

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       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def __init__(      self						,   a						,   a=7						,   a=3						,   a=18						,   a=30						,   a=400						,   a=True						,   a=None						,   a=True						,   a=None						,   ):
         lowercase__    :   Union[str, Any]      =       size if size is not None else {'shortest_edge': 20}
         lowercase__    :   int      =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
         lowercase__    :   Optional[int]      =       parent
         lowercase__    :   Union[str, Any]      =       batch_size
         lowercase__    :   int      =       num_channels
         lowercase__    :   Union[str, Any]      =       image_size
         lowercase__    :   Any      =       min_resolution
         lowercase__    :   Dict      =       max_resolution
         lowercase__    :   Optional[Any]      =       do_resize
         lowercase__    :   Optional[Any]      =       size
         lowercase__    :   List[Any]      =       do_center_crop
         lowercase__    :   Dict      =       crop_size





    def       snake_case_				(      self):
         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       SCREAMING_SNAKE_CASE__					(__snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       str														=     MobileNetVaImageProcessor if is_vision_available() else None





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       MobileNetVaImageProcessingTester(self)





    @property
    def       snake_case_				(      self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def       snake_case_				(      self):
         lowercase__    :   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						,   'crop_size'))





    def       snake_case_				(      self):
         lowercase__    :   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})

         lowercase__    :   Tuple      =       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):
         pass





    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   int      =       self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__    :   Optional[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
         lowercase__    :   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
         lowercase__    :   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'],
             )						,   )





    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   Any      =       self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__    :   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
         lowercase__    :   List[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
         lowercase__    :   Optional[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'],
             )						,   )





    def       snake_case_				(      self):
         # Initialize image_processing
         lowercase__    :   int      =       self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__    :   List[Any]      =       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
         lowercase__    :   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
         lowercase__    :   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'],
             )						,   )




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

