dict			)
     # create random PIL images
     lowerCamelCase			: Union[str, Any]   =    prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__A			)
     for image in image_inputs:
       self.assertIsInstance(__A						,    Image.Image			)

     # Test not batched input
     lowerCamelCase			: str   =    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			: Union[str, Any]   =    image_processing(__A						,    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Any   =    self.image_processing_class(**self.image_processor_dict			)
     # create random numpy tensors
     lowerCamelCase			: Optional[int]   =    prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__A						,    numpify=__A			)
     for image in image_inputs:
       self.assertIsInstance(__A						,    np.ndarray			)

     # Test not batched input
     lowerCamelCase			: int   =    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			: Optional[Any]   =    image_processing(__A						,    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    self.image_processing_class(**self.image_processor_dict			)
     # create random PyTorch tensors
     lowerCamelCase			: int   =    prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__A						,    torchify=__A			)
     for image in image_inputs:
       self.assertIsInstance(__A						,    torch.Tensor			)

     # Test not batched input
     lowerCamelCase			: int   =    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(__A						,    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 importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: str   =    ArgumentParser(
      description=(
          "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
      )     )

  # Optional arguments for the launch helper
  parser.add_argument("--num_cores"				,						type=SCREAMING_SNAKE_CASE_				,						default=1				,						help="Number of TPU cores to use (1 or 8)."     )

  # positional
  parser.add_argument(
      "training_script"				,						type=SCREAMING_SNAKE_CASE_				,						help=(
          "The full path to the single TPU training "
          "program/script to be launched in parallel, "
          "followed by all the arguments for the "
          "training script"
      )				,						)

  # rest from the training program
  parser.add_argument("training_script_args"				,						nargs=SCREAMING_SNAKE_CASE_     )

  return parser.parse_args()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[Any]   =    parse_args()

  # Import training_script as a module.
  lowerCamelCase			: Tuple   =    Path(args.training_script     )
  sys.path.append(str(script_fpath.parent.resolve()     )     )
  lowerCamelCase			: int   =    script_fpath.stem
  lowerCamelCase			: Tuple   =    importlib.import_module(SCREAMING_SNAKE_CASE_     )

  # Patch sys.argv
  lowerCamelCase			: Union[str, Any]   =    [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores     )]

  xmp.spawn(mod._mp_fn				,						args=()				,						nprocs=args.num_cores     )


if __name__ == "__main__":
   main()



